Attorney Docket #: 250298.000961 OFF-TARGET PEPTIDE-MHC COMPLEX CONFORMATION MODELING SYSTEMS AND METHODS FOR ANTIGEN-RECOGNITION MOLECULE DEVELOPMENT CROSS-REFERENCE TO RELATED APPLICATIONS This application claims priority, and benefit under 35 U.S.C. § 119(e), to U.S. Provisional Application No. 63/665,588, filed on June 28, 2024, and entitled “TARGET PEPTIDE-MHC COMPLEX CONFORMATION MODELING SYSTEMS AND METHODS FOR ANTIGEN- RECOGNITION MOLECULE DEVELOPMENT,” the entirety of which is incorporated herein by reference. SEQUENCE LISTING The instant application contains a Sequence Listing which has been submitted electronically in XML format and is hereby incorporated by reference in its entirety. Said XML copy, created on June 20, 2025, is named 250298_000961_SL.xml and is 413,733 bytes in size. FIELD This application relates generally to computational systems and methods for providing 3D computational models of a peptide presented in a complex with a major histocompatibility complex (MHC) molecule (MHC-peptide complex) and comparing such models for the purpose of predicting off-target toxicity associated with potential target MHC-peptide complex(s). BACKGROUND Antigen-recognition molecules, such as T-cell receptors (TCRs) and antibodies, are capable of identifying antigens, which include agents recognized by the immune system of a host as defined herein. Antigen-recognition molecules can help the immune system neutralize an antigen by binding to an antigenic peptide presented in an MHC-peptide complex on the surface of an antigen-presenting cell. The MHC-peptide complex is presented on the surface of the antigen-presenting cell as a result of a cellular process in which an MHC gene in the antigen cell encodes an MHC molecule; the MHC molecule subsequently binds to the antigenic peptide thereby creating the MHC-peptide complex; and the resulting MHC-peptide complex is positioned on the cell surface so that a portion
Attorney Docket #: 250298.000961 of the peptide is presented for binding with an antigen-recognition molecule. Each peptide is made up of a short chain of amino acids, and some of the amino acids of a peptide in an MHC-peptide complex are bound to the MHC molecule while at least some of the remaining amino acids are presented as available for binding with an antigen-recognition molecule. This cellular process is also carried out in cells native to the body. Generally, antigen- recognition molecules are able to distinguish between peptides presented on native cells vs. peptides presented on antigen-presenting cells so that normal cells are not attacked by the immune system. Antigen-recognition molecules are able to bind to a peptide of an MHC-peptide complex on the surface of an antigen-presenting cell to help the immune system neutralize the antigen. Research is underway with a goal of engineering antigen-recognition molecules to target cells that would otherwise not be targeted through the above-described mechanism. For instance, cancer cells are native cells that are not effectively suppressed by the immune system, and research has shown that it can be possible to engineer TCRs, antibodies, and other antigen-recognition molecules to target the cancer-specific MHC-peptide complexes on cancer cells. While targeted treatments with engineered antigen-recognition molecules may be effective to neutralize intended target cells, side effects of the treatment may be severe if engineered antigen-recognition molecules attack off-target native cells in addition to the intended target cells. Side effects are often identified during clinical trials, which can result in patient death, other adverse effects on patients, and expenditure of time and resources in research and development. Accordingly, there is a need in the art for methods and systems that allow for accurate and efficient prediction of off- targets to the target peptide of interest which helps evaluate the risk associated with the target peptide at the target selection step as well as help in screening the most specific antigen-recognition molecules. SUMMARY A workflow is presented herein which includes methods and computational systems and devices which are configured to execute computational steps of the methods. Computational systems and methods are presented herein for providing 3D computational models of potential off- target peptides each positioned in a groove of an MHC molecule (“MHC-off-target models”), quantifying structural similarity between each potential off-target peptide to a target peptide based on a comparison of the MHC-off-target models to a 3D computational model of a target MHC-
Attorney Docket #: 250298.000961 peptide complex (“MHC-target model”), and ranking the potential off-target peptides based on a structural similarity metric. Examples are provided in which aspects of the methods and systems can be applied to solve bioinformatics and treatment development problems. One embodiment includes a method for providing one or more off-target 3D computational models of a potential off-target peptide in a groove of an MHC molecule (“comparison MHC-off-target models”) for comparison to a 3D computational model of a target peptide in a complex with the MHC molecule (“MHC-target model”). The method includes generating a coarse-grained model of the potential off-target peptide in the groove of the MHC molecule (“coarse-grained MHC-off-target model”) by a substituting, in the MHC-target model, an amino acid sequence of the potential off-target peptide in place of an amino acid sequence of the target peptide; generating a plurality of refined computational models of the potential off-target peptide in the groove of the MHC molecule (“refined MHC-off-target models”) by computationally optimizing the coarse-grained MHC-off-target model multiple times such that each optimization of the coarse-grained MHC-off-target model results in a respective refined MHC-off-target model of the plurality of refined MHC-off-target models; and selecting the one or more comparison MHC-off-target models from the plurality of refined MHC-off-target models such that the one or more comparison MHC-off-target models have lower energy than a majority of the plurality of the refined MHC-off-target models. One embodiment includes a method for quantifying structural similarity between a potential off-target peptide in a groove of an MHC molecule and a target peptide in complex with the MHC molecule for the purposes of antigen-recognition molecule binding. The method includes obtaining a plurality of 3D computational models of the potential off-target peptide in the groove of the MHC molecule (“comparison MHC-off-target models”); calculating, for each of the plurality of comparison MHC-off-target models, one or more structural similarity metrics such that each of the one or more structural similarity metrics comprises a corresponding value for each of the plurality of comparison MHC-off-target models and represents a measure of structural similarity between the comparison MHC-off-target model and a 3D computational model of the target peptide in complex with the MHC molecule (“MHC-target model”); and calculating, for each of the one or more structural similarity metrics, a single composite structural similarity metric comprising a value based at least in part on the corresponding structural similarity metric values for at least a portion of the plurality of comparison MHC-off-target models.
Attorney Docket #: 250298.000961 One embodiment includes a method for identifying potential off-target peptide(s) based on sequence similarity and structural similarity for an antigen-recognition molecule that recognizes a target peptide presented in complex with an MHC molecule (MHC-target peptide complex. The method includes obtaining a pool of peptides of suitable length, optionally wherein the pool of peptides are expressed in normal tissues, optionally essential, normal tissues; identifying, within the pool, high sequence similarity peptides that have (i) more amino acid sequence similarity to the target peptide than a majority of peptides within the pool and (ii) have a binding affinity to the MHC molecule greater than a threshold value; and identifying the potential off-target peptides by selecting within the high sequence similarity peptides, the peptide(s) that are more structurally similar, when positioned in a groove of the MHC molecule, to the MHC-target peptide complex than a majority of the high sequence similarity peptides. One embodiment includes a method of ranking a plurality of potential off-target peptides of a target peptide. The method includes obtaining a 3D computational model of a target peptide in complex with the MHC molecule (“MHC-target model”); obtaining a plurality of off target 3D computational models of a potential off-target peptide in a groove of an MHC molecule (“comparison MHC-off-target models”) such that each of the plurality of off-target peptides is respectively represented in one or more comparison MHC-off-target models of the plurality of comparison MHC-off-target models; computing a structural similarly metric for each of the plurality of off-target peptides such that the structural similarity metric indicates a degree of similarity between the MHC-target model and at least a portion of the one or more comparison MHC-off-target models associated with the respective potential off-target peptide; and ranking the plurality of potential off-target peptides based at least in part on the structural similarity metric. One embodiment includes a for ranking potential target peptides to mitigate off-target toxicity. The method includes obtaining two or more potential target peptides among disease- associated peptides that are predicted to bind to an MHC molecule; obtaining, for each of the potential target peptides, a respective list of potential off-target peptides; ranking, for each of the potential target peptides, potential off-target peptides within the respective list of off-target peptides based at least in part on structural similarity of each potential off-target peptide to the potential target peptide; and ranking the two or more potential target peptides based at least in part on the ranking of potential off-target peptides within the respective list of potential off-target peptides.
Attorney Docket #: 250298.000961 One embodiment includes a method of ranking a plurality of potential off-target peptides for a target peptide. The method includes providing a target conformation, wherein the target conformation is a computational representation of a three-dimensional structure comprising the target peptide and a groove of a major histocompatibility complex (MHC) molecule, wherein the target peptide is positioned within the groove for binding to the MHC molecule; providing a plurality of off-target conformations, wherein each off-target conformation is a computational representation of a three-dimensional structure comprising a potential off-target peptide and the groove of the MHC molecule and wherein each of the off-target conformations corresponds to one of the plurality of potential off-target peptides positioned within the groove for binding to the MHC molecule; aligning the target conformation with each of the off-target conformations in three- dimensional space; computing a difference value for each of the off-target conformations, wherein each difference value quantifies a difference between the target conformation and one of the off- target conformations; and ranking the plurality of off-target conformations based on their respective difference values. One embodiment includes a non-transitory computer-readable medium configured to communicate with one or more processor(s) of a computational device, the non-transitory computer-readable medium including instructions thereon, that when executed by the processor(s), cause the computational device to perform any of the methods described herein. Steps of the method embodiments can be executed in various orders to achieve the desired output of the method as understood by a person skilled in the pertinent art. Methods can include additional and/or alternative steps as understood by a person skilled in the pertinent art. One embodiment includes a system of one or more computers configured to perform particular operations or actions by virtue of having software, firmware, hardware, or a combination of them installed on the system that in operation causes or cause the system to perform the actions. One or more computer programs can be configured to perform particular operations or actions by virtue of including instructions that, when executed by data processing apparatus, cause the apparatus to perform the actions. The system of one or more computers can be configured to computationally carry out at least a portion of the steps of some or all of the method embodiments. For instance, the system can include non-transitory computer-readable medium in communication with one or more processors, and including instructions thereon, that when executed by the processor(s) causes the system to execute steps of the method embodiments.
Attorney Docket #: 250298.000961 BRIEF DESCRIPTION OF THE DRAWINGS The above and further aspects of this invention should be read with reference to the drawings, in which like elements in different drawings are identically numbered. The drawings, which are not necessarily to scale, depict selected embodiments and are not intended to limit the scope of the invention. The detailed description illustrates by way of example, not by way of limitation, the principles of the invention. This description will clearly enable one skilled in the art to make and use the invention, and describes several embodiments, adaptations, variations, alternatives, and uses of the invention, including what is presently believed to be the best mode of carrying out the invention. The patent or application file contains at least one drawing executed in color. Copies of this patent or patent application publication with color drawings will be provided by the Office upon request and payment of the necessary fee. FIG. 1 is a flow diagram illustrating an exemplary method for ranking a plurality of potential off-target peptides of a target peptide. FIG. 2 is a flow diagram illustrating an exemplary method for providing one or more comparison MHC-off-target models for comparison to an MHC-target model. FIG. 3 is a flow diagram illustrating an exemplary method for quantifying structural similarity between a potential off-target peptide in a groove of an MHC molecule and a target peptide in complex with the MHC molecule for the purposes of antigen-recognition molecule binding. FIG. 4 is a flow diagram illustrating an exemplary method for identifying potential off- target peptide(s) based on sequence similarity and structural similarity for an antigen-recognition molecule that recognizes a target peptide presented in complex with an MHC molecule (MHC- target peptide complex). FIG. 5 is a flow diagram illustrating an exemplary method for ranking potential target peptides to mitigate off-target toxicity. FIG. 6 is a block diagram of an exemplary system for development of an antigen recognition molecule. FIG. 7 is a block diagram of an exemplary structure-based off-target prediction engine. FIG. 8 is a block diagram of an embodiment of the structure-based off-target prediction engine.
Attorney Docket #: 250298.000961 FIG. 9 is a block diagram of another embodiment of the structure-based off-target prediction engine. FIG. 10 illustrates a block diagram of an exemplary embodiment of a target toxicity database. FIG. 11 illustrates a block diagram of an embodiment of a computing device. FIG. 12 illustrates a block diagram of an embodiment of a computing network. FIG. 13 illustrates cellular functions related to the example embodiments presented herein. FIG. 14 is a block diagram of an embodiment of the structure-based off-target prediction engine applied as a proof of concept to a MAGEA3168-176 (EVDPIGHLY (SEQ ID NO: 29)) – HLA- A*01:01 complex. FIG. 15 is a block diagram of another embodiment of the structure-based off-target prediction engine applied as a proof of concept to a MAGEA3168-176 (EVDPIGHLY (SEQ ID NO: 29)) – HLA-A*01:01 complex. FIGs. 16A through 16E include a chart in which 231 potential off-target peptides of the MAGEA3168-176 (EVDPIGHLY (SEQ ID NO: 29)) – HLA-A*01:01 complex are ranked according to root mean square deviation (RMSD) of conformation of the respective off-target peptide to the target peptide. FIG. 16A includes the top 50 off-target peptides, i.e., highest priority, highest ranked off-target peptides. FIG.16A discloses SEQ ID NOS 202-207, 47, 208-214, 216-219, 215, 220-230, 45, 261-266, 232, and 267-278, respectively, in order of appearance. FIG. 16B includes the 51st through 100th ranked off-target peptides. FIG. 16B discloses SEQ ID NOS 279-324, 39, and 325-327, respectively, in order of appearance. FIG. 16C includes the 101st through 150th ranked off-target peptides. FIG.16C discloses SEQ ID NOS 328-339, 44, 340-352, 231, 353-368, 32, 369, 41, and 370-373, respectively, in order of appearance. FIG. 16D includes the 151st through 200th ranked off-target peptides. FIG. 16D discloses SEQ ID NOS 374-392, 43, 393-418, 30, and 419-421, respectively, in order of appearance. FIG. 16E includes the 201st through 231st ranked off-target peptides. FIG. 16E discloses SEQ ID NOS 422-437, 31, and 438-451, respectively, in order of appearance. FIGs. 17A through 17E include a chart in which 231 potential off-target peptides of the MAGEA3168-176 (EVDPIGHLY (SEQ ID NO: 29)) – HLA-A*01:01 complex are ranked according to root mean square deviation (RMSD) of conformation of the respective off-target peptide and
Attorney Docket #: 250298.000961 HLA groove to the target HLA-peptide complex. FIG.17A includes the top 50 off-target peptides, i.e., highest priority, highest ranked off-target peptides. FIG. 17A discloses SEQ ID NOS 47, 45, 209, 231-232, 321, 39, 218, 215, 217, 368, 313, 281, 263, 431, 269, 443, 452, 294, 314, 207, 361, 346, 283, 223, 227, 279, 204, 332, 406, 342, 213, 261, 367, 325, 324, 229, 203, 326, 339, 369, 208, 374, 340, 320, 216, 211, 319, 268, and 225, respectively, in order of appearance. FIG. 17B includes the 51st through 100th ranked off-target peptides. FIG. 17B discloses SEQ ID NOS 262, 205, 311, 376, 286, 214, 438, 206, 301, 389, 344, 222, 296, 380, 219, 32, 271, 212, 440, 336, 333, 322, 210, 221, 273, 266, 354, 416, 226, 338, 270, 295, 299, 364, 435, 427, 419, 312, 334, 350, 335, 317, 331, 228, 377, 407, 297, 359, 292, and 449, respectively, in order of appearance. FIG. 17C includes the 101st through 150th ranked off-target peptides. FIG. 17C discloses SEQ ID NOS 363, 293, 396, 316, 291, 351, 373, 399, 43, 309, 308, 372, 337, 304, 274, 362, 387, 224, 403, 415, 202, 392, 288, 352, 30, 451, 328, 421, 285, 381, 408, 298, 425, 353, 386, 409, 400, 275, 341, 422, 414, 277, 300, 411, 441, 429, 410, 357, 358, and 437, respectively, in order of appearance. FIG. 17D includes the 151st through 200th ranked off-target peptides. FIG. 17D discloses SEQ ID NOS 404, 379, 278, 315, 306, 398, 397, 302, 402, 264, 453, 375, 318, 230, 284, 282, 307, 329, 370, 347, 428, 348, 417, 310, 265, 436, 276, 378, 395, 267, 433, 442, 401, 323, 413, 280, 394, 366, 220, 371, 305, 349, 287, 360, 384, 289, 450, 44, 365, and 343, respectively, in order of appearance. FIG. 17E includes the 201st through 231st ranked off-target peptides. FIG. 17E discloses SEQ ID NOS 430, 303, 426, 423, 41, 424, 432, 393, 439, 418, 444, 355, 446, 390, 290, 356, 383, 412, 385, 388, 272, 330, 420, 345, 31, 405, 448, 327, 391, 445, and 447, respectively, in order of appearance. FIGs. 18A through 18E include a chart in which 231 potential off-target peptides of the MAGEA3168-176 (EVDPIGHLY (SEQ ID NO: 29)) – HLA-A*01:01 complex are ranked according to similarity of molecular surface interaction fingerprints (MSIFs) of the respective off-target peptide and HLA groove to the target HLA-peptide complex. FIG. 18A includes the top 50 off- target peptides, i.e., highest priority, highest ranked off-target peptides. FIG. 18A discloses SEQ ID NOS 311, 419, 292, 452, 359, 318, 229, 209, 47, 269, 43, 223, 232, 357, 44, 393, 399, 447, 309, 387, 317, 203, 424, 306, 453, 215, 383, 261, 320, 421, 380, 427, 301, 308, 414, 362, 406, 281, 325, 369, 270, 213, 290, 342, 454, 202, 341, 287, 265, and 207, respectively, in order of appearance. FIG. 18B includes the 51st through 100th ranked off-target peptides. FIG. 18B discloses SEQ ID NOS 206, 347, 397, 413, 355, 30, 435, 405, 450, 381, 319, 339, 312, 299, 219, 214, 221, 327, 295, 375, 263, 332, 222, 402, 288, 417, 390, 204, 225, 230, 389, 358, 321, 386,
Attorney Docket #: 250298.000961 337, 374, 330, 437, 368, 348, 227, 446, 45, 267, 403, 316, 216, 336, 277, and 338, respectively, in order of appearance. FIG. 18C includes the 101st through 150th ranked off-target peptides. FIG. 18C discloses SEQ ID NOS 326, 291, 212, 268, 224, 226, 443, 367, 39, 377, 329, 324, 379, 353, 401, 363, 211, 378, 400, 231, 266, 384, 278, 433, 279, 310, 360, 280, 349, 286, 428, 333, 31, 404, 334, 425, 407, 373, 442, 217, 305, 345, 307, 432, 303, 208, 285, 429, 356, and 282, respectively, in order of appearance. FIG.18D includes the 151st through 200th ranked off-target peptides. FIG. 18D discloses SEQ ID NOS 436, 409, 351, 331, 340, 364, 323, 431, 395, 366, 408, 272, 296, 275, 274, 293, 361, 398, 262, 376, 372, 346, 300, 273, 370, 297, 411, 322, 220, 394, 276, 392, 371, 304, 388, 422, 210, 426, 445, 412, 448, 264, 396, 41, 328, 314, 441, 423, 410, and 343, respectively, in order of appearance. FIG. 18E includes the 201st through 231st ranked off-target peptides. FIG. 18E discloses SEQ ID NOS 294, 271, 335, 420, 350, 391, 438, 283, 354, 315, 289, 218, 344, 32, 416, 298, 444, 451, 418, 415, 313, 430, 449, 385, 228, 302, 205, 284, 352, 440, and 439, respectively, in order of appearance. FIG. 18F discloses SEQ ID NOS 311, 419, 292, 452, 359, 318, 229, 209, 47 and 269, respectively, in order of appearance. FIG. 19A illustrates a superposition of a 3D computational model of the MAGEA3168-176 (EVDPIGHLY (SEQ ID NO: 29)) in a complex with the HLA-A*01:01 MHC molecule and a 3D computational model of the TTN off-target peptide ESDPIVAQY (SEQ ID NO: 47) in the groove of the HLA-A*01:01 molecule. FIG. 19B illustrates a superposition of a 3D computational model of the TTN off-target peptide ESDPIVAQY (SEQ ID NO: 47) in the groove of the HLA-A*01:01 molecule and an experimentally determined 3D computational model of the TTN off-target peptide ESDPIVAQY (SEQ ID NO: 47) in the groove of the HLA-A*01:01 molecule for the sake of verifying the exemplary methods illustrated in FIGs. 14 and 15. FIG. 20A illustrates a superposition of a 3D computational model of the MAGEA3168-176 (EVDPIGHLY (SEQ ID NO: 29)) in a complex with the HLA-A*01:01 MHC molecule and a 3D computational model of the MRPL43 potential off-target peptide TVDPISSSL (SEQ ID NO: 202) in the groove of the HLA-A*01:01 molecule. FIG. 20B illustrates a superposition of a 3D computational model of the MAGEA3168-176 (EVDPIGHLY (SEQ ID NO: 29)) in a complex with the HLA-A*01:01 MHC molecule and a 3D computational model of the IGHM off-target peptide ESATITCLV (SEQ ID NO: 204) in the groove of the HLA-A*01:01 molecule.
Attorney Docket #: 250298.000961 FIG. 21 is a block diagram of an embodiment of the structure-based off-target prediction engine applied as a proof of concept to a WT1126- 134 RMFPNAPYL (SEQ ID NO: 241) – HLA- A*02:01 complex. FIGs. 22A through 22D include a chart in which 142 potential off-target peptides of the WT1126- 134 RMFPNAPYL (SEQ ID NO: 241) – HLA-A*02:01 complex are ranked according to root mean square deviation (RMSD) of conformation of the respective off-target peptide to the target. FIG. 22A includes the top 40 off-target peptides, i.e., highest priority, highest ranked off- target peptides. FIG.22A discloses SEQ ID NOS 455-457, 235, 238, 458-463, 237, 464, 240, 465- 467, 233, and 468-489, respectively, in order of appearance. FIG. 22B includes the 41st through 80th ranked off-target peptides. FIG. 22B discloses SEQ ID NOS 490-503, 239, and 504-528, respectively, in order of appearance. FIG. 22C includes the 81st through 120th ranked off-target peptides. FIG. 22C discloses SEQ ID NOS 529-549, 236, and 550-567, respectively, in order of appearance. FIG. 22D includes the 121st through 142nd ranked off-target peptides. FIG. 22D discloses SEQ ID NOS 568-586 and 249-251, respectively, in order of appearance. FIGs. 23A through 23D include a chart in which 142 potential off-target peptides of the WT1126- 134 RMFPNAPYL (SEQ ID NO: 241) – HLA-A*02:01 complex are ranked according to root mean square deviation (RMSD) of conformation of the respective off-target peptide to the target at residue positions 1, 2, 3, and 4 only. FIG. 23A includes the top 40 off-target peptides, i.e., highest priority, highest ranked off-target peptides. FIG. 23A discloses SEQ ID NOS 488, 505, 457, 237, 552, 504, 578, 570, 528, 482, 463, 553, 235, 238, 455, 495, 456, 236, 240, 233, 467, 479, 500, 459, 501, 466, 525, 522, 511, 506, 516, 518, 503, 567, 475, 458, 476, 531, 513, and 470, respectively, in order of appearance. FIG.23B includes the 41st through 80th ranked off-target peptides. FIG. 23B discloses SEQ ID NOS 535, 494, 461, 468, 559, 496, 558, 557, 460, 524, 462, 478, 473, 489, 465, 464, 491, 477, 481, 517, 472, 469, 498, 471, 573, 530, 583, 521, 250, 249, 586, 561, 581, 541, 508, 572, 585, 502, 251, and 564, respectively, in order of appearance. FIG. 23C includes the 81st through 120th ranked off-target peptides. FIG. 23C discloses SEQ ID NOS 239, 580, 515, 538, 512, 520, 497, 551, 532, 523, 534, 487, 545, 499, 529, 484, 582, 542, 574, 483, 568, 480, 509, 533, 550, 526, 566, 560, 555, 577, 543, 527, 576, 569, 507, 565, 474, 563, 575, and 584, respectively, in order of appearance. FIG. 23D includes the 121st through 142nd ranked off-target peptides. FIG. 23D discloses SEQ ID NOS 490, 493, 519, 547, 485, 546, 549,
Attorney Docket #: 250298.000961 514, 571, 537, 486, 562, 492, 510, 536, 556, 544, 554, 579, 548, and 539-540, respectively, in order of appearance. FIG. 24A illustrates a superposition of a 3D computational model of the WT1126-134 RMFPNAPYL (SEQ ID NO: 241) target peptide in a complex with the HLA-A*02:01 MHC molecule and a 3D computational model of the SF3B4 off-target peptide KLYGKPIRV (SEQ ID NO: 235) in the groove of the HLA-A*02:01 molecule. FIG. 24B illustrates a superposition of a 3D computational model of the WT1126-134 RMFPNAPYL (SEQ ID NO: 241) target peptide in a complex with the HLA-A*02:01 MHC molecule and a 3D computational model of the ILF2 off-target peptide KILPTLEAV (SEQ ID NO: 233) in the groove of the HLA-A*02:01 molecule. FIG. 24C illustrates a superposition of a 3D computational model of the WT1126-134 RMFPNAPYL (SEQ ID NO: 241) target peptide in a complex with the HLA-A*02:01 MHC molecule and a 3D computational model of the USP9Y off-target peptide RLWGEPVNL (SEQ ID NO: 240) in the groove of the HLA-A*02:01 molecule. FIG. 24D illustrates a superposition of a 3D computational model of the WT1126-134 RMFPNAPYL (SEQ ID NO: 241) target peptide in a complex with the HLA-A*02:01 MHC molecule and a 3D computational model of the SHC1 off-target peptide RVPPPPQSV (SEQ ID NO: 236) in the groove of the HLA-A*02:01 molecule. FIG. 25A illustrates an X-Ray structure of ESK in complex with HLA-A*02:01/WT1126- 134. FIG. 25A discloses SEQ ID NO: 241. FIG.25B illustrates the X-Ray structure of ESK in complex with HLA-A*02:01/WT1126- 134 of FIG. 25A with a 3D computational model of off-target peptide KLYGKPIRV (SEQ ID NO: 235) in the groove of the HLA-A*02:01 molecule. FIG.25B discloses SEQ ID NOS 241 and 235, respectively, in order of appearance. DEFINITIONS Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one skilled in the pertinent art. Singular forms “a”, “an”, and “the” include plural references unless the context clearly dictates otherwise. Thus, for example, a reference to “a method” includes one or more methods,
Attorney Docket #: 250298.000961 and/or steps of the type described herein and/or which will become apparent to those persons skilled in the art upon reading this disclosure. The term “about” or “approximately” includes being within a meaningful range of a value. The allowable variation encompassed by the term “about” or “approximately” depends on the particular system under study, and can be readily appreciated by one skilled in the pertinent art. The terms “major histocompatibility complex,” and “MHC” encompass the terms “human leukocyte antigen” or “HLA” (the latter two of which are generally reserved for human MHC molecules), naturally occurring MHC molecules (e.g., MHC class I molecule comprising MHC class I α (heavy) chain and β2 microglobulin; MHC class II molecule comprising MHC class II α chain and MHC class II β chain), individual chains of MHC molecules (e.g., MHC class I α (heavy) chain, MHC class II α chain, and MHC class II β chain), individual subunits of such chains of MHC molecules (e.g., α1, α2, and/or α3 subunits of MHC class I α chain, α1-α2 subunits of MHC class II α chain, β1-β2 subunits of MHC class II β chain) as well as portions (e.g., the peptide-binding portions, e.g., the peptide-binding grooves), mutants and various derivatives thereof (including fusions proteins), wherein such portion, mutants and derivatives retain the ability to display an antigenic peptide for recognition by a T-cell receptor (TCR), e.g., an antigen- specific TCR. An MHC class I molecule comprises a peptide binding groove formed by the α1 and α2 domains of the heavy a chain that can stow a peptide of around 14 amino acids. In certain embodiments, an MHC class I molecule can stow a peptide of about 8 amino acids in length. In certain embodiments, an MHC class I molecule can stow a peptide of about 9 amino acids in length. In certain embodiments, an MHC class I molecule can stow a peptide of about 10 amino acids in length. In certain embodiments, an MHC class I molecule can stow a peptide of about 11 amino acids in length. In certain embodiments, an MHC class I molecule can stow a peptide of about 12 amino acids in length. In certain embodiments, an MHC class I molecule can stow a peptide of about 13 amino acids in length. In certain embodiments, an MHC class I molecule can stow a peptide of about 14 amino acids in length. Despite the fact that both classes of MHC bind a core of about 9 amino acids (e.g., 5 to 16 amino acids) within peptides, the open-ended nature of MHC class II peptide binding groove (the α1 domain of a class II MHC α polypeptide in association with the β1 domain of a class II MHC β polypeptide) allows for a wider range of peptide lengths. Peptides binding MHC class II usually vary between 12 and 20 amino acids in
Attorney Docket #: 250298.000961 length, though shorter or longer lengths (e.g., 23 amino acids in length) are not uncommon. As a result, peptides may shift within the MHC class II peptide binding groove, changing which 9-mer sits directly within the groove at any given time. In certain embodiments, an MHC class II molecule can stow a peptide of about 12 amino acids in length. In certain embodiments, an MHC class II molecule can stow a peptide of about 13 amino acids in length. In certain embodiments, an MHC class II molecule can stow a peptide of about 14 amino acids in length. In certain embodiments, an MHC class II molecule can stow a peptide of about 15 amino acids in length. In certain embodiments, an MHC class II molecule can stow a peptide of about 16 amino acids in length. In certain embodiments, an MHC class II molecule can stow a peptide of about 17 amino acids in length. In certain embodiments, an MHC class II molecule can stow a peptide of about 18 amino acids in length. In certain embodiments, an MHC class II molecule can stow a peptide of about 19 amino acids in length. In certain embodiments, an MHC class II molecule can stow a peptide of about 20 amino acids in length. In some embodiments, the MHC-peptide complex described herein may be an MHC-peptide complex from a non-human animal. In other embodiments, the MHC-peptide complex described herein may include an HLA-peptide complex, i.e., an MHC-peptide complex from a human. The term “non-human animal” and the like refers to any vertebrate organism that is not a human. In some embodiments, a non-human animal is a cyclostome, a bony fish, a cartilaginous fish (e.g., a shark or a ray), an amphibian, a reptile, a mammal, and a bird. In some embodiments, a non-human animal is a mammal. In some embodiments, a non-human mammal is a primate, a goat, a sheep, a pig, a dog, a cow, or a rodent. In some embodiments, a non-human animal is a rodent such as a rat or a mouse. The term “antigen” refers to any agent (e.g., protein, peptide, polysaccharide, glycoprotein, glycolipid, nucleotide, portions thereof, or combinations thereof) that, when introduced into an immunocompetent host is recognized by the immune system of the host and elicits an immune response by the host. The T-cell receptor recognizes a peptide presented in the context of a major histocompatibility complex (MHC) as part of an immunological synapse. The peptide-MHC (pMHC) complex is recognized by TCR, with the peptide (antigenic determinant) and the TCR idiotype providing the specificity of the interaction. Accordingly, the term “antigen” encompasses peptides presented in the context of MHCs, e.g., peptide-MHC complexes. The peptide displayed on MHC may also be referred to as an “epitope” or an “antigenic determinant”.
Attorney Docket #: 250298.000961 The terms “peptide,” “antigenic determinant,” “epitopes,” etc., encompass not only those presented naturally by antigen-presenting cells (APCs), but may be any desired peptide so long as it is recognized by an immune cell of an animal, e.g., when presented appropriately to the cells of an immune system. For example, a peptide having an artificially prepared amino acid sequence may also be used as the epitope. The term “antigen-recognition molecule” refers to any molecule that is capable of recognizing an antigen as defined above. Antigen-recognition molecules can include, but are not limited to, T cell receptors (TCR), antibodies, antibody fragments, or chimeric antigen receptors (CARs). “MHC-peptide complex,” “peptide-MHC complex,” “pMHC complex,” “peptide-in- groove,” and the like includes: (i) an MHC molecule, e.g., a human and/or non-human animal MHC molecule, or portion thereof (e.g., the peptide-binding groove thereof, and e.g., the extracellular portion thereof), and (ii) a peptide (e.g., an antigenic peptide), where the MHC molecule and the peptide are complexed in such a manner that the pMHC complex can specifically bind a T-cell receptor. A pMHC complex encompasses cell surface expressed pMHC complexes and soluble pMHC complexes. “HLA-peptide complex,” “peptide-HLA complex,” “pHLA complex,” and the like refers to an MHC-peptide complex wherein the MHC molecule is a Human Leukocyte Antigen (HLA) molecule. The terms “antibody,” “antibodies,” “immunoglobulin, “binding protein” and the like refer to monoclonal antibodies, multispecific antibodies, human antibodies, humanized antibodies, chimeric antibodies, single-chain Fvs (scFv), single chain antibodies, Fab fragments, F(ab′) fragments, disulfide-linked Fvs (sdFv), intrabodies, minibodies, diabodies and anti-idiotypic (anti- Id) antibodies (including, e.g., anti-Id antibodies to antigen-specific TCR), and epitope-binding fragments of any of the above. The terms “antibody” and “antibodies” also refer to covalent diabodies such as those disclosed in U.S. Pat. Appl. Pub. 20070004909, incorporated herein by reference in its entirety, and Ig-DARTS such as those disclosed in U.S. Pat. Appl. Pub. 20090060910, incorporated herein by reference in its entirety. In some instances, an immunoglobulin or antibody may be a membrane-bound immunoglobulin or antibody, such as a B Cell Receptor (BCR).
Attorney Docket #: 250298.000961 A “pMHC-binding protein” refers to an antigen-binding protein (e.g., an immunoglobulin, antibody, TCR, CAR, or the like) that specifically binds a pMHC complex. An “individual” or “subject” or “animal” refers to humans, veterinary animals (e.g., cats, dogs, cows, horses, sheep, pigs, etc.) and experimental animal models of diseases (e.g., mice, rats). In one embodiment, the subject is a human. The terms “protein” and “polypeptide”, used interchangeably herein, encompass all kinds of naturally occurring and synthetic proteins, including protein fragments of all lengths, fusion proteins and modified proteins, including without limitation, glycoproteins, as well as all other types of modified proteins (e.g., proteins resulting from phosphorylation, acetylation, myristoylation, palmitoylation, glycosylation, oxidation, formylation, amidation, polyglutamylation, ADP-ribosylation, pegylation, biotinylation, etc.). Small polypeptides of less than 100 amino acids, preferably less than 50 amino acids, may be referred to as “peptides”. The terms “polynucleotide” and “nucleic acid”, used interchangeably herein, include polymeric forms of nucleotides of any length, including ribonucleotides (RNA), deoxyribonucleotides (DNA), or analogs or modified versions thereof. They include single-, double-, and multi-stranded DNA or RNA, genomic DNA, complementary DNA (cDNA), DNA- RNA hybrids, and polymers comprising purine bases, pyrimidine bases, or other natural, chemically modified, biochemically modified, non-natural, or derivatized nucleotide bases. In general, a "promoter" or "promoter sequence" is a DNA regulatory region capable of binding an RNA polymerase in a cell (e.g., directly or through other promoter-bound proteins or substances) and initiating transcription of a coding sequence. A promoter may be operably linked to other expression control sequences, including enhancer and repressor sequences and/or with a polynucleotide described herein. The term “operably linked” or the like refers to a juxtaposition wherein the components described are in a relationship permitting them to function in their intended manner. For example, a control sequence “operably linked” to a coding sequence is ligated in such a way that expression of the coding sequence is achieved under conditions compatible with the control sequences. “Operably linked” sequences include both expression control sequences that are contiguous with a gene of interest and expression control sequences that act in trans or at a distance to control a gene of interest (or sequence of interest). The term “expression control sequence” includes polynucleotide sequences, which are necessary to affect the expression and processing of coding
Attorney Docket #: 250298.000961 sequences to which they are ligated. “Expression control sequences” include: appropriate transcription initiation, termination, promoter and enhancer sequences; efficient RNA processing signals such as splicing and polyadenylation signals; sequences that stabilize cytoplasmic mRNA; sequences that enhance translation efficiency (i.e., Kozak consensus sequence); sequences that enhance polypeptide stability; and when desired, sequences that enhance polypeptide secretion. The nature of such control sequences differs depending upon the host organism. For example, in prokaryotes, such control sequences generally include promoters, ribosomal binding sites and transcription termination sequences, while in eukaryotes typically such control sequences include promoters and transcription termination sequences. The term “control sequences” is intended to include components whose presence is essential for expression and processing, and can also include additional components whose presence is advantageous, for example, leader sequences and fusion partner sequences. The term “isolated” refers to a homogenous population of molecules (such as polynucleotides or polypeptides) which have been substantially separated and/or purified away from other components of the system the molecules are produced in, such as a recombinant cell, as well as a protein that has been subjected to at least one purification or isolation step. In certain aspects, “isolated” refers to a molecule that is substantially free of other cellular material and/or chemicals and encompasses molecules that are isolated to a higher purity, such as to 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or 100% purity. The term “isolated” as used herein may also refer to a cell, or homogenous population of cells, that has been removed from its natural environment and substantially separated from other cellular components with which it is naturally associated. This includes cells that have been cultured in vitro or otherwise manipulated outside of their native biological context. The term encompasses cells that are part of a purified population, free from significant contamination by other cell types, and may include, e.g., genetically modified cells, stem cells, or cells derived from tissues or organs. In some embodiments, an isolated cell of the present disclosure is an immune cell, e.g., an antigen-presenting cell (APC).. The term “derivative” as used herein refers to a peptide, polypeptide, or polynucleotide, or a variant or analog thereof, comprising one or more mutations and/or chemical modifications as compared to a reference peptide, polypeptide or polynucleotide. Mutations and/or chemical modifications are further detailed below and can include, for example, insertions, substitutions,
Attorney Docket #: 250298.000961 deletions, transversions, and/or inversions at one or more locations in the amino acid or nucleotide sequence. The term “library” refers to an isolated collection of at least two elements that differ from one another in at least one aspect. For example, a “peptide library” is a collection of at least two peptides that may differ from one another by at least one amino acid. As another example, a “pMHC complex library” is a collection of pMHC complexes that may differ from one another by at least one amino acid in the peptide or at least one MHC polypeptide. The elements of the library are isolated from like type of elements that are not part of the library (e.g., peptides of a peptide library are isolated from peptides that are not part of the library). The library may exist in vitro or ex vivo. One or more elements (e.g., each element) of the library may be isolated from one or more other elements (e.g., each other element) of the library. A library may be “sorted” such that each of the one or more isolated elements exists in an identifiable and accessible physical space, such as a cell or a container, so that each set of the one or more isolated elements may be selectively accessed or pulled from the library for use according to the disclosure elsewhere herein. Each container may be separable (e.g., a vial) or part of an integral solid support (e.g., an individual well of a multi-well plate). One or more elements of a library (including partial portions or the entirety of the library) may be used according to the disclosure herein. Various elements of a library may be used contemporaneously or consecutively, such as for screening antigen-recognition molecules for off-target cross-reactivity. In accordance with the disclosure herein, there may be employed conventional molecular biology, microbiology, and recombinant DNA techniques within the skill of the art. Such techniques are explained fully in the literature. See, e.g., Sambrook, Fritsch & Maniatis, Molecular Cloning: A Laboratory Manual, Second Edition. Cold Spring Harbor, NY: Cold Spring Harbor Laboratory Press, 1989 (herein “Sambrook et al., 1989”); DNA Cloning: A Practical Approach, Volumes I and II (D.N. Glover ed.1985); Oligonucleotide Synthesis (M.J. Gait ed.1984); Nucleic Acid Hybridization [B.D. Hames & S.J. Higgins eds. (1985)]; Transcription And Translation [B.D. Hames & S.J. Higgins, eds. (1984)]; Animal Cell Culture [R.I. Freshney, ed. (1986)]; Immobilized Cells And Enzymes [IRL Press, (1986)]; B. Perbal, A Practical Guide To Molecular Cloning (1984); Ausubel, F.M. et al. (eds.). Current Protocols in Molecular Biology. John Wiley & Sons, Inc., 1994. These techniques include site directed mutagenesis as described in Kunkel, Proc. Natl. Acad. Sci. USA 82: 488- 492 (1985), U. S. Patent No. 5,071, 743, Fukuoka et al., Biochem.
Attorney Docket #: 250298.000961 Biophys. Res. Commun. 263: 357-360 (1999); Kim and Maas, BioTech. 28: 196-198 (2000); Parikh and Guengerich, BioTech. 24: 428-431 (1998); Ray and Nickoloff, BioTech. 13: 342-346 (1992); Wang et al., BioTech. 19: 556-559 (1995); Wang and Malcolm, BioTech. 26: 680-682 (1999); Xu and Gong, BioTech.26: 639-641 (1999), U.S. Patents Nos.5,789, 166 and 5,932, 419, Hogrefe, Strategies l4. 3: 74-75 (2001), U. S. Patents Nos. 5,702,931, 5,780,270, and 6,242,222, Angag and Schutz, Biotech. 30: 486-488 (2001), Wang and Wilkinson, Biotech. 29: 976-978 (2000), Kang et al., Biotech.20: 44-46 (1996), Ogel and McPherson, Protein Engineer.5: 467-468 (1992), Kirsch and Joly, Nucl. Acids. Res.26: 1848-1850 (1998), Rhem and Hancock, J. Bacteriol. 178: 3346-3349 (1996), Boles and Miogsa, Curr. Genet. 28: 197-198 (1995), Barrenttino et al., Nuc. Acids. Res. 22: 541-542 (1993), Tessier and Thomas, Meths. Molec. Biol. 57: 229-237, and Pons et al., Meth. Molec. Biol. 67: 209-218. The term “administration” and the like refers to and includes the administration of a composition (e.g., antigen-recognition molecule) to a subject or system (e.g., to a cell, organ, tissue, organism, or relevant component or set of components thereof). The skilled artisan will appreciate that route of administration may vary depending, for example, on the subject or system to which the composition is being administered, the nature of the composition, the purpose of the administration, etc. For example, in certain embodiments, administration to an animal subject (e.g., to a human or a rodent) may be bronchial (including by bronchial instillation), buccal, enteral, interdermal, intra-arterial, intradermal, intragastric, intramedullary, intramuscular, intranasal, intraperitoneal, intrathecal, intravenous, intraventricular, mucosal, nasal, oral, rectal, subcutaneous, sublingual, topical, tracheal (including by intratracheal instillation), transdermal, vaginal and/or vitreal. In some embodiments, administration may involve intermittent dosing. In some embodiments, administration may involve continuous dosing (e.g., perfusion) for at least a selected period of time. The term “essential, normal tissues” refers to tissues of a patient where an activity of a given antigen-recognition molecule administered for treating a disease may create unacceptable side-effects. The list of tissues considered essential, normal would vary depending on the disease being treated and on the risks associated with the disease itself (e.g., the list would be smaller for life-threatening diseases than for non-life-threatening diseases). For example but not by way of limitation, when treating life-threatening cancers, tissue types that may be considered non-essential may include breast, ovary and testes. The list of tissues considered essential, normal would also
Attorney Docket #: 250298.000961 vary depending on the likelihood for a given antigen-recognition molecule to reach such tissues. For example, brain may not be included in the list of essential, normal tissues in cases of antigen- recognition molecules which do not permeate blood-brain-barrier of patients with the disease being treated. The terms “component,” “engine,” “module,” “system,” “server,” “processor,” “memory,” and the like are intended to include one or more computer-related units, such as but not limited to hardware, firmware, a combination of hardware and software, software, or software in execution. For example, a component may be, but is not limited to being, a process running on a processor, an object, an executable, a thread of execution, a program, and/or a computer. By way of illustration, both an application running on a computing device and the computing device can be a component. One or more components can reside within a process and/or thread of execution and a component may be localized on one computer and/or distributed between two or more computers. In addition, these components can execute from various computer readable media having various data structures stored thereon. The components may communicate by way of local and/or remote processes such as in accordance with a signal having one or more data packets, such as data from one component interacting with another component in a local system, distributed system, and/or across a network such as the Internet with other systems by way of the signal. The term “3D computational model” refers to a computer-readable representation of a 3D (three-dimensional) structure that includes information about portions of the 3D structure in relation to each other. In examples presented herein in which the 3D structure includes one or more molecules, the 3D computational model of that 3D structure includes positions of atoms, or collections of atoms, in relation to each other. In some examples, the 3D computational model represents a conformation of molecule(s) such that arrangement in space of constituent atoms of the molecule(s) are represented in the 3D computational model. In examples presented herein in which the 3D structure includes a peptide, the 3D computational model includes positions of at least a portion of the amino acids of the peptide in relation to each other. For instance, a 3D computational model of a peptide may represent a conformation of the peptide, or a folded 3D structure of the peptide. For instance, a 3D computational model of a peptide, MHC molecule, and/or other molecule may be a file in Protein Data Bank (PDB) format, Macromolecular Crystallographic Information File (mmCIF or PDBx/mmCIF) format, Polygon File Format or
Attorney Docket #: 250298.000961 Stanford Triangle Format (PLY), or other suitable file or data structure format as understood by a person skilled in the pertinent art. A 3D computational model of a peptide, a peptide in a groove of an MHC molecule, or a peptide in complex with an MHC molecule can be computationally generated, computationally refined, and/or based on experimentally determined structures. The terms “MHC-target model” and “MHC-off-target model” refer to 3D computational models of peptides positioned in the grooves of, or in complex with, MHC molecules, respectively a target peptide positioned in a groove of, or in complex with, an MHC molecule and an off-target peptide positioned in a groove of, or in complex with, an MHC molecule. For purposes of 3D computational models, a peptide may be considered to be positioned in the groove of an MHC molecule or in complex with an MHC molecule when positioned or docked in a conformation and orientation relative to the binding groove of the MHC molecule that optimizes, based on at least one measure, the probability of peptide binding or loading on to the MHC molecule, regardless of whether stable peptide binding/loading has been experimentally validated or does in fact occur. A given target peptide may be represented by one or more MHC-target models, and likewise a given off-target peptide may be represented by one or more MHC-target models. MHC-target models and MHC-off-target models can be generated computationally and/or based on experimentally determined structures. For instance, MHC-target models and MHC-off-target models can be experimentally determined using methods such as X-ray crystallography, cryogenic electron microscopy (cryo-EM), Nuclear Magnetic Resonance (NMR), other suitable experimental method as understood by a person skilled in the pertinent art, or combinations thereof. MHC-target models and MHC-off-target models can be generated computationally by de novo/ab initio approaches or by using one or more templates. For instance, such models can be generated computationally by fragment assembly (e.g., using Rosetta or other such tools), by template-based modeling/homology-based modeling/threading (e.g., using Rosetta or other such tools), by sequence-based machine learning (e.g., using AlphaFold or other such tools), or by other suitable computational tool as understood by a person skilled in the pertinent art, or combinations thereof. MHC-target models and MHC-off-target models can be generated using both experimental data and computational prediction tools. For instance, an initial 3D computational model or a portion of a 3D computational model may be determined by experimentation and the experimental-based 3D computational model (or model portion) may be refined or supplemented using computational tools.
Attorney Docket #: 250298.000961 The terms “coarse-grained” and “refined”, when referring to a 3D computational model such as an MHC-target model and an MHC-off-target model, indicate a level of computational refinement performed on the 3D computational model to improve the accuracy and/or increase the resolution of the 3D computational model as compared to the physical structure that the 3D computational model represents. Generally, a “refined” 3D computational model is more accurate, higher resolution, and/or has been refined by more computational processing than a “coarse- grained” computational model. For instance, a coarse-grained model may be refined by minimizing some energy function or finding a minimum in an energy landscape (e.g., using Rosetta FlexPepDock, GROMOS force field, CHARMM force field, or other such tools). The term “connected” means that one function, feature, structure, or characteristic is directly joined to or in communication with another function, feature, structure, or characteristic. The term “coupled” means that one function, feature, structure, or characteristic is directly or indirectly joined to or in communication with another function, feature, structure, or characteristic. The terms “comprising” or “containing” or “including” are meant that at least the named element, or method step is present in article or method, but does not exclude the presence of other elements or method steps, even if the other such elements or method steps have the same function as what is named. As used herein, unless otherwise specified, the use of the ordinal adjectives “first,” “second,” “third,” etc., to describe a common object, merely indicate that different instances of like objects are being referred to, and are not intended to imply that the objects so described must be in a given sequence, either temporally, spatially, in ranking, or in any other manner. In this description, numerous specific details are set forth. It is to be understood, however, that implementations of the disclosed technology may be practiced without these specific details. In other instances, well-known methods, structures, and techniques have not been shown in detail in order not to obscure an understanding of this description. References to “one embodiment,” “an embodiment,” “some embodiments,” “example embodiment,” “various embodiments,” “one implementation,” “an implementation,” “example implementation,” “various implementations,” “some implementations,” etc., indicate that the implementation(s) of the disclosed technology so described may include a particular feature, structure, or characteristic, but not every implementation necessarily includes the particular feature, structure, or characteristic.
Attorney Docket #: 250298.000961 Further, repeated use of the phrase “in one implementation” does not necessarily refer to the same implementation, although it may. DETAILED DESCRIPTION Embodiments presented herein provide in silico (i.e., computational) methods for 3D modeling of off-target peptides for comparison to target peptides. In certain aspects, methods presented herein can be used for the ultimate purpose of antigen-recognition molecule development. Being able to model and compare peptide conformations and peptide-in-groove structures in silico can provide valuable information so that laboratory and human testing can be more efficient and reduce adverse events in clinical trial testing. Embodiments presented herein generally relate to identification of off-target peptide(s) that are similar to an intended target peptide of an MHC-target peptide complex, such that an antigen-recognition molecule that is engineered for the intended target MHC-target peptide complex is likely to also target the off-target peptide(s). Embodiments presented herein provide methods for generating 3D models of off-targets and targets, comparing off-target models to target models, ranking off-targets for a given target based on structural similarity so that higher ranked off-targets may be used for further in vivo studies, and ranking potential targets based on similarity of potential off-targets so that higher ranked targets and their associated off-targets may be used for further in vivo studies. In some embodiments, ranking and identification of the off-targets and/or targets can be agnostic of the antigen-recognition molecule so that the identified off-target peptides, predicted in vivo toxicity, and/or ranking of potential target peptides can be used to guide development of an antigen-recognition molecule engineered to target a target peptide having a low number of identified off-target peptides, a low predicted probability of in vivo toxicity, and/or a preferred ranking. Some embodiments disclosed herein include computational systems, engines, modules, devices, and/or networks configured to carry out a majority of steps associated with the above embodiments. The output of such computational systems, etc. can be used to inform antigen-recognition molecule development, screening of patients for clinical trials, individual patient treatment, and other such applications as understood by a person skilled in the pertinent art according to the teachings herein. One aim of some embodiments presented herein is to avoid side effects that would otherwise be identified during clinical trials, thereby reducing patient death,
Attorney Docket #: 250298.000961 reducing other adverse effects on patients, and reducing expenditure of time and resources in research and development. MHC molecules are generally classified into two categories: class I and class II MHC molecules. An MHC class I molecule is an integral membrane protein comprising a glycoprotein heavy chain, also referred to herein as the α chain, which has three extracellular domains (i.e., α1, α2 and α3) and two intracellular domains (i.e., a transmembrane domain (TM) and a cytoplasmic domain (CYT)). The heavy chain is noncovalently associated with a soluble subunit called β2 microglobulin (β2m or β2M). An MHC class II molecule or MHC class II protein is a heterodimeric integral membrane protein comprising one α chain and one β chain in noncovalent association. The α chain has two extracellular domains (α1 and α2), and two intracellular domains (a TM domain and a CYT domain). The β chain contains two extracellular domains (β1 and β2), and two intracellular domains (a TM domain and CYT domain). The domain organization of class I and class II MHC molecules forms the antigenic determinant binding site, e.g., the peptide-binding portion or peptide binding groove, of the MHC molecule. A peptide binding groove refers to a portion of an MHC protein that forms a cavity in which a peptide, e.g., antigenic determinant, can bind. The conformation of a peptide binding groove is capable of being altered upon binding of a peptide to enable proper alignment of amino acid residues important for TCR binding to the peptide-MHC (pMHC) complex. In some embodiments, MHC molecules include fragments of MHC chains that are sufficient to form a peptide binding groove. For example, a peptide binding groove of a class I protein can comprise portions of the α1 and α2 domains of the heavy chain capable of forming two β-pleated sheets and two α helices. Inclusion of a portion of the β2 microglobulin chain stabilizes the MHC class I molecule. While for most versions of MHC Class II molecules, interaction of the α and β chains can occur in the absence of a peptide, the two-chain molecule of MHC Class II is unstable until the binding groove is filled with a peptide. A peptide binding groove of a class II protein can comprise portions of the α1 and β1 domains capable of forming two β-pleated sheets and two α helices. A first portion of the α1 domain forms a first β-pleated sheet and a second portion of the α1 domain forms a first a helix. A first portion of the β1 domain forms a second β- pleated sheet and a second portion of the β1 domain forms a second a helix. The X-ray crystallographic structure of class II protein with a peptide engaged in the binding groove of the protein shows that one or both ends of the engaged peptide can project beyond the MHC protein
Attorney Docket #: 250298.000961 (Brown et al., pp. 33-39, 1993, Nature, Vol. 364; incorporated herein in its entirety by reference). Thus, the ends of the α1 and β1 α helices of class II form an open cavity such that the ends of the peptide bound to the binding groove are not buried in the cavity. Moreover, the X-ray crystallographic structure of class II proteins shows that the N-terminal end of the MHC β chain apparently projects from the side of the MHC protein in an unstructured manner since the first 4 amino acid residues of the β chain could not be assigned by X-ray crystallography. Many human and other mammalian MHCs are well known in the art. In some embodiments, the MHC molecule may be a human HLA molecule selected from the group consisting of HLA-A, HLA-B, HLA-C, HLA-E, HLA-F, and HLA-G. A list of commonly used HLA alleles is described in Shankarkumar et al. ((2004) The Human Leukocyte Antigen (HLA) System, Int. J. Hum. Genet. 4(2):91-103), incorporated herein in its entirety by reference. Shankarkumar et al. also present a brief explanation of HLA nomenclature used in the art. Additional information regarding HLA nomenclature and various HLA alleles can be found in Holdsworth et al. (2009) The HLA dictionary 2008: a summary of HLA-A, -B, -C, -DRB1/3/4/5, and DQB1 alleles and their association with serologically defined HLA-A, -B, -C, -DR, and -DQ antigens, Tissue Antigens 73:95-170, and a recent update by Marsh et al. (2010) Nomenclature for factors of the HLA system, 2010, Tissue Antigens 75:291-455, each of which publications is incorporated herein in its entirety by reference. In some embodiments, the MHC I or MHC II polypeptides may be derived from any functional human HLA-A, B, C, DR, or DQ molecules. In one embodiment, the HLA molecule is encoded by HLA-A2, such as an HLA-A*02:01 allele. In another embodiment, the HLA molecule is encoded by HLA-A1, such as an HLA-A*01:01 allele. Targeting peptide-MHC (pMHC) complexes specifically expressed on cells such as cancer cells via, e.g., antibody-based or cell-based therapeutics approaches, can be an effective way of destroying such cells. However, the potential off-targets associated with these pMHC complexes can often lead to off-target toxicity. The present disclosure provides, among other things, a method useful in the prediction of such off-targets. Methods useful in prediction of such off-targets are presented in WO2023122621A2, published June 29, 2023, which is incorporated by reference herein and attached as an Appendix hereto. In certain embodiments, WO2023122621A2 presents a method, referred to herein as the “PIGSPRED method” in which, for a target peptide, amino acid positions are distinguished as either being bound in an MHC-target peptide complex or available to bind to an antigen
Attorney Docket #: 250298.000961 recognition molecule. In certain embodiments, the PIGSPRED method identifies potential off- target peptides based on binding affinity to the MHC molecule and the similarity of the amino acid sequence of a potential off-target peptide to the available positions of the target peptide. WO2023122621A2 at paragraphs [0272]-[0274] and Table 1B presents one example in which the PIGSPRED method is applied to a MAGEA4 target peptide GVYDGREHTV (SEQ ID NO: 242). In this example, positions 4 and 6-9 of said target peptide are identified as available positions, and potential off-target GLADGRTHTV (SEQ ID NO: 243) is determined a higher degree of similarity to said target peptide than other potential off-target peptides GVPDCRIFTV (SEQ ID NO: 244), SVYDAREFSV (SEQ ID NO: 245), GLSDGQWHTV (SEQ ID NO: 246), GVFDNCSHTV (SEQ ID NO: 247), and KVSDGHFHTV (SEQ ID NO: 248). In some embodiments, the PIGSPRED method calculates a DoS score, wherein only positions of the target peptide identified as not involved in interacting with the MHC molecule are considered in calculating the DoS score, and the PIGSPRED method calculates a probability of in vivo toxicity of each potential target peptide based at least in part on the DoS scores of the off- target peptide(s). Once cancer-specific pHLAs are identified, PIGSPRED can be used to calculate the number of potential off-targets having sequence similarity associated with each cancer-specific pHLA. In certain embodiments of the PIGSPRED method, the number of potential off-targets is representative of the likelihood of off-target toxicity associated with a target and is used to rank the list of pHLA targets and to prioritize the targets for therapeutic development. In certain embodiments of the PIGSPRED method, the off-targets predicted by PIGSPRED can be used in experimental screening of therapeutic molecules that do not bind the off-targets. The most specific therapeutic molecules can thus be selected for further development. Another method, referred to herein as “X-scan” determines antigen-recognition molecule (e.g., TCR) binding motifs. In some embodiments, the X-scan method can be used to determine positions of a target peptide which are important for binding to a respective antigen recognition molecule by: (i) generating a plurality of mutant peptides which have exactly one amino acid substitution at exactly one position of the target peptide; and (ii) determining which mutated peptides lead to increased or decreased signaling (e.g., TCR signaling) when brought into contact with the antigen-recognition molecule. Details of the X-scan method are described in greater detail in Border, Ellen C., et al. "Affinity-enhanced T-cell receptors for adoptive T-cell therapy targeting
Attorney Docket #: 250298.000961 MAGE-A10: strategy for selection of an optimal candidate." Oncoimmunology 8.2 (2019), incorporated by reference herein in its entirety. In certain embodiments, the present disclosure provides computational methods based on 3D models of target and off-target peptides for ranking and/or selecting off-target peptides for use in in vitro methods for assessing off-target effects of an antigen-recognition molecule. In certain aspects, both the PIGSPRED method and the present disclosure describe methods which can be used to estimate off-target toxicity associated with a target peptide and identify potential off-target peptides for use in experimental screening of therapeutic molecules. In certain embodiments, systems and methods of the present disclosure can be utilized as an alternative to the PIGSPRED method; in certain embodiments, systems and methods of the present disclosure can incorporate or utilize the PIGSPRED method to pre-screen targets and/or off targets; in certain embodiments, systems and methods of the present disclosure can incorporate or utilize additional and/or alternative pre-screening methods to the PIGSPRED method; and in certain embodiments, a combination of similarity metrics determined by the PIGSPRED method (e.g., a probability of in vivo toxicity, risk metric, DoS, ranking) and/or alternative method can be used in combination with structural similarity metrics disclosed herein to prioritize target and/or off-target peptides for antigen-recognition molecule development. Various methods described herein may relate to identifying (e.g., ranking/selecting) target peptides and/or to identifying (e.g., ranking/selecting) off-target peptides for a given target peptide. Any of these methods may further comprise synthesizing a target peptide and/or one or more off-target peptides identified. Methods for peptide synthesis include those known in the art and described herein. Any of these methods may further comprise isolating (e.g., purifying) a target peptide and/or one or more off-target peptides identified. Methods for peptide isolation and/or purification include those known in the art and described herein. Any of these methods may further comprise loading a target peptide and/or one or more off-target peptides to an MHC molecule or any suitable component thereof to form a pMHC complex as described elsewhere herein. Any of these methods may further comprise binding a target peptide-MHC complex and/or one or more off-target peptide-MHC complexes to an antigen-recognition molecule (e.g., an antibody, TCR, or CAR). For example, any of these methods may comprise screening a target peptide-MHC complex and/or one or more off-target peptide MHC complexes for binding to an antigen-recognition molecule. Any of these methods may comprise incubating and/or contacting
Attorney Docket #: 250298.000961 a target peptide and/or one or more off-target peptides with one or more cells (e.g., pulsing cells with peptide as described elsewhere herein). In a further aspect, provided herein are off-target peptides identified using the methods described herein. Accordingly, the present disclosure also provides libraries (e.g., target-specific libraries) comprising one or more of the off-target peptides identified using the methods described herein. In some embodiments, the libraries of the present disclosure may include the target peptides associated with the off-target peptides as well. In some embodiments, the libraries of the present disclosure may include off-target peptides identified by analyzing experimental structures of a pHLA in complex with an antigen-recognition molecule. In some embodiments, off-target peptides identified using the methods described herein may be identified for a Melanoma-Associated Antigen 3 (MAGE3) target peptide, e.g., for MAGEA3168-176 target EVDPIGHLY (SEQ ID NO: 29). The MAGEA3 gene belongs to the MAGEA family, which comprises genes encoding proteins with significant sequence similarity, ranging from 50 to 80%. These genes are located at the Xq28 chromosomal region and exhibit variability in their promoters and first exons, allowing for diverse transcriptional regulation while maintaining functional consistency. MAGEA3 is a protein-coding gene linked to conditions such as, but not limited to, ocular melanoma and melanoma, and it plays a role in pathways like NF- kappaB signaling. MAGEA3 can function as an activator of ubiquitin ligase activity, influencing processes for example, without limitation, autophagy repression and tumor progression. Additionally, MAGEA3 is recognized by cytolytic T-lymphocytes in melanoma, highlighting its importance in cancer biology. In some embodiments, off-target peptides identified using the methods described herein may be identified for a Wilms Tumor 1 (WT1) target peptide, e.g., for WT1126-134 target RMFPNAPYL (SEQ ID NO: 241). The WT1 gene encodes a transcription factor characterized by four zinc-finger motifs at its C-terminus and a DNA-binding domain rich in proline and glutamine at the N-terminus. This gene can play a crucial role in the development of the urogenital system and can be associated with mutations in a subset of Wilms tumor patients. WT1 exhibits a complex pattern of tissue-specific imprinting, with varying expression from maternal and paternal alleles across different tissues. The WT1 gene has multiple transcript variants, some of which utilize a non-standard translation initiation codon. Additionally, WT1 mRNA can undergo tissue-specific RNA editing, which is developmentally regulated. Mutations in WT1 are linked to conditions such
Attorney Docket #: 250298.000961 as, but not limited to, acute myeloid leukemia, affecting prognosis and treatment resistance. The WT1 gene is involved in pathways related to sexual development and kidney function, and it has both tumor suppressor and oncogenic roles, depending on its isoform-specific functions. A variety of platforms for targeting MAGEA3 and/or WT1 are contemplated herein, such as, but not limited to, alternative format multispecific (e.g., bispecific) antibodies or antigen- binding fragments thereof, CAR T-cells (e.g., scFv-based CAR T cells) and engineered (T-cell receptor) TCR-T cells, and cancer vaccines. In one embodiment, anti-tumor TCRs may be engineered into T cells for cell-based therapy. In one embodiment, an anti-tumor TCR targeting MAGEA3168-176 target EVDPIGHLY (SEQ ID NO: 29)-HLA-A*01:01 can be developed for an HLA-A*01:01/MAGEA3168-176-targeted autologous TCR T cell therapy. In one embodiment, an anti-tumor TCR targeting WT1126-134 target RMFPNAPYL (SEQ ID NO: 241)-HLA-A*02:01 can be developed for an HLA-A*02:01/ WT1126-134-targeted autologous TCR T cell therapy. Without wishing to be bound by theory, TCRs can be a promising therapeutic modality for the treatment of patients with solid tumors. TCRs greatly expand the repertoire of tumor antigens that can be targeted using immunotherapy, compared to CARs and traditional antibodies. Over 75% of proteins reside exclusively within intracellular compartments. Tumor-associated antigens (TAAs) and tumor-specific antigens (TSAs) typically consist of peptides derived from intracellular proteins that are loaded onto MHC. TCRs recognize tumor antigens via interactions with peptide-MHC (pMHC) complexes. Intracellular antigens undergo proteasomal cleavage to generate antigenic peptides. Peptides are transported into the ER, where they are further processed and loaded onto MHC. pMHC complexes are subsequently trafficked to the cell surface to enable extracellular presentation of intracellular antigens. TCRs are therefore a powerful tool that which can use to leverage the adaptive immune system in targeting and eliminating solid tumors. Ensuring tumor specificity of a target p-MHC is not enough for therapeutic TCR/antibody development; off-target toxicity remains a challenge. In some embodiments, the cancer is a hematologic malignancy, for example, a lymphoma, a leukemia, or a myeloma. In some embodiments, the lymphoma is Hodgkin's lymphoma or non-Hodgkin’s lymphoma. In some embodiments, the leukemia is chronic lymphocytic leukemia (CLL), acute lymphocytic leukemia (ALL), chronic myeloid leukemia (CML), or acute myeloid leukemia (AML). In some embodiments, the cancer is a solid cancer. In
Attorney Docket #: 250298.000961 one embodiment, the cancer is a head and neck cancer. In one embodiment, the cancer is a renal cell carcinoma. In one embodiment, the cancer is a breast cancer, e.g., triple-negative breast cancer (TNBC). In one embodiment, the cancer is a non-small cell lung cancer (NSCLC), for example, lung adenocarcinoma. In one embodiment, the cancer is liver hepatocellular carcinoma (HCC). In one embodiment, the cancer is lung squamous cell carcinoma (SCC). In one embodiment, the cancer is bladder cancer. In one embodiment, the cancer is esophageal cancer. In one embodiment, the cancer is uveal melanoma. In one embodiment, the cancer is nasopharyngeal cancer. In one embodiment, the cancer is synovial sarcoma. In one embodiment, the cancer is ovarian cancer. In one embodiment, the cancer is uterine cancer. In one embodiment, the cancer is endometrial cancer. In one embodiment, the cancer is melanoma. In some embodiments, the cancer is selected from melanoma, non-small cell lung cancer (NSCLC), breast cancer, head and neck squamous cell carcinoma, bladder cancer, esophageal cancer, gastric cancer, ovarian cancer, and colorectal cancer. In some embodiments, the cancer is selected from Wilms tumor (i.e., nephroblastoma), acute myeloid leukemia, breast cancer, ovarian cancer, lung cancer, and prostate cancer. In various embodiments, a variety of platforms for targeting a MAGEA3 and/or WT1 are contemplated herein, such as, but not limited to, alternative format multispecific (e.g., bispecific) antibodies or antigen-binding fragments thereof, CAR T-cells (e.g., scFv-based CAR T cells) and engineered (T-cell receptor) TCR-T cells, and cancer vaccines. In one embodiment, anti-tumor TCRs may be engineered into T cells for cell-based therapy. In one embodiment, an anti-tumor TCR targeting MAGEA3168-176 target EVDPIGHLY (SEQ ID NO: 29)-HLA-A*01:01 can be developed for an HLA-A*01:01/MAGEA3168-176-targeted autologous TCR T cell therapy. In one embodiment, an anti-tumor TCR targeting WT1126-134 target RMFPNAPYL (SEQ ID NO: 241)- HLA-A*02:01 can be developed for an HLA-A*02:01/ WT1126-134-targeted autologous TCR T cell therapy. Without wishing to be bound by theory, TCRs can be a promising therapeutic modality for the treatment of patients with solid tumors. TCRs greatly expand the repertoire of tumor antigens that can be targeted using immunotherapy, compared to CARs and traditional antibodies. Over 75% of proteins reside exclusively within intracellular compartments. Tumor- associated antigens (TAAs) and tumor-specific antigens (TSAs) typically consist of peptides derived from intracellular proteins that are loaded onto MHC. TCRs recognize tumor antigens via
Attorney Docket #: 250298.000961 interactions with peptide-MHC (pMHC) complexes. Intracellular antigens undergo proteasomal cleavage to generate antigenic peptides. Peptides are transported into the ER, where they are further processed and loaded onto MHC. pMHC complexes are subsequently trafficked to the cell surface to enable extracellular presentation of intracellular antigens. TCRs are therefore a powerful tool that which can use to leverage the adaptive immune system in targeting and eliminating solid tumors. Ensuring tumor specificity of a target pMHC is not enough for therapeutic TCR/antibody development; off-target toxicity remains a challenge. In certain embodiments, the MAGEA3 and/or WT1 peptide described herein is wild type. In certain embodiments, the MAGEA3 and/or WT1 peptide described herein comprises a mutation (e.g., a deletion, a substitution, or an addition). Non-limiting examples of amino acid mutations comprise amino acid insertions, substitutions, and/or deletions. Inserted amino acid residues may be inserted at any position and inserted amino acid residues may be inserted in a way such that some of or all the inserted amino acid residues are immediately adjacent to one another or such that none of the inserted amino acid residues are immediately adjacent to one another. Amino acid substitution means exchanging an amino acid residue for a replacement amino acid residue at the same position. In some embodiments, the MAGEA3 peptide is a MAGEA3168-176 peptide having an amino acid sequence of EVDPIGHLY (SEQ ID NO: 29). See, e.g., NCBI Reference Sequences 5BRZ_C, NP_005353.1, BAD97239.1, KAI2601173.1, 4V0P_A, 8T9A_C, 9BD2_A, and KAI2601175.1, which are each herein incorporated by reference in their entirety. The peptide can be a peptide comprising about 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 25, 30, 35, 40, 50, 60, 70, 80, 90, or 100 amino acid residues of the MAGEA3 protein. In some embodiments, the WT1 peptide is a WT1126-134 peptide having an amino acid sequence of RMFPNAPYL (SEQ ID NO: 241). See, e.g., NCBI Reference Sequences KAI2559287.1 and 3HPJ_C, which are each herein incorporated by reference in their entirety. The peptide can be a peptide comprising about 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 25, 30, 35, 40, 50, 60, 70, 80, 90, or 100 amino acid residues of the MAGEA3 and/or WT1 protein. In some embodiments, the peptide comprises 9 amino acid residues. The substitution may be at any position along the length of the peptide. For example, it may be located in the C terminal third of the peptide, the central third of the peptide, or the N terminal third of the peptide.
Attorney Docket #: 250298.000961 In some embodiments, the target peptide (e.g., MAGEA3168-176 target peptide or WT1126- 134 target peptide) is 5-40 amino acids in length, or 5-33 amino acids in length, or 5-30 amino acids in length, or 5-23 amino acids in length, 5-20 amino acids in length, or 5-17 amino acids in length, or 5-14 amino acids in length, or 5-12 amino acids in length, or 5-11 amino acids in length, or 5- 10 amino acids in length, or 6-40 amino acids in length, or 6-33 amino acids in length, or 6-30 amino acids in length, or 6-23 amino acids in length, or 6-20 amino acids in length, or 6-17 amino acids in length, or 6-14 amino acids in length, or 6-12 amino acids in length, or 6-11 amino acids in length, or 6-10 amino acids in length, or 7-40 amino acids in length, or 7-33 amino acids in length, or 7-30 amino acids in length, or 7-23 amino acids in length, or 7-20 amino acids in length, or 7-17 amino acids in length, or 7-14 amino acids in length, or 7-12 amino acids in length, or 7- 11 amino acids in length, or 7-10 amino acids in length, or 8-40 amino acids in length, or 8-33 amino acids in length, or 8-30 amino acids in length, or 8-23 amino acids in length, or 8-20 amino acids in length, or 8-17 amino acids in length, or 8-14 amino acids in length, or 8-12 amino acids in length, or 8-11 amino acids in length, or 8-10 amino acids in length, or 9-40 amino acids in length, or 9-33 amino acids in length, 9-30 amino acids in length, or 9-23 amino acids in length, or 9-20 amino acids in length, or 9-17 amino acids in length, or 9-14 amino acids in length, or 9- 12 amino acids in length, or 9-11 amino acids in length, or 9-10 amino acids in length, or 10-40 amino acids in length, or 10-33 amino acids in length, or 10-30 amino acids in length, or 10-23 amino acids in length, or 10-20 amino acids in length, or 10-17 amino acids in length, or 10-14 amino acids in length, or 10-12 amino acids in length, or 10-11 amino acids in length, or 11-40 amino acids in length, or 11-33 amino acids in length, or 11-30 amino acids in length, or 11-23 amino acids in length, or 11-20 amino acids in length, or 11-17 amino acids in length, or 11-14 amino acids in length, or 11-12 amino acids in length, or 12-40 amino acids in length, or 12-33 amino acids in length, or 12-30 amino acids in length, or 12-23 amino acids in length, or 12-20 amino acids in length, or 12-17 amino acids in length, or 12-14 amino acids in length, or 40 amino acids in length, or 39 amino acids in length, or 38 amino acids in length, or 37 amino acids in length, or 36 amino acids in length, or 35 amino acids in length, or 34 amino acids in length, or 33 amino acids in length, or 32 amino acids in length, or 31 amino acids in length, or 30 amino acids in length, or 29 amino acids in length, or 28 amino acids in length, or 27 amino acids in length, or 26 amino acids in length, or 25 amino acids in length, or 24 amino acids in length, or 23 amino acids in length, or 22 amino acids in length, or 21 amino acids in length, or 20 amino acids in
Attorney Docket #: 250298.000961 length, or 19 amino acids in length, or 18 amino acids in length, or 17 amino acids in length, or 16 amino acids in length, or 15 amino acids in length, or 14 amino acids in length, or 13 amino acids in length, or 12 amino acids in length, or 11 amino acids in length, or 10 amino acids in length, or 9 amino acids in length, or 8 amino acids in length, or 7 amino acids in length, or 6 amino acids in length, or 5 amino acids in length. In some embodiments, the target peptide (e.g., e.g., MAGEA3168-176 target peptide or WT1126-134 target peptide) is 5-30 amino acids in length, or 8-30 amino acids in length, or 8-20 amino acids in length, or 8-23 amino acids in length, or 8-17 amino acids in length, or 8-14 amino acids in length, or 8-12 amino acids in length, or 8-11 amino acids in length, or 8-10 amino acids in length, or 9-30 amino acids in length, or 9-20 amino acids in length, or 9-23 amino acids in length, or 9-17 amino acids in length, or 9-14 amino acids in length, or 9-12 amino acids in length, or 9-11 amino acids in length, or 9-10 amino acids in length, or 12-30 amino acids in length, or 12-23 amino acids in length, or 12-20 amino acids in length, or 12-17 amino acids in length, or 12-14 amino acids in length, or 12 amino acids in length, or 10 amino acids in length, or 9 amino acids in length. In various embodiments, a peptide library of the present disclosure may comprise one or more off-target peptides identified for MAGEA3168-176 target EVDPIGHLY (SEQ ID NO: 29) and/or WT1126-134 target RMFPNAPYL (SEQ ID NO: 241) as described herein. In some embodiments, off-target peptides for MAGEA3168-176 target EVDPIGHLY (SEQ ID NO: 29) include those listed in Tables 2-4 herein (See Example 1). Tables 2-4 further include, e.g., the DoS (i.e., the degree of sequence similarity of the off-target peptide with the target peptide considering all amino acid positions); a ranked order as determined by a structural similarity metric (RMSD); the predicted binding affinity of each off-target peptide represented by the half maximal inhibitory concentration (IC50) value and a binding affinity percentile rank value; a description of the off- target gene corresponding to each of the off-target peptides; the mRNA level (TMP from GTEx) in the normal tissue with the highest expression for each off-target gene; as well as a description of which peptides were present in the internal immunopeptidomics database of MHC-bound peptides identified by mass spectrometry (Mass Spec) in tissue samples, as determined experimentally using exemplary methods of the disclosure. In some embodiments, off-target peptides for WT1126-134 target RMFPNAPYL (SEQ ID NO: 241) include those listed in Table 5 herein (See Example 2). Table 5 further includes, e.g., a description of the off-target gene
Attorney Docket #: 250298.000961 corresponding to each of the off-target peptides; the DoS (i.e., the degree of sequence similarity of the off-target peptide with the target peptide considering all amino acid positions); a ranked order as determined by a structural similarity metric (RMSD); as well as ranking by important residue RMSD, as determined experimentally using exemplary methods of the disclosure In some embodiments, an off-target peptide associated with MAGEA3168-176 target EVDPIGHLY (SEQ ID NO: 29) comprises an amino acid sequence of any of SEQ ID NOs: 30- 47 and 200-232, or a pharmaceutically acceptable salt thereof, or a fragment or derivative thereof. In some embodiments, an off-target peptide associated with MAGEA3168-176 target EVDPIGHLY (SEQ ID NO: 29) consists essentially of an amino acid sequence of any of SEQ ID NOs: 30-47 and 200-232. In some embodiments, an off-target peptide associated with MAGEA3168-176 target EVDPIGHLY (SEQ ID NO: 29) consists of an amino acid sequence of any one of SEQ ID NOs: 30-47 and 200-232. In some embodiments, an off-target peptide associated with MAGEA3168-176 target EVDPIGHLY (SEQ ID NO: 29) comprises an amino acid sequence of any of SEQ ID NOs: 200- 232, or a pharmaceutically acceptable salt thereof, or a fragment or derivative thereof. In some embodiments, an off-target peptide associated with MAGEA3168-176 target EVDPIGHLY (SEQ ID NO: 29) consists essentially of an amino acid sequence of any of SEQ ID NOs: 200-232. In some embodiments, an off-target peptide associated with MAGEA3168-176 target EVDPIGHLY (SEQ ID NO: 29) consists of an amino acid sequence of any one of SEQ ID NOs: 200-232. In some embodiments, an off-target peptide associated with MAGEA3168-176 target EVDPIGHLY (SEQ ID NO: 29) comprises an amino acid sequence of any of SEQ ID NOs: 200- 201, or a pharmaceutically acceptable salt thereof, or a fragment or derivative thereof. In some embodiments, an off-target peptide associated with MAGEA3168-176 target EVDPIGHLY (SEQ ID NO: 29) consists essentially of an amino acid sequence of any of SEQ ID NOs: 200-201. In some embodiments, an off-target peptide associated with MAGEA3168-176 target EVDPIGHLY (SEQ ID NO: 29) consists of an amino acid sequence of any one of SEQ ID NOs: 200-201. In some embodiments, an off-target peptide associated with MAGEA3168-176 target EVDPIGHLY (SEQ ID NO: 29) comprises an amino acid sequence of any of SEQ ID NOs: 202- 232, or a pharmaceutically acceptable salt thereof, or a fragment or derivative thereof. In some embodiments, an off-target peptide associated with MAGEA3168-176 target EVDPIGHLY (SEQ ID NO: 29) consists essentially of an amino acid sequence of any of SEQ ID NOs: 202-232. In some
Attorney Docket #: 250298.000961 embodiments, an off-target peptide associated with MAGEA3168-176 target EVDPIGHLY (SEQ ID NO: 29) consists of an amino acid sequence of any one of SEQ ID NOs: 202-232. In some embodiments, an off-target peptide associated with MAGEA3168-176 target EVDPIGHLY (SEQ ID NO: 29) comprises an amino acid sequence of any of SEQ ID NOs: 202- 230, or a pharmaceutically acceptable salt thereof, or a fragment or derivative thereof. In some embodiments, an off-target peptide associated with MAGEA3168-176 target EVDPIGHLY (SEQ ID NO: 29) consists essentially of an amino acid sequence of any of SEQ ID NOs: 202-230. In some embodiments, an off-target peptide associated with MAGEA3168-176 target EVDPIGHLY (SEQ ID NO: 29) consists of an amino acid sequence of any one of SEQ ID NOs: 202-230. In some embodiments, an off-target peptide associated with MAGEA3168-176 target EVDPIGHLY (SEQ ID NO: 29) comprises an amino acid sequence of any of SEQ ID NOs: 209, 231, and 232, or a pharmaceutically acceptable salt thereof, or a fragment or derivative thereof. In some embodiments, an off-target peptide associated with MAGEA3168-176 target EVDPIGHLY (SEQ ID NO: 29) consists essentially of an amino acid sequence of any of SEQ ID NOs: 209, 231, and 232. In some embodiments, an off-target peptide associated with MAGEA3168-176 target EVDPIGHLY (SEQ ID NO: 29) consists of an amino acid sequence of any one of SEQ ID NOs: 209, 231, and 232. In some embodiments, an off-target peptide associated with WT1126-134 target RMFPNAPYL (SEQ ID NO: 241) comprises an amino acid sequence of any of SEQ ID NOs 233- 240, or a pharmaceutically acceptable salt thereof, or a fragment or derivative thereof. In some embodiments, an off-target peptide associated with WT1126-134 target RMFPNAPYL (SEQ ID NO: 241) consists essentially of an amino acid sequence of any of SEQ ID NOs: 233-240. In some embodiments, an off-target peptide associated with WT1126-134 target RMFPNAPYL (SEQ ID NO: 241) consists of an amino acid sequence of any one of SEQ ID NOs: 233-240. In various embodiments, a peptide library of one or more off-target peptides for an identified target peptide may further comprise the target peptide itself (e.g., MAGEA3168-176 target EVDPIGHLY (SEQ ID NO: 29) or WT1126-134 target RMFPNAPYL (SEQ ID NO: 241)). In some embodiments, the target peptide comprises an amino acid sequence of SEQ ID NO: 29 or 241, or a pharmaceutically acceptable salt thereof, or a fragment or derivative thereof. In some embodiments, the target peptide consists essentially of an amino acid sequence of SEQ ID NO: 29 or 241. In some embodiments, the target peptide consists of an amino acid sequence of SEQ ID
Attorney Docket #: 250298.000961 NO: 29 or 241. In various embodiments, a peptide library of one or more off-target peptides for an identified target peptide, optionally including the target peptide itself (e.g., MAGEA3168-176 target EVDPIGHLY (SEQ ID NO: 29) and/or WT1126-134 target RMFPNAPYL (SEQ ID NO: 241)) may further include other variants of the target peptide, such as a wild-type/non-mutated version of the target peptide. The target peptide and/or wild-type version of the target peptide may be used, for example, in comparison studies with the one or more off-target peptides, such as to assess antigen-recognition molecule cross-reactivity with off-target peptides. In some embodiments, an off-target peptide associated with MAGEA3168-176 target EVDPIGHLY (SEQ ID NO: 29) may be cross-reactive to an antigen-recognition molecule targeting MAGEA3168-176 such as an antibody or TCR, e.g., a MAGEA3 TCR. In some embodiments, an antigen-recognition molecule described herein, such as an antibody or TCR, may react to cells that present MAGEA3168-176 target EVDPIGHLY (SEQ ID NO: 29) in complex with HLA-A*01:01 on a cell surface. In some embodiments, an antigen-recognition molecule, such as an antibody or TCR, may be cross-reactive with an off-target peptide associated with MAGEA3168- 176 target EVDPIGHLY (SEQ ID NO: 29). In some embodiments, an off-target peptide associated with WT1126-134 target RMFPNAPYL (SEQ ID NO: 241) may be cross-reactive to an antigen-recognition molecule targeting WT1126-134 such as an antibody or TCR, e.g., a WT1 TCR. In some embodiments, an antigen-recognition molecule, such as an antibody or TCR, may react to cells that present WT1126- 134 target RMFPNAPYL (SEQ ID NO: 241) in complex with HLA-A*02:01 on a cell surface. In some embodiments, an antigen-recognition molecule, such as an antibody or TCR, may be cross- reactive with an off-target peptide associated with WT1126-134 target RMFPNAPYL (SEQ ID NO: 241). In specific embodiments, the antigen-recognition molecule (e.g., WT1 TCR) is cross- reactive with ILF2127-135 off-target KILPTLEAV (SEQ ID NO: 233) (ILF2 (Interleukin Enhancer Binding Factor 2) gene); RBM4B59-67 off-target KLHGVNIN (SEQ ID NO: 234) (RBM4B (RNA Binding Motif Protein 4B) gene); SF3B478-86 off-target KLYGKPIRV (SEQ ID NO: 235) (SF3B4 (Splicing Factor 3b Subunit 4) gene); SHC1469-477 off-target RVPPPPQSV (SEQ ID NO: 236) (SHC (Src Homology 2 Domain-Containing) Transforming Protein 1) gene); ARHGEF26863-871 off-target RLLGLETNV (SEQ ID NO: 237) (ARHGEF26 (Rho Guanine Nucleotide Exchange Factor 26) gene); CRYL1130-138 off-target KLFAGLVHV (SEQ ID NO: 238) (CRYL1 (Crystallin Lambda 1) gene); CYP2C859-67 off-target KVYGPVFTV (SEQ ID NO: 239) (CYP2C8
Attorney Docket #: 250298.000961 (Cytochrome P450 Family 2 Subfamily C Member 8) gene); and/or USP9Y1674-1682 off-target (USP9Y (Ubiquitin Specific Peptidase 9 Y-Linked) gene). In some embodiments, the off-target peptide associated with MAGEA3168-176 target EVDPIGHLY (SEQ ID NO: 29) and/or WT1126-134 target RMFPNAPYL (SEQ ID NO: 241) has 9 amino acids. In some embodiments, the off-target peptide associated with MAGEA3168-176 target EVDPIGHLY (SEQ ID NO: 29) and/or WT1126-134 target RMFPNAPYL (SEQ ID NO: 241) can have a similar conformation to MAGEA3168-176 target EVDPIGHLY (SEQ ID NO: 29) and/or WT1126-134 target RMFPNAPYL (SEQ ID NO: 241), for example, at TCR interacting residue positions determined to be important for TCR interaction (e.g., backbone residues at positions 4, 5, 6, and 8). In some embodiments, off-target peptides of a MAGEA3168-176 target EVDPIGHLY (SEQ ID NO: 29)-HLA-A*01:01 complex can be ranked according to root mean square deviation (RMSD) of the respective off-target peptide backbone conformation relative to the target peptide backbone conformation. The off-target peptides can be sorted by median RMSD value, e.g., for the 5, 10, 15, 20, 25, 30, 40, 50, or 100 refined lowest energy MHC-off-target models. A lower RMSD value can indicate a higher degree of structural similarity. Non-limiting examples of off- target peptides having a high degree of structural similarity relative to the target peptide include off-target peptides having the amino acid sequence of SEQ ID NOs: 30-47 and 200-232. In some embodiments, an off-target peptide may be non-reactive to a TCR (e.g., a MAGEA3 TCR) as determined by exemplary method described herein. Such off-target peptide may have a dissimilar conformation to the MAGEA3168-176 target EVDPIGHLY (SEQ ID NO: 29). In some embodiments, off-target peptides of a WT1126-134 target RMFPNAPYL (SEQ ID NO: 241)-HLA-A*02:01 complex can be ranked according to root mean square deviation (RMSD) of the respective off-target peptide backbone conformation relative to the target peptide backbone conformation. The off-target peptides can be sorted by median RMSD value, e.g., for the 5, 10, 15, 20, 25, 30.40, 50, or 100 refined lowest energy MHC-off-target models. A lower RMSD value can indicate a higher degree of structural similarity. Non-limiting examples of off-target peptides having a high degree of structural similarity relative to the target peptide include off-target peptides having the amino acid sequence of SEQ ID NOs: 233-240. In some embodiments, an off-target peptide may be non-reactive to a TCR (e.g., a WT1 TCR) as determined by exemplary method
Attorney Docket #: 250298.000961 described herein. Such off-target peptide may have a dissimilar conformation to the WT1126-134 target RMFPNAPYL (SEQ ID NO: 241). In some embodiments, an off-target peptide associated with MAGEA3168-176 target EVDPIGHLY (SEQ ID NO: 29) comprises an amino acid sequence of any of SEQ ID NOs: 30- 47 and 200-232, or a pharmaceutically acceptable salt thereof, or a fragment or derivative thereof. In some embodiments, an off-target peptide associated with MAGEA3168-176 target EVDPIGHLY (SEQ ID NO: 29) consists essentially of an amino acid sequence of any of SEQ ID NOs: 30-47 and 200-232. In some embodiments, an off-target peptide associated with MAGEA3168-176 target EVDPIGHLY (SEQ ID NO: 29) consists of an amino acid sequence of any one of SEQ ID NOs: 30-47 and 200-232. In some embodiments, an off-target peptide associated with MAGEA3168-176 target EVDPIGHLY (SEQ ID NO: 29) comprises an amino acid sequence of any of SEQ ID NOs: 200- 232, or a pharmaceutically acceptable salt thereof, or a fragment or derivative thereof. In some embodiments, an off-target peptide associated with MAGEA3168-176 target EVDPIGHLY (SEQ ID NO: 29) consists essentially of an amino acid sequence of any of SEQ ID NOs: 200-232. In some embodiments, an off-target peptide associated with MAGEA3168-176 target EVDPIGHLY (SEQ ID NO: 29) consists of an amino acid sequence of any one of SEQ ID NOs: 200-232. In some embodiments, an off-target peptide associated with MAGEA3168-176 target EVDPIGHLY (SEQ ID NO: 29) comprises an amino acid sequence of any of SEQ ID NOs: 200- 201, or a pharmaceutically acceptable salt thereof, or a fragment or derivative thereof. In some embodiments, an off-target peptide associated with MAGEA3168-176 target EVDPIGHLY (SEQ ID NO: 29) consists essentially of an amino acid sequence of any of SEQ ID NOs: 200-201. In some embodiments, an off-target peptide associated with MAGEA3168-176 target EVDPIGHLY (SEQ ID NO: 29) consists of an amino acid sequence of any one of SEQ ID NOs: 200-201. In some embodiments, an off-target peptide associated with MAGEA3168-176 target EVDPIGHLY (SEQ ID NO: 29) comprises an amino acid sequence of any of SEQ ID NOs: 202- 232, or a pharmaceutically acceptable salt thereof, or a fragment or derivative thereof. In some embodiments, an off-target peptide associated with MAGEA3168-176 target EVDPIGHLY (SEQ ID NO: 29) consists essentially of an amino acid sequence of any of SEQ ID NOs: 202-232. In some embodiments, an off-target peptide associated with MAGEA3168-176 target EVDPIGHLY (SEQ ID NO: 29) consists of an amino acid sequence of any one of SEQ ID NOs: 202-232.
Attorney Docket #: 250298.000961 In some embodiments, an off-target peptide associated with MAGEA3168-176 target EVDPIGHLY (SEQ ID NO: 29) comprises an amino acid sequence of any of SEQ ID NOs: 202- 230, or a pharmaceutically acceptable salt thereof, or a fragment or derivative thereof. In some embodiments, an off-target peptide associated with MAGEA3168-176 target EVDPIGHLY (SEQ ID NO: 29) consists essentially of an amino acid sequence of any of SEQ ID NOs: 202-230. In some embodiments, an off-target peptide associated with MAGEA3168-176 target EVDPIGHLY (SEQ ID NO: 29) consists of an amino acid sequence of any one of SEQ ID NOs: 202-230. In some embodiments, an off-target peptide associated with MAGEA3168-176 target EVDPIGHLY (SEQ ID NO: 29) comprises an amino acid sequence of any of SEQ ID NOs: 209, 231, and 232, or a pharmaceutically acceptable salt thereof, or a fragment or derivative thereof. In some embodiments, an off-target peptide associated with MAGEA3168-176 target EVDPIGHLY (SEQ ID NO: 29) consists essentially of an amino acid sequence of any of SEQ ID NOs: 209, 231, and 232. In some embodiments, an off-target peptide associated with MAGEA3168-176 target EVDPIGHLY (SEQ ID NO: 29) consists of an amino acid sequence of any one of SEQ ID NOs: 209, 231, and 232. In some embodiments, an off-target peptide associated with WT1126-134 target RMFPNAPYL (SEQ ID NO: 241) comprises an amino acid sequence of any of SEQ ID NOs: 233- 240, or a pharmaceutically acceptable salt thereof, or a fragment or derivative thereof. In some embodiments, an off-target peptide associated with WT1126-134 target RMFPNAPYL (SEQ ID NO: 241) consists essentially of an amino acid sequence of any of SEQ ID NOs: 233-240. In some embodiments, an off-target peptide associated with WT1126-134 target RMFPNAPYL (SEQ ID NO: 241) consists of an amino acid sequence of any one of SEQ ID NOs: 233-240. In some embodiments, a peptide library comprising off-target peptides described herein may comprise two or more of any of various off-target peptides described herein. As a non-limiting example, a peptide library comprising off-target peptides may comprise two or more peptides each selected from the amino acid sequences of SEQ ID NOs: 30-47 and 200-232, or a pharmaceutically acceptable salt thereof, or a fragment or derivative. As another non-limiting example, a peptide library comprising off-target peptides may comprise two or more peptides each selected from the amino acid sequences of SEQ ID NOs: 200-232, or a pharmaceutically acceptable salt thereof, or a fragment or derivative. As yet another non-limiting example, a peptide library comprising off- target peptides may comprise two or more peptides each selected from the amino acid sequences
Attorney Docket #: 250298.000961 of SEQ ID NOs: 200-201, or a pharmaceutically acceptable salt thereof, or a fragment or derivative. In some embodiments, a peptide library comprising off-target peptides may comprise two or more peptides each selected from the amino acid sequences of SEQ ID NOs: 202-232, or a pharmaceutically acceptable salt thereof, or a fragment or derivative. In some embodiments, a peptide library comprising off-target peptides may comprise two or more peptides each selected from the amino acid sequences of SEQ ID NOs: 202-230, or a pharmaceutically acceptable salt thereof, or a fragment or derivative. In some embodiments, a peptide library comprising off-target peptides may comprise two or more peptides each selected from the amino acid sequences of SEQ ID NOs: 209, 231, and 232, or a pharmaceutically acceptable salt thereof, or a fragment or derivative. In some embodiments, a peptide library comprising off-target peptides may comprise two or more peptides each selected from the amino acid sequences of SEQ ID NOs: 233-240, or a pharmaceutically acceptable salt thereof, or a fragment or derivative. In a specific embodiment, the isolated peptide can be, e.g., 8-12 amino acids in length. In a specific embodiment, the isolated peptide can be 9-12 amino acids in length. In a specific embodiment, the off-target peptide, or fragment or derivative thereof, can be, e.g., 8-14 amino acids in length, for example 9 amino acids in length. In another specific embodiment, the off-target peptide, or fragment or derivative thereof, can be, e.g., 12-20 amino acids in length. In some embodiments, the peptides within a peptide library described herein can each present in a complex with a major histocompatibility complex (MHC) molecule described herein. In certain embodiments, the MHC molecule is a class I MHC molecule such as, but not limited to, a class I human leukocyte antigen (HLA) molecule. The class I human leukocyte antigen (HLA) molecule can be an HLA-A molecule. In some embodiments, the HLA-A molecule is an HLA-A2 molecule. In some specific embodiments, the HLA-A2 molecule is an HLA-A*02:01 molecule. In some embodiments, the HLA-A molecule is an HLA-A1 molecule. In some specific embodiments, the HLA-A1 molecule is an HLA-A*01:01molecule. In some embodiments, a peptide within an MHC-peptide complex described herein, e.g., a MAGEA3168-176 peptide complex, and/or an MHC-off-target peptide complex described herein, e.g., an MHC-off-target peptide complex comprising an off-target peptide associated with MAGEA3168-176 target EVDPIGHLY (SEQ ID NO: 29), can be covalently bound to an MHC described herein.
Attorney Docket #: 250298.000961 In some embodiments, a peptide within an MHC-peptide complex described herein, e.g., a MAGEA3168-176 peptide complex , and/or an MHC-off-target peptide complex described herein, e.g., an MHC-off-target peptide complex comprising an off-target peptide associated with MAGEA3168-176 target EVDPIGHLY (SEQ ID NO: 29), can be non-covalently bound to an MHC described herein. In some embodiments, a peptide within an MHC-peptide complex described herein, e.g., a WT1126-134 peptide complex, and/or an MHC-off-target peptide complex described herein, e.g., an MHC-off-target peptide complex comprising an off-target peptide associated with WT1126-134 target RMFPNAPYL (SEQ ID NO: 241), can be covalently bound to an MHC described herein. In some embodiments, a peptide within an MHC-peptide complex described herein, e.g., a WT1126-134 peptide complex , and/or an MHC-off-target peptide complex described herein, e.g., an MHC-off-target peptide complex comprising an off-target peptide associated with WT1126-134 target RMFPNAPYL (SEQ ID NO: 241), can be non-covalently bound to an MHC described herein. In various embodiments, the present disclosure provides a library comprising two or more proteins or fragments thereof each comprising one or more off-target peptides described herein. In some embodiments, each off-target peptide can be selected from the amino acid sequences of SEQ ID NOs: 30-47 and 200-232. In some embodiments, each off-target peptide can be selected from the amino acid sequences of SEQ ID NOs: 200-232. In some embodiments, each off-target peptide can be selected from the amino acid sequences of SEQ ID NOs: 200-201. In some embodiments, each off-target peptide can be selected from the amino acid sequences of SEQ ID NOs: 202-232. In some embodiments, each off-target peptide can be selected from the amino acid sequences of SEQ ID NOs: 202-230. In some embodiments, each off-target peptide can be selected from the amino acid sequences of SEQ ID NOs: 209, 231, and 232. In some embodiments, each off-target peptide can be selected from the amino acid sequences of SEQ ID NOs: 233-240. Peptide libraries of present disclosure may comprise any number of a plurality of peptides as desired. For example, a peptide library of the present disclosure may comprise at least 2 peptides, at least 3 peptides, at least 4 peptides, at least 5 peptides, at least 6 peptides, at least 7 peptides, at least 8 peptides, at least 9 peptides, at least 10 peptides, at least 20 peptides, at least 30 peptides, at least 40 peptides, at least 50 peptides, or about 2-5 peptides, about 2-10 peptides, about 5-15 peptides, about 10-20 peptides, about 10-30 peptides, about 12-25 peptides, about 20-
Attorney Docket #: 250298.000961 30 peptides, about 25-50 peptides, about 40-80 peptides, about 50-100 peptides, about 60-120 peptides, about 70-140 peptides, about 80-160 peptides, about 90-180 peptides, about 100-200 peptides, about 110-220 peptides, about 120-240 peptides, about 130-260 peptides, about 140-280 peptides, about 150-300 peptides, about 160-320 peptides, about 170-340 peptides, about 180-360 peptides, about 190-380 peptides, about 200-400 peptides, about 210-420 peptides, about 220-440 peptides, about 230-460 peptides, about 240-480 peptides, about 250-500 peptides, about 260-520 peptides, about 270-540 peptides, about 280-560 peptides, about 290-580 peptides, about 300-600 peptides, about 310-620 peptides, about 320-640 peptides, about 330-660 peptides, about 340-680 peptides, about 350-700 peptides, about 360-720 peptides, about 370-740 peptides, about 380-760 peptides, about 390-780 peptides, about 400-800 peptides, about 410-820 peptides, about 420-840 peptides, about 430-860 peptides, about 440-880 peptides, about 450-900 peptides, about 460-920 peptides, about 470-940 peptides, about 480-960 peptides, about 490-980 peptides, about 500- 1000 peptides, etc. In a further aspect, provided herein are databases including computational representations of the off-target peptides identified using the methods described herein. The database can include information for each of the off-target peptides represented in any one of the exemplary libraries disclosed hereinabove. Accordingly, the databases may include computational representations of the target peptides associated with the off-target peptides as well. The computer-readable representations of the peptides in the database can include a peptide sequence or a computer model of the peptide. A peptide of the disclosure may be synthetically produced or produced by hydrolysis. Synthetically produced peptides can include randomly generated peptides, specifically designed peptides, and peptides where at least some of the amino acid positions are conserved among several peptides and the remaining positions are random. Alternatively, a peptide of the present disclosure may be produced by expression in a heterologous host cell. In some embodiments, peptides of the disclosure can be synthesized by e.g., solid phase synthesis. As such, the peptides may be immobilized, for example to a solid support such as a bead. Peptides of the disclosure may be synthesized by the Fmoc-polyamide mode of solid-phase peptide synthesis. Temporary N-amino group protection is afforded by the 9- fluorenylmethyloxycarbonyl (Fmoc) group. Repetitive cleavage of this highly base-labile protecting group is done using 20% piperidine in N, N-dimethylformamide. Side-chain
Attorney Docket #: 250298.000961 functionalities may be protected as their butyl ethers (in the case of serine threonine and tyrosine), butyl esters (in the case of glutamic acid and aspartic acid), butyloxycarbonyl derivative (in the case of lysine and histidine), trityl derivative (in the case of cysteine) and 4-methoxy-2,3,6- trimethylbenzenesulphonyl derivative (in the case of arginine). Where glutamine or asparagine are C-terminal residues, use is made of the 4,4′-dimethoxybenzhydryl group for protection of the side chain amido functionalities. The solid-phase support is based on a polydimethyl-acrylamide polymer constituted from the three monomers dimethylacrylamide (backbone-monomer), bisacryloylethylene diamine (cross linker) and acryloylsarcosine methyl ester (functionalizing agent). The peptide-to-resin cleavable linked agent used is the acid-labile 4-hydroxymethyl- phenoxyacetic acid derivative. All amino acid derivatives are added as their preformed symmetrical anhydride derivatives except for asparagine and glutamine, which are added using a reversed N, N-dicyclohexyl-carbodiimide/1-hydroxybenzotriazole mediated coupling procedure. All coupling and deprotection reactions are monitored using ninhydrin, trinitrobenzene sulphonic acid or isotin test procedures. Upon completion of synthesis, peptides are cleaved from the resin support with concomitant removal of side-chain protecting groups by treatment with 95% trifluoroacetic acid containing a 50% scavenger mix. Scavengers commonly used include ethanedithiol, phenol, anisole and water, the exact choice depending on the constituent amino acids of the peptide being synthesized. Also, a combination of solid phase and solution phase methodologies for the synthesis of peptides is possible. Trifluoroacetic acid is removed by evaporation in vacuo, with subsequent trituration with diethyl ether affording the crude peptide. Any scavengers present are removed by a simple extraction procedure which on lyophilization of the aqueous phase affords the crude peptide free of scavengers. Purification may be performed by techniques such as re-crystallization, ion-exchange chromatography, size exclusion chromatography, hydrophobic interaction chromatography and reverse-phase high performance liquid chromatography using e.g., acetonitrile/water gradient separation, or a combination thereof. Peptides may be analyzed using thin layer chromatography, electrophoresis, in particular capillary electrophoresis, solid phase extraction (CSPE), reverse-phase high performance liquid chromatography, amino-acid analysis after acid hydrolysis and by fast atom bombardment (FAB) mass spectrometric analysis, as well as MALDI and ESI-Q-TOF mass spectrometric analysis.
Attorney Docket #: 250298.000961 Alternatively, the peptide may be produced by recombinant expression in a heterologous host cell. Such methods typically involve the use of a vector comprising a nucleic acid sequence encoding the peptide to be expressed, to express the polypeptide in vivo; for example, in bacteria, yeast, insect or mammalian cells. In further embodiments, in vitro cell-free systems may be used. The peptides may be isolated and/or may be provided in substantially pure form. For example, they may be provided in a form which is substantially free of other peptides or proteins. A peptide, or fragment or derivative thereof, disclosed herein (e.g., an isolated peptide, an off-target peptide, a target peptide, a peptide within a peptide library, a peptide within an MHC- peptide complex, etc.) may vary in length. In some embodiments, the peptides of the disclosure may be 5-40 amino acids in length. In some embodiments, the peptides of the disclosure may be 5-9 amino acids in length. In some embodiments, the peptides of the disclosure may be 5-10 amino acids in length. In some embodiments, the peptides of the disclosure may be 5-11 amino acids in length. In some embodiments, the peptides of the disclosure may be 5-12 amino acids in length. In some embodiments, the peptides of the disclosure may be 5-13 amino acids in length. In some embodiments, the peptides of the disclosure may be 5-14 amino acids in length. In some embodiments, the peptides of the disclosure may be 5-15 amino acids in length. In some embodiments, the peptides of the disclosure may be 5-16 amino acids in length. In some embodiments, the peptides of the disclosure may be 5-17 amino acids in length. In some embodiments, the peptides of the disclosure may be 5-18 amino acids in length. In some embodiments, the peptides of the disclosure may be 5-19 amino acids in length. In some embodiments, the peptides of the disclosure may be 5-20 amino acids in length. In some embodiments, the peptides of the disclosure may be 5-21 amino acids in length. In some embodiments, the peptides of the disclosure may be 5-22 amino acids in length. In some embodiments, the peptides of the disclosure may be 5-23 amino acids in length. In some embodiments, the peptides of the disclosure may be 5-24 amino acids in length. In some embodiments, the peptides of the disclosure may be 5-25 amino acids in length. In some embodiments, the peptides of the disclosure may be 5-26 amino acids in length. In some embodiments, the peptides of the disclosure may be 5-27 amino acids in length. In some embodiments, the peptides of the disclosure may be 5-28 amino acids in length. In some
Attorney Docket #: 250298.000961 embodiments, the peptides of the disclosure may be 5-29 amino acids in length. In some embodiments, the peptides of the disclosure may be 5-30 amino acids in length. In some embodiments, the peptides of the disclosure may be 5-31 amino acids in length. In some embodiments, the peptides of the disclosure may be 5-32 amino acids in length. In some embodiments, the peptides of the disclosure may be 5-33 amino acids in length. In some embodiments, the peptides of the disclosure may be 5-34 amino acids in length. In some embodiments, the peptides of the disclosure may be 5-35 amino acids in length. In some embodiments, the peptides of the disclosure may be 5-36 amino acids in length. In some embodiments, the peptides of the disclosure may be 5-37 amino acids in length. In some embodiments, the peptides of the disclosure may be 5-38 amino acids in length. In some embodiments, the peptides of the disclosure may be 5-39 amino acids in length. In some embodiments, the peptides of the disclosure may be 5-40 amino acids in length. In various embodiments, the peptides of the disclosure may be 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, or 40 amino acids in length. In some embodiments, the peptides of the disclosure may be 6-40 amino acids in length. In some embodiments, the peptides of the disclosure may be 6-9 amino acids in length. In some embodiments, the peptides of the disclosure may be 6-10 amino acids in length. In some embodiments, the peptides of the disclosure may be 6-11 amino acids in length. In some embodiments, the peptides of the disclosure may be 6-12 amino acids in length. In some embodiments, the peptides of the disclosure may be 6-13 amino acids in length. In some embodiments, the peptides of the disclosure may be 6-14 amino acids in length. In some embodiments, the peptides of the disclosure may be 6-15 amino acids in length. In some embodiments, the peptides of the disclosure may be 6-16 amino acids in length. In some embodiments, the peptides of the disclosure may be 6-17 amino acids in length. In some embodiments, the peptides of the disclosure may be 6-18 amino acids in length. In some embodiments, the peptides of the disclosure may be 6-19 amino acids in length. In some embodiments, the peptides of the disclosure may be 6-20 amino acids in length. In some embodiments, the peptides of the disclosure may be 6-21 amino acids in length. In some embodiments, the peptides of the disclosure may be 6-22 amino acids in length. In some embodiments, the peptides of the disclosure may be 6-23 amino acids in length. In some
Attorney Docket #: 250298.000961 embodiments, the peptides of the disclosure may be 6-24 amino acids in length. In some embodiments, the peptides of the disclosure may be 6-25 amino acids in length. In some embodiments, the peptides of the disclosure may be 6-26 amino acids in length. In some embodiments, the peptides of the disclosure may be 6-27 amino acids in length. In some embodiments, the peptides of the disclosure may be 6-28 amino acids in length. In some embodiments, the peptides of the disclosure may be 6-29 amino acids in length. In some embodiments, the peptides of the disclosure may be 6-30 amino acids in length. In some embodiments, the peptides of the disclosure may be 6-31 amino acids in length. In some embodiments, the peptides of the disclosure may be 6-32 amino acids in length. In some embodiments, the peptides of the disclosure may be 6-33 amino acids in length. In some embodiments, the peptides of the disclosure may be 6-34 amino acids in length. In some embodiments, the peptides of the disclosure may be 6-35 amino acids in length. In some embodiments, the peptides of the disclosure may be 6-36 amino acids in length. In some embodiments, the peptides of the disclosure may be 6-37 amino acids in length. In some embodiments, the peptides of the disclosure may be 6-38 amino acids in length. In some embodiments, the peptides of the disclosure may be 6-39 amino acids in length. In some embodiments, the peptides of the disclosure may be 6-40 amino acids in length. In some embodiments, the peptides of the disclosure may be 7-40 amino acids in length. In some embodiments, the peptides of the disclosure may be 7-9 amino acids in length. In some embodiments, the peptides of the disclosure may be 7-10 amino acids in length. In some embodiments, the peptides of the disclosure may be 7-11 amino acids in length. In some embodiments, the peptides of the disclosure may be 7-12 amino acids in length. In some embodiments, the peptides of the disclosure may be 7-13 amino acids in length. In some embodiments, the peptides of the disclosure may be 7-14 amino acids in length. In some embodiments, the peptides of the disclosure may be 7-15 amino acids in length. In some embodiments, the peptides of the disclosure may be 7-16 amino acids in length. In some embodiments, the peptides of the disclosure may be 7-17 amino acids in length. In some embodiments, the peptides of the disclosure may be 7-18 amino acids in length. In some embodiments, the peptides of the disclosure may be 7-19 amino acids in length. In some embodiments, the peptides of the disclosure may be 7-20 amino acids in length. In some embodiments, the peptides of the disclosure may be 7-21 amino acids in length. In some
Attorney Docket #: 250298.000961 embodiments, the peptides of the disclosure may be 7-22 amino acids in length. In some embodiments, the peptides of the disclosure may be 7-23 amino acids in length. In some embodiments, the peptides of the disclosure may be 7-24 amino acids in length. In some embodiments, the peptides of the disclosure may be 7-25 amino acids in length. In some embodiments, the peptides of the disclosure may be 7-26 amino acids in length. In some embodiments, the peptides of the disclosure may be 7-27 amino acids in length. In some embodiments, the peptides of the disclosure may be 7-28 amino acids in length. In some embodiments, the peptides of the disclosure may be 7-29 amino acids in length. In some embodiments, the peptides of the disclosure may be 7-30 amino acids in length. In some embodiments, the peptides of the disclosure may be 7-31 amino acids in length. In some embodiments, the peptides of the disclosure may be 7-32 amino acids in length. In some embodiments, the peptides of the disclosure may be 7-33 amino acids in length. In some embodiments, the peptides of the disclosure may be 7-34 amino acids in length. In some embodiments, the peptides of the disclosure may be 7-35 amino acids in length. In some embodiments, the peptides of the disclosure may be 7-36 amino acids in length. In some embodiments, the peptides of the disclosure may be 7-37 amino acids in length. In some embodiments, the peptides of the disclosure may be 7-38 amino acids in length. In some embodiments, the peptides of the disclosure may be 7-39 amino acids in length. In some embodiments, the peptides of the disclosure may be 7-40 amino acids in length. In some embodiments, the peptides of the disclosure may be 8-40 amino acids in length. In some embodiments, the peptides of the disclosure may be 8-9 amino acids in length. In some embodiments, the peptides of the disclosure may be 8-10 amino acids in length. In some embodiments, the peptides of the disclosure may be 8-11 amino acids in length. In some embodiments, the peptides of the disclosure may be 8-12 amino acids in length. In some embodiments, the peptides of the disclosure may be 8-13 amino acids in length. In some embodiments, the peptides of the disclosure may be 8-14 amino acids in length. In some embodiments, the peptides of the disclosure may be 8-15 amino acids in length. In some embodiments, the peptides of the disclosure may be 8-16 amino acids in length. In some embodiments, the peptides of the disclosure may be 8-17 amino acids in length. In some embodiments, the peptides of the disclosure may be 8-18 amino acids in length. In some embodiments, the peptides of the disclosure may be 8-19 amino acids in length. In some
Attorney Docket #: 250298.000961 embodiments, the peptides of the disclosure may be 8-20 amino acids in length. In some embodiments, the peptides of the disclosure may be 8-21 amino acids in length. In some embodiments, the peptides of the disclosure may be 8-22 amino acids in length. In some embodiments, the peptides of the disclosure may be 8-23 amino acids in length. In some embodiments, the peptides of the disclosure may be 8-24 amino acids in length. In some embodiments, the peptides of the disclosure may be 8-25 amino acids in length. In some embodiments, the peptides of the disclosure may be 8-26 amino acids in length. In some embodiments, the peptides of the disclosure may be 8-27 amino acids in length. In some embodiments, the peptides of the disclosure may be 8-28 amino acids in length. In some embodiments, the peptides of the disclosure may be 8-29 amino acids in length. In some embodiments, the peptides of the disclosure may be 8-30 amino acids in length. In some embodiments, the peptides of the disclosure may be 8-31 amino acids in length. In some embodiments, the peptides of the disclosure may be 8-32 amino acids in length. In some embodiments, the peptides of the disclosure may be 8-33 amino acids in length. In some embodiments, the peptides of the disclosure may be 8-34 amino acids in length. In some embodiments, the peptides of the disclosure may be 8-35 amino acids in length. In some embodiments, the peptides of the disclosure may be 8-36 amino acids in length. In some embodiments, the peptides of the disclosure may be 8-37 amino acids in length. In some embodiments, the peptides of the disclosure may be 8-38 amino acids in length. In some embodiments, the peptides of the disclosure may be 8-39 amino acids in length. In some embodiments, the peptides of the disclosure may be 8-40 amino acids in length. In some embodiments, the peptides of the disclosure may be 9-40 amino acids in length. In some embodiments, the peptides of the disclosure may be 9-10 amino acids in length. In some embodiments, the peptides of the disclosure may be 9-11 amino acids in length. In some embodiments, the peptides of the disclosure may be 9-12 amino acids in length. In some embodiments, the peptides of the disclosure may be 9-13 amino acids in length. In some embodiments, the peptides of the disclosure may be 9-14 amino acids in length. In some embodiments, the peptides of the disclosure may be 9-15 amino acids in length. In some embodiments, the peptides of the disclosure may be 9-16 amino acids in length. In some embodiments, the peptides of the disclosure may be 9-17 amino acids in length. In some embodiments, the peptides of the disclosure may be 9-18 amino acids in length. In some
Attorney Docket #: 250298.000961 embodiments, the peptides of the disclosure may be 9-19 amino acids in length. In some embodiments, the peptides of the disclosure may be 9-20 amino acids in length. In some embodiments, the peptides of the disclosure may be 9-21 amino acids in length. In some embodiments, the peptides of the disclosure may be 9-22 amino acids in length. In some embodiments, the peptides of the disclosure may be 9-23 amino acids in length. In some embodiments, the peptides of the disclosure may be 9-24 amino acids in length. In some embodiments, the peptides of the disclosure may be 9-25 amino acids in length. In some embodiments, the peptides of the disclosure may be 9-26 amino acids in length. In some embodiments, the peptides of the disclosure may be 9-27 amino acids in length. In some embodiments, the peptides of the disclosure may be 9-28 amino acids in length. In some embodiments, the peptides of the disclosure may be 9-29 amino acids in length. In some embodiments, the peptides of the disclosure may be 9-30 amino acids in length. In some embodiments, the peptides of the disclosure may be 9-31 amino acids in length. In some embodiments, the peptides of the disclosure may be 9-32 amino acids in length. In some embodiments, the peptides of the disclosure may be 9-33 amino acids in length. In some embodiments, the peptides of the disclosure may be 9-34 amino acids in length. In some embodiments, the peptides of the disclosure may be 9-35 amino acids in length. In some embodiments, the peptides of the disclosure may be 9-36 amino acids in length. In some embodiments, the peptides of the disclosure may be 9-37 amino acids in length. In some embodiments, the peptides of the disclosure may be 9-38 amino acids in length. In some embodiments, the peptides of the disclosure may be 9-39 amino acids in length. In some embodiments, the peptides of the disclosure may be 9-40 amino acids in length. In some embodiments, the peptides of the disclosure may be 10-40 amino acids in length. In some embodiments, the peptides of the disclosure may be 10-11 amino acids in length. In some embodiments, the peptides of the disclosure may be 10-12 amino acids in length. In some embodiments, the peptides of the disclosure may be 10-13 amino acids in length. In some embodiments, the peptides of the disclosure may be 10-14 amino acids in length. In some embodiments, the peptides of the disclosure may be 10-15 amino acids in length. In some embodiments, the peptides of the disclosure may be 10-16 amino acids in length. In some embodiments, the peptides of the disclosure may be 10-17 amino acids in length. In some embodiments, the peptides of the disclosure may be 10-18 amino acids in length. In some
Attorney Docket #: 250298.000961 embodiments, the peptides of the disclosure may be 10-19 amino acids in length. In some embodiments, the peptides of the disclosure may be 10-20 amino acids in length. In some embodiments, the peptides of the disclosure may be 10-21 amino acids in length. In some embodiments, the peptides of the disclosure may be 10-22 amino acids in length. In some embodiments, the peptides of the disclosure may be 10-23 amino acids in length. In some embodiments, the peptides of the disclosure may be 10-24 amino acids in length. In some embodiments, the peptides of the disclosure may be 10-25 amino acids in length. In some embodiments, the peptides of the disclosure may be 10-26 amino acids in length. In some embodiments, the peptides of the disclosure may be 10-27 amino acids in length. In some embodiments, the peptides of the disclosure may be 10-28 amino acids in length. In some embodiments, the peptides of the disclosure may be 10-29 amino acids in length. In some embodiments, the peptides of the disclosure may be 10-30 amino acids in length. In some embodiments, the peptides of the disclosure may be 10-31 amino acids in length. In some embodiments, the peptides of the disclosure may be 10-32 amino acids in length. In some embodiments, the peptides of the disclosure may be 10-33 amino acids in length. In some embodiments, the peptides of the disclosure may be 10-34 amino acids in length. In some embodiments, the peptides of the disclosure may be 10-35 amino acids in length. In some embodiments, the peptides of the disclosure may be 10-36 amino acids in length. In some embodiments, the peptides of the disclosure may be 10-37 amino acids in length. In some embodiments, the peptides of the disclosure may be 10-38 amino acids in length. In some embodiments, the peptides of the disclosure may be 10-39 amino acids in length. In some embodiments, the peptides of the disclosure may be 10-40 amino acids in length. In some embodiments, the peptides of the disclosure may be 11-40 amino acids in length. In some embodiments, the peptides of the disclosure may be 11-12 amino acids in length. In some embodiments, the peptides of the disclosure may be 11-13 amino acids in length. In some embodiments, the peptides of the disclosure may be 11-14 amino acids in length. In some embodiments, the peptides of the disclosure may be 11-15 amino acids in length. In some embodiments, the peptides of the disclosure may be 11-16 amino acids in length. In some embodiments, the peptides of the disclosure may be 11-17 amino acids in length. In some embodiments, the peptides of the disclosure may be 11-18 amino acids in length. In some embodiments, the peptides of the disclosure may be 11-19 amino acids in length. In some
Attorney Docket #: 250298.000961 embodiments, the peptides of the disclosure may be 11-20 amino acids in length. In some embodiments, the peptides of the disclosure may be 11-21 amino acids in length. In some embodiments, the peptides of the disclosure may be 11-22 amino acids in length. In some embodiments, the peptides of the disclosure may be 11-23 amino acids in length. In some embodiments, the peptides of the disclosure may be 11-24 amino acids in length. In some embodiments, the peptides of the disclosure may be 11-25 amino in length. In some
embodiments, the peptides of the disclosure may be 11-26 amino acids in length. In some embodiments, the peptides of the disclosure may be 11-27 amino in length. In some
embodiments, the peptides of the disclosure may be 11-28 amino acids in length. In some embodiments, the peptides of the disclosure may be 11-29 amino in length. In some
embodiments, the peptides of the disclosure may be 11-30 amino acids in length. In some embodiments, the peptides of the disclosure may be 11-31 amino acids in length. In some embodiments, the peptides of the disclosure may be 11-32 amino acids in length. In some embodiments, the peptides of the disclosure may be 11-33 amino acids in length. In some embodiments, the peptides of the disclosure may be 11-34 amino acids in length. In some embodiments, the peptides of the disclosure may be 11-35 amino
in length. In some embodiments, the peptides of the disclosure may be 11-36 amino acids in length. In some embodiments, the peptides of the disclosure may be 11-37 amino
in length. In some embodiments, the peptides of the disclosure may be 11-38 amino acids in length. In some embodiments, the peptides of the disclosure may be 11-39 amino acids in length. In some embodiments, the peptides of the disclosure may be 11-40 amino acids in length. In some embodiments, the peptides of the disclosure may be 12-16 amino acids in length. In some embodiments, the peptides of the disclosure may be 12-40 amino acids in length. In some embodiments, the peptides of the disclosure may be 12-13 amino acids in length. In some embodiments, the peptides of the disclosure may be 12-14 amino acids in length. In some embodiments, the peptides of the disclosure may be 12-15 amino acids in length. In some embodiments, the peptides of the disclosure may be 12-16 amino acids in length. In some embodiments, the peptides of the disclosure may be 12-17 amino acids in length. In some embodiments, the peptides of the disclosure may be 12-18 amino acids in length. In some embodiments, the peptides of the disclosure may be 12-19 amino acids in length. In some embodiments, the peptides of the disclosure may be 12-20 amino acids in length. In some
Attorney Docket #: 250298.000961 embodiments, the peptides of the disclosure may be 12-21 amino acids in length. In some embodiments, the peptides of the disclosure may be 12-22 amino acids in length. In some embodiments, the peptides of the disclosure may be 12-23 amino acids in length. In some embodiments, the peptides of the disclosure may be 12-24 amino acids in length. In some embodiments, the peptides of the disclosure may be 12-25 amino acids in length. In some embodiments, the peptides of the disclosure may be 12-26 amino in length. In some
embodiments, the peptides of the disclosure may be 12-27 amino acids in length. In some embodiments, the peptides of the disclosure may be 12-28 amino in length. In some
embodiments, the peptides of the disclosure may be 12-29 amino acids in length. In some embodiments, the peptides of the disclosure may be 12-30 amino in length. In some
embodiments, the peptides of the disclosure may be 12-31 amino acids in length. In some embodiments, the peptides of the disclosure may be 12-32 amino acids in length. In some embodiments, the peptides of the disclosure may be 12-33 amino acids in length. In some embodiments, the peptides of the disclosure may be 12-34 amino acids in length. In some embodiments, the peptides of the disclosure may be 12-35 amino acids in length. In some embodiments, the peptides of the disclosure may be 12-36 amino
in length. In some embodiments, the peptides of the disclosure may be 12-37 amino acids in length. In some embodiments, the peptides of the disclosure may be 12-38 amino
in length. In some embodiments, the peptides of the disclosure may be 12-39 amino acids in length. In some embodiments, the peptides of the disclosure may be 12-40 amino acids in length. In some embodiments, the peptides of the disclosure may be 13-40 amino acids in length. In some embodiments, the peptides of the disclosure may be 13-14 amino acids in length. In some embodiments, the peptides of the disclosure may be 13-15 amino acids in length. In some embodiments, the peptides of the disclosure may be 13-16 amino acids in length. In some embodiments, the peptides of the disclosure may be 13-17 amino acids in length. In some embodiments, the peptides of the disclosure may be 13-18 amino acids in length. In some embodiments, the peptides of the disclosure may be 13-19 amino acids in length. In some embodiments, the peptides of the disclosure may be 13-20 amino acids in length. In some embodiments, the peptides of the disclosure may be 13-21 amino acids in length. In some embodiments, the peptides of the disclosure may be 13-22 amino acids in length. In some embodiments, the peptides of the disclosure may be 13-23 amino acids in length. In some
Attorney Docket #: 250298.000961 embodiments, the peptides of the disclosure may be 13-24 amino acids in length. In some embodiments, the peptides of the disclosure may be 13-25 amino acids in length. In some embodiments, the peptides of the disclosure may be 13-26 amino acids in length. In some embodiments, the peptides of the disclosure may be 13-27 amino acids in length. In some embodiments, the peptides of the disclosure may be 13-28 amino acids in length. In some embodiments, the peptides of the disclosure may be 13-29 amino in length. In some
embodiments, the peptides of the disclosure may be 13-30 amino acids in length. In some embodiments, the peptides of the disclosure may be 13-31 amino in length. In some
embodiments, the peptides of the disclosure may be 13-32 amino acids in length. In some embodiments, the peptides of the disclosure may be 13-33 amino in length. In some
embodiments, the peptides of the disclosure may be 13-34 amino acids in length. In some embodiments, the peptides of the disclosure may be 13-35 amino acids in length. In some embodiments, the peptides of the disclosure may be 13-36 amino acids in length. In some embodiments, the peptides of the disclosure may be 13-37 amino acids in length. In some embodiments, the peptides of the disclosure may be 13-38 amino acids in length. In some embodiments, the peptides of the disclosure may be 13-39 amino acids in length. In some embodiments, the peptides of the disclosure may be 14-40 amino acids in length. In some embodiments, the peptides of the disclosure may be 14-15 amino acids in length. In some embodiments, the peptides of the disclosure may be 14-16 amino acids in length. In some embodiments, the peptides of the disclosure may be 14-17 amino acids in length. In some embodiments, the peptides of the disclosure may be 14-18 amino acids in length. In some embodiments, the peptides of the disclosure may be 14-19 amino acids in length. In some embodiments, the peptides of the disclosure may be 14-20 amino acids in length. In some embodiments, the peptides of the disclosure may be 14-21 amino acids in length. In some embodiments, the peptides of the disclosure may be 14-22 amino acids in length. In some embodiments, the peptides of the disclosure may be 14-23 amino acids in length. In some embodiments, the peptides of the disclosure may be 14-24 amino acids in length. In some embodiments, the peptides of the disclosure may be 14-25 amino acids in length. In some embodiments, the peptides of the disclosure may be 14-26 amino acids in length. In some embodiments, the peptides of the disclosure may be 14-27 amino acids in length. In some embodiments, the peptides of the disclosure may be 14-28 amino acids in length. In some
Attorney Docket #: 250298.000961 embodiments, the peptides of the disclosure may be 14-29 amino acids in length. In some embodiments, the peptides of the disclosure may be 14-30 amino acids in length. In some embodiments, the peptides of the disclosure may be 14-31 amino acids in length. In some embodiments, the peptides of the disclosure may be 14-32 amino acids in length. In some embodiments, the peptides of the disclosure may be 14-33 amino acids in length. In some embodiments, the peptides of the disclosure may be 14-34 amino acids in length. In some embodiments, the peptides of the disclosure may be 14-35 amino acids in length. In some embodiments, the peptides of the disclosure may be 14-36 amino acids in length. In some embodiments, the peptides of the disclosure may be 14-37 amino acids in length. In some embodiments, the peptides of the disclosure may be 14-38 amino acids in length. In some embodiments, the peptides of the disclosure may be 14-39 amino acids in length. In some embodiments, the peptides of the disclosure may be 15-40 amino acids in length. In some embodiments, the peptides of the disclosure may be 15-16 amino acids in length. In some embodiments, the peptides of the disclosure may be 15-17 amino acids in length. In some embodiments, the peptides of the disclosure may be 15-18 amino acids in length. In some embodiments, the peptides of the disclosure may be 15-19 amino acids in length. In some embodiments, the peptides of the disclosure may be 15-20 amino acids in length. In some embodiments, the peptides of the disclosure may be 15-21 amino acids in length. In some embodiments, the peptides of the disclosure may be 15-22 amino acids in length. In some embodiments, the peptides of the disclosure may be 15-23 amino acids in length. In some embodiments, the peptides of the disclosure may be 15-24 amino acids in length. In some embodiments, the peptides of the disclosure may be 15-25 amino acids in length. In some embodiments, the peptides of the disclosure may be 15-26 amino acids in length. In some embodiments, the peptides of the disclosure may be 15-27 amino acids in length. In some embodiments, the peptides of the disclosure may be 15-28 amino acids in length. In some embodiments, the peptides of the disclosure may be 15-29 amino acids in length. In some embodiments, the peptides of the disclosure may be 15-30 amino acids in length. In some embodiments, the peptides of the disclosure may be 15-31 amino acids in length. In some embodiments, the peptides of the disclosure may be 15-32 amino acids in length. In some embodiments, the peptides of the disclosure may be 15-33 amino acids in length. In some embodiments, the peptides of the disclosure may be 15-34 amino acids in length. In some
Attorney Docket #: 250298.000961 embodiments, the peptides of the disclosure may be 15-35 amino acids in length. In some embodiments, the peptides of the disclosure may be 15-36 amino acids in length. In some embodiments, the peptides of the disclosure may be 15-37 amino acids in length. In some embodiments, the peptides of the disclosure may be 15-38 amino acids in length. In some embodiments, the peptides of the disclosure may be 15-39 amino acids in length. In some embodiments, the peptides of the disclosure may be 15-40 amino acids in length. In some embodiments, the peptides of the disclosure may be 16-40 amino acids in length. In some embodiments, the peptides of the disclosure may be 16-17 amino acids in length. In some In some embodiments, the peptides of the disclosure may be 16-18 amino acids in length. In some embodiments, the peptides of the disclosure may be 16-19 amino acids in length. embodiments, the peptides of the disclosure may be 16-20 amino acids in length. In some embodiments, the peptides of the disclosure may be 16-21 amino acids in length. In some embodiments, the peptides of the disclosure may be 16-22 amino acids in length. In some embodiments, the peptides of the disclosure may be 16-23 amino acids in length. In some embodiments, the peptides of the disclosure may be 16-24 amino acids in length. In some embodiments, the peptides of the disclosure may be 16-25 amino acids in length. In some embodiments, the peptides of the disclosure may be 16-26 amino acids in length. In some embodiments, the peptides of the disclosure may be 16-27 amino acids in length. In some embodiments, the peptides of the disclosure may be 16-28 amino acids in length. In some embodiments, the peptides of the disclosure may be 16-29 amino acids in length. In some embodiments, the peptides of the disclosure may be 16-30 amino acids in length. In some embodiments, the peptides of the disclosure may be 16-31 amino acids in length. In some embodiments, the peptides of the disclosure may be 16-32 amino acids in length. In some embodiments, the peptides of the disclosure may be 16-33 amino acids in length. In some embodiments, the peptides of the disclosure may be 16-34 amino acids in length. In some embodiments, the peptides of the disclosure may be 16-35 amino acids in length. In some embodiments, the peptides of the disclosure may be 16-36 amino acids in length. In some embodiments, the peptides of the disclosure may be 16-37 amino acids in length. In some embodiments, the peptides of the disclosure may be 16-38 amino acids in length. In some embodiments, the peptides of the disclosure may be 16-39 amino acids in length. In some embodiments, the peptides of the disclosure may be 16-40 amino acids in length.
Attorney Docket #: 250298.000961 In some embodiments, the peptides of the disclosure may be 17-40 amino acids in length. In some embodiments, the peptides of the disclosure may be 17-18 amino acids in length. In some embodiments, the peptides of the disclosure may be 17-19 amino acids in length. In some embodiments, the peptides of the disclosure may be 17-20 amino acids in length. In some embodiments, the peptides of the disclosure may be 17-21 amino acids in length. In some embodiments, the peptides of the disclosure may be 17-22 amino acids in length. In some embodiments, the peptides of the disclosure may be 17-23 amino in length. In some
embodiments, the peptides of the disclosure may be 17-24 amino acids in length. In some embodiments, the peptides of the disclosure may be 17-25 amino in length. In some
embodiments, the peptides of the disclosure may be 17-26 amino acids in length. In some embodiments, the peptides of the disclosure may be 17-27 amino
in length. In some embodiments, the peptides of the disclosure may be 17-28 amino
in length. In some embodiments, the peptides of the disclosure may be 17-29 amino acids in length. In some embodiments, the peptides of the disclosure may be 17-30 amino
in length. In some embodiments, the peptides of the disclosure may be 17-31 amino
in length. In some embodiments, the peptides of the disclosure may be 17-32 amino acids in length. In some embodiments, the peptides of the disclosure may be 17-33 amino
in length. In some embodiments, the peptides of the disclosure may be 17-34 amino acids in length. In some embodiments, the peptides of the disclosure may be 17-35 amino
in length. In some embodiments, the peptides of the disclosure may be 17-36 amino
in length. In some embodiments, the peptides of the disclosure may be 17-37 amino acids in length. In some embodiments, the peptides of the disclosure may be 17-38 amino acids in length. In some embodiments, the peptides of the disclosure may be 17-39 amino acids in length. In some embodiments, the peptides of the disclosure may be 17-40 amino acids in length. In some embodiments, the peptides of the disclosure may be 18-22 amino acids in length. In some embodiments, the peptides of the disclosure may be 18-40 amino acids in length. In some embodiments, the peptides of the disclosure may be 18-19 amino acids in length. In some embodiments, the peptides of the disclosure may be 18-20 amino acids in length. In some embodiments, the peptides of the disclosure may be 18-21 amino acids in length. In some embodiments, the peptides of the disclosure may be 18-22 amino acids in length. In some embodiments, the peptides of the disclosure may be 18-23 amino acids in length. In some
Attorney Docket #: 250298.000961 embodiments, the peptides of the disclosure may be 18-24 amino acids in length. In some embodiments, the peptides of the disclosure may be 18-25 amino acids in length. In some embodiments, the peptides of the disclosure may be 18-26 amino acids in length. In some embodiments, the peptides of the disclosure may be 18-27 amino acids in length. In some embodiments, the peptides of the disclosure may be 18-28 amino acids in length. In some embodiments, the peptides of the disclosure may be 18-29 amino in length. In some
embodiments, the peptides of the disclosure may be 18-30 amino acids in length. In some embodiments, the peptides of the disclosure may be 18-31 amino in length. In some
embodiments, the peptides of the disclosure may be 18-32 amino acids in length. In some embodiments, the peptides of the disclosure may be 18-33 amino in length. In some
embodiments, the peptides of the disclosure may be 18-34 amino acids in length. In some embodiments, the peptides of the disclosure may be 18-35 amino acids in length. In some embodiments, the peptides of the disclosure may be 18-36 amino acids in length. In some embodiments, the peptides of the disclosure may be 18-37 amino acids in length. In some embodiments, the peptides of the disclosure may be 18-38 amino acids in length. In some embodiments, the peptides of the disclosure may be 18-39 amino acids in length. In some embodiments, the peptides of the disclosure may be 18-40 amino acids in length. In some embodiments, the peptides of the disclosure may be 19-40 amino acids in length. In some embodiments, the peptides of the disclosure may be 19-20 amino acids in length. In some embodiments, the peptides of the disclosure may be 19-21 amino acids in length. In some embodiments, the peptides of the disclosure may be 19-22 amino acids in length. In some embodiments, the peptides of the disclosure may be 19-23 amino acids in length. In some embodiments, the peptides of the disclosure may be 19-24 amino acids in length. In some embodiments, the peptides of the disclosure may be 19-25 amino acids in length. In some embodiments, the peptides of the disclosure may be 19-26 amino acids in length. In some embodiments, the peptides of the disclosure may be 19-27 amino acids in length. In some embodiments, the peptides of the disclosure may be 19-28 amino acids in length. In some embodiments, the peptides of the disclosure may be 19-29 amino acids in length. In some embodiments, the peptides of the disclosure may be 19-30 amino acids in length. In some embodiments, the peptides of the disclosure may be 19-31 amino acids in length. In some embodiments, the peptides of the disclosure may be 19-32 amino acids in length. In some
Attorney Docket #: 250298.000961 embodiments, the peptides of the disclosure may be 19-33 amino acids in length. In some embodiments, the peptides of the disclosure may be 19-34 amino acids in length. In some embodiments, the peptides of the disclosure may be 19-35 amino acids in length. In some embodiments, the peptides of the disclosure may be 19-36 amino acids in length. In some embodiments, the peptides of the disclosure may be 19-37 amino acids in length. In some embodiments, the peptides of the disclosure may be 19-38 amino acids in length. In some embodiments, the peptides of the disclosure may be 19-39 amino acids in length. In some embodiments, the peptides of the disclosure may be 19-40 amino acids in length. In some embodiments, the peptides of the disclosure may be 20-40 amino acids in length. In some embodiments, the peptides of the disclosure may be 20-21 amino acids in length. In some embodiments, the peptides of the disclosure may be 20-22 amino acids in length. In some embodiments, the peptides of the disclosure may be 20-23 amino acids in length. In some embodiments, the peptides of the disclosure may be 20-24 amino acids in length. In some embodiments, the peptides of the disclosure may be 20-25 amino
in length. In some embodiments, the peptides of the disclosure may be 20-26 amino
in length. In some embodiments, the peptides of the disclosure may be 20-27 amino acids in length. In some embodiments, the peptides of the disclosure may be 20-28 amino
in length. In some embodiments, the peptides of the disclosure may be 20-29 amino acids in length. In some embodiments, the peptides of the disclosure may be 20-30 amino
in length. In some embodiments, the peptides of the disclosure may be 20-31 amino
in length. In some embodiments, the peptides of the disclosure may be 20-32 amino acids in length. In some embodiments, the peptides of the disclosure may be 20-33 amino acids in length. In some embodiments, the peptides of the disclosure may be 20-34 amino acids in length. In some embodiments, the peptides of the disclosure may be 20-35 amino acids in length. In some embodiments, the peptides of the disclosure may be 20-36 amino acids in length. In some embodiments, the peptides of the disclosure may be 20-37 amino acids in length. In some embodiments, the peptides of the disclosure may be 20-38 amino acids in length. In some embodiments, the peptides of the disclosure may be 20-39 amino acids in length. In some embodiments, the peptides of the disclosure may be 20-40 amino acids in length. In some embodiments, the peptides of the disclosure may be 21-40 amino acids in length. In some embodiments, the peptides of the disclosure may be 21-22 amino acids in length. In some
Attorney Docket #: 250298.000961 embodiments, the peptides of the disclosure may be 21-22 amino acids in length. In some embodiments, the peptides of the disclosure may be 21-24 amino acids in length. In some embodiments, the peptides of the disclosure may be 21-25 amino acids in length. In some embodiments, the peptides of the disclosure may be 21-26 amino acids in length. In some embodiments, the peptides of the disclosure may be 21-27 amino acids in length. In some embodiments, the peptides of the disclosure may be 21-28 amino in length. In some
embodiments, the peptides of the disclosure may be 21-29 amino acids in length. In some embodiments, the peptides of the disclosure may be 21-30 amino in length. In some
embodiments, the peptides of the disclosure may be 21-31 amino acids in length. In some embodiments, the peptides of the disclosure may be 21-32 amino in length. In some
embodiments, the peptides of the disclosure may be 21-33 amino acids in length. In some embodiments, the peptides of the disclosure may be 21-34 amino acids in length. In some embodiments, the peptides of the disclosure may be 21-35 amino acids in length. In some embodiments, the peptides of the disclosure may be 21-36 amino acids in length. In some embodiments, the peptides of the disclosure may be 21-37 amino acids in length. In some embodiments, the peptides of the disclosure may be 21-38 amino acids in length. In some embodiments, the peptides of the disclosure may be 21-39 amino acids in length. In some embodiments, the peptides of the disclosure may be 21-40 amino acids in length. In some embodiments, the peptides of the disclosure may be 22-40 amino acids in length. In some embodiments, the peptides of the disclosure may be 22-23 amino acids in length. In some embodiments, the peptides of the disclosure may be 22-24 amino acids in length. In some embodiments, the peptides of the disclosure may be 22-25 amino acids in length. In some embodiments, the peptides of the disclosure may be 22-26 amino acids in length. In some embodiments, the peptides of the disclosure may be 22-27 amino acids in length. In some embodiments, the peptides of the disclosure may be 22-28 amino acids in length. In some embodiments, the peptides of the disclosure may be 22-29 amino acids in length. In some embodiments, the peptides of the disclosure may be 22-30 amino acids in length. In some embodiments, the peptides of the disclosure may be 22-31 amino acids in length. In some embodiments, the peptides of the disclosure may be 22-32 amino acids in length. In some embodiments, the peptides of the disclosure may be 22-33 amino acids in length. In some embodiments, the peptides of the disclosure may be 22-34 amino acids in length. In some
Attorney Docket #: 250298.000961 embodiments, the peptides of the disclosure may be 22-35 amino acids in length. In some embodiments, the peptides of the disclosure may be 22-36 amino acids in length. In some embodiments, the peptides of the disclosure may be 22-37 amino acids in length. In some embodiments, the peptides of the disclosure may be 22-38 amino acids in length. In some embodiments, the peptides of the disclosure may be 22-39 amino acids in length. In some embodiments, the peptides of the disclosure may be 22-40 amino acids in length. In some embodiments, the peptides of the disclosure may be 23-40 amino acids in length. In some embodiments, the peptides of the disclosure may be 23-24 amino acids in length. In some embodiments, the peptides of the disclosure may be 23-25 amino acids in length. In some embodiments, the peptides of the disclosure may be 23-25 amino acids in length. In some embodiments, the peptides of the disclosure may be 23-27 amino acids in length. In some embodiments, peptides of the disclosure may be 23-28 amino acids in length. In some embodiments, the peptides of the disclosure may be 23-29 amino acids in length. In some embodiments, the peptides of the disclosure may be 23-30 amino acids in length. In some embodiments, the peptides of the disclosure may be 23-31 amino acids in length. In some embodiments, the peptides of the disclosure may be 23-32 amino acids in length. In some embodiments, the peptides of the disclosure may be 23-33 amino acids in length. In some embodiments, the peptides of the disclosure may be 23-34 amino acids in length. In some embodiments, the peptides of the disclosure may be 23-35 amino acids in length. In some embodiments, the peptides of the disclosure may be 23-36 amino acids in length. In some embodiments, the peptides of the disclosure may be 23-37 amino acids in length. In some embodiments, the peptides of the disclosure may be 23-38 amino acids in length. In some embodiments, the peptides of the disclosure may be 23-39 amino acids in length. In some embodiments, the peptides of the disclosure may be 23-40 amino acids in length. In some embodiments, the peptides of the disclosure may be 24-40 amino acids in length. In some embodiments, the peptides of the disclosure may be 24-25 amino acids in length. In some embodiments, the peptides of the disclosure may be 24-26 amino acids in length. In some embodiments, the peptides of the disclosure may be 24-27 amino acids in length. In some embodiments, the peptides of the disclosure may be 24-28 amino acids in length. In some embodiments, the peptides of the disclosure may be 24-29 amino acids in length. In some embodiments, the peptides of the disclosure may be 24-30 amino acids in length. In some
Attorney Docket #: 250298.000961 embodiments, the peptides of the disclosure may be 24-31 amino acids in length. In some embodiments, the peptides of the disclosure may be 24-32 amino acids in length. In some embodiments, the peptides of the disclosure may be 24-33 amino acids in length. In some embodiments, the peptides of the disclosure may be 24-34 amino acids in length. In some embodiments, the peptides of the disclosure may be 24-35 amino acids in length. In some embodiments, the peptides of the disclosure may be 24-36 amino acids in length. In some embodiments, the peptides of the disclosure may be 24-37 amino acids in length. In some embodiments, the peptides of the disclosure may be 24-38 amino acids in length. In some embodiments, the peptides of the disclosure may be 24-39 amino acids in length. In some embodiments, the peptides of the disclosure may be 24-40 amino acids in length. In some embodiments, the peptides of the disclosure may be 25-40 amino acids in length. In some embodiments, the peptides of the disclosure may be 25-26 amino acids in length. In some embodiments, the peptides of the disclosure may be 25-27 amino acids in length. In some embodiments, the peptides of the disclosure may be 25-28 amino acids in length. In some embodiments, the peptides of the disclosure may be 25-29 amino acids in length. In some embodiments, the peptides of the disclosure may be 25-30 amino acids in length. In some embodiments, the peptides of the disclosure may be 25-31 amino acids in length. In some embodiments, the peptides of the disclosure may be 25-32 amino acids in length. In some embodiments, the peptides of the disclosure may be 25-33 amino acids in length. In some embodiments, the peptides of the disclosure may be 25-34 amino acids in length. In some embodiments, the peptides of the disclosure may be 25-35 amino acids in length. In some embodiments, the peptides of the disclosure may be 25-36 amino acids in length. In some embodiments, the peptides of the disclosure may be 25-37 amino acids in length. In some embodiments, the peptides of the disclosure may be 25-38 amino acids in length. In some embodiments, the peptides of the disclosure may be 25-39 amino acids in length. In some embodiments, the peptides of the disclosure may be 25-40 amino acids in length. In some embodiments, the peptides of the disclosure may be 26-40 amino acids in length. In some embodiments, the peptides of the disclosure may be 26-27 amino acids in length. In some embodiments, the peptides of the disclosure may be 26-28 amino acids in length. In some embodiments, the peptides of the disclosure may be 26-29 amino acids in length. In some embodiments, the peptides of the disclosure may be 26-30 amino acids in length. In some
Attorney Docket #: 250298.000961 embodiments, the peptides of the disclosure may be 26-31 amino acids in length. In some embodiments, the peptides of the disclosure may be 26-32 amino acids in length. In some embodiments, the peptides of the disclosure may be 26-33 amino acids in length. In some embodiments, the peptides of the disclosure may be 26-34 amino acids in length. In some embodiments, the peptides of the disclosure may be 26-35 amino acids in length. In some embodiments, the peptides of the disclosure may be 26-36 amino acids in length. In some embodiments, the peptides of the disclosure may be 26-37 amino acids in length. In some embodiments, the peptides of the disclosure may be 26-38 amino acids in length. In some embodiments, the peptides of the disclosure may be 26-39 amino acids in length. In some embodiments, the peptides of the disclosure may be 26-40 amino acids in length. In some embodiments, the peptides of the disclosure may be 27-40 amino acids in length. In some embodiments, the peptides of the disclosure may be 27-31 amino acids in length. In some embodiments, the peptides of the disclosure may be 27-32 amino acids in length. In some embodiments, the peptides of the disclosure may be 27-33 amino acids in length. In some embodiments, the peptides of the disclosure may be 27-34 amino acids in length. In some embodiments, the peptides of the disclosure may be 27-35 amino
in length. In some embodiments, the peptides of the disclosure may be 27-36 amino acids in length. In some embodiments, the peptides of the disclosure may be 27-37 amino
in length. In some embodiments, the peptides of the disclosure may be 27-38 amino acids in length. In some embodiments, the peptides of the disclosure may be 27-39 amino
in length. In some embodiments, the peptides of the disclosure may be 27-40 amino acids in length. In some embodiments, the peptides of the disclosure may be 28-40 amino acids in length. In some embodiments, the peptides of the disclosure may be 28-29 amino acids in length. In some embodiments, the peptides of the disclosure may be 28-30 amino acids in length. In some embodiments, the peptides of the disclosure may be 28-31 amino acids in length. In some embodiments, the peptides of the disclosure may be 28-32 amino acids in length. In some embodiments, the peptides of the disclosure may be 28-33 amino acids in length. In some embodiments, the peptides of the disclosure may be 28-34 amino acids in length. In some embodiments, the peptides of the disclosure may be 28-35 amino acids in length. In some embodiments, the peptides of the disclosure may be 28-36 amino acids in length. In some embodiments, the peptides of the disclosure may be 28-37 amino acids in length. In some
Attorney Docket #: 250298.000961 embodiments, the peptides of the disclosure may be 28-38 amino acids in length. In some embodiments, the peptides of the disclosure may be 28-39 amino acids in length. In some embodiments, the peptides of the disclosure may be 28-40 amino acids in length. In some embodiments, the peptides of the disclosure may be 29-40 amino acids in length. In some embodiments, the peptides of the disclosure may be 29-30 amino acids in length. In some embodiments, the peptides of the disclosure may be 29-31 amino acids in length. In some embodiments, the peptides of the disclosure may be 29-32 amino acids in length. In some embodiments, the peptides of the disclosure may be 29-33 amino acids in length. In some embodiments, the peptides of the disclosure may be 29-34 amino acids in length. In some embodiments, the peptides of the disclosure may be 29-35 amino acids in length. In some embodiments, the peptides of the disclosure may be 29-36 amino acids in length. In some embodiments, the peptides of the disclosure may be 29-37 amino acids in length. In some embodiments, the peptides of the disclosure may be 29-38 amino acids in length. In some embodiments, the peptides of the disclosure may be 29-39 amino acids in length. In some embodiments, the peptides of the disclosure may be 29-40 amino acids in length. In some embodiments, the peptides of the disclosure may be 30-40 amino acids in length. In some embodiments, the peptides of the disclosure may be 30-31 amino acids in length. In some embodiments, the peptides of the disclosure may be 30-32 amino acids in length. In some embodiments, the peptides of the disclosure may be 30-33 amino acids in length. In some embodiments, the peptides of the disclosure may be 30-34 amino acids in length. In some embodiments, the peptides of the disclosure may be 30-35 amino acids in length. In some embodiments, the peptides of the disclosure may be 30-36 amino acids in length. In some embodiments, the peptides of the disclosure may be 30-37 amino acids in length. In some embodiments, the peptides of the disclosure may be 30-38 amino acids in length. In some embodiments, the peptides of the disclosure may be 30-39 amino acids in length. In some embodiments, the peptides of the disclosure may be 30-40 amino acids in length. In some embodiments, the peptides of the disclosure may be 5-40 amino acids in length, or 5-33 amino acids in length, or 5-30 amino acids in length, or 5-23 amino acids in length, 5-20 amino acids in length, or 5-17 amino acids in length, or 5-14 amino acids in length, or 5-12 amino acids in length, or 5-11 amino acids in length, or 5-10 amino acids in length, or 6-40 amino acids in length, or 6-33 amino acids in length, or 6-30 amino acids in length, or 6-23 amino acids in
Attorney Docket #: 250298.000961 length, or 6-20 amino acids in length, or 6-17 amino acids in length, or 6-14 amino acids in length, or 6-12 amino acids in length, or 6-11 amino acids in length, or 6-10 amino acids in length, or 7- 40 amino acids in length, or 7-33 amino acids in length, or 7-30 amino acids in length, or 7-23 amino acids in length, or 7-20 amino acids in length, or 7-17 amino acids in length, or 7-14 amino acids in length, or 7-12 amino acids in length, or 7-11 amino acids in length, or 7-10 amino acids in length, or 8-40 amino acids in length, or 8-33 amino acids in length, or 8-30 amino acids in length, or 8-23 amino acids in length, or 8-20 amino acids in length, or 8-17 amino acids in length, or 8-14 amino acids in length, or 8-12 amino acids in length, or 8-11 amino acids in length, or 8- 10 amino acids in length, or 9-40 amino acids in length, or 9-33 amino acids in length, 9-30 amino acids in length, or 9-23 amino acids in length, or 9-20 amino acids in length, or 9-17 amino acids in length, or 9-14 amino acids in length, or 9-12 amino acids in length, or 9-11 amino acids in length, or 9-10 amino acids in length, or 10-40 amino acids in length, or 10-33 amino acids in length, or 10-30 amino acids in length, or 10-23 amino acids in length, or 10-20 amino acids in length, or 10-17 amino acids in length, or 10-14 amino acids in length, or 10-12 amino acids in length, or 10-11 amino acids in length, or 11-40 amino acids in length, or 11-33 amino acids in length, or 11-30 amino acids in length, or 11-23 amino acids in length, or 11-20 amino acids in length, or 11-17 amino acids in length, or 11-14 amino acids in length, or 11-12 amino acids in length, or 12-40 amino acids in length, or 12-33 amino acids in length, or 12-30 amino acids in length, or 12-23 amino acids in length, or 12-20 amino acids in length, or 12-17 amino acids in length, or 12-14 amino acids in length, or 40 amino acids in length, or 39 amino acids in length, or 38 amino acids in length, or 37 amino acids in length, or 36 amino acids in length, or 35 amino acids in length, or 34 amino acids in length, or 33 amino acids in length, or 32 amino acids in length, or 31 amino acids in length, or 30 amino acids in length, or 29 amino acids in length, or 28 amino acids in length, or 27 amino acids in length, or 26 amino acids in length, or 25 amino acids in length, or 24 amino acids in length, or 23 amino acids in length, or 22 amino acids in length, or 21 amino acids in length, or 20 amino acids in length, or 19 amino acids in length, or 18 amino acids in length, or 17 amino acids in length, or 16 amino acids in length, or 15 amino acids in length, or 14 amino acids in length, or 13 amino acids in length, or 12 amino acids in length, or 11 amino acids in length, or 10 amino acids in length, or 9 amino acids in length, or 8 amino acids in length, or 7 amino acids in length, or 6 amino acids in length, or 5 amino acids in length.
Attorney Docket #: 250298.000961 In some embodiments, the peptides of the disclosure may be 5-30 amino acids in length, or 8-30 amino acids in length, or 8-20 amino acids in length, or 8-23 amino acids in length, or 8- 17 amino acids in length, or 8-14 amino acids in length, or 8-12 amino acids in length, or 8-11 amino acids in length, or 8-10 amino acids in length, or 9-30 amino acids in length, or 9-20 amino acids in length, or 9-23 amino acids in length, or 9-17 amino acids in length, or 9-14 amino acids in length, or 9-12 amino acids in length, or 9-11 amino acids in length, or 9-10 amino acids in length, or 12-30 amino acids in length, or 12-23 amino acids in length, or 12-20 amino acids in length, or 12-17 amino acids in length, or 12-14 amino acids in length, or 12 amino acids in length, or 10 amino acids in length, or 9 amino acids in length. The peptides of the disclosure may comprise one or more chemical modifications. Non- limiting examples of chemical modifications include, for example, phosphorylation, acetylation, deamidation acylation, amidination, pyridoxylation of lysine, reductive alkylation, trinitrobenzylation of amino groups with 2,4,6-trinitrobenzene sulphonic acid (TNBS), amide modification of carboxyl groups and sulphydryl modification by performic acid oxidation of cysteine to cysteic acid, formation of mercurial derivatives, formation of mixed disulfides with other thiol compounds, reaction with maleimide, carboxymethylation with iodoacetic acid or iodoacetamide and carbamoylation with cyanate at alkaline pH. Chemical modifications may correspond to those that are not present in vivo. For example, modification of, for example, arginyl residues in proteins may be based on the reaction of vicinal dicarbonyl compounds such as phenylglyoxal, 2,3-butanedione, and 1,2- cyclohexanedione to form an adduct. Another example is the reaction of methylglyoxal with arginine residues. Cysteine can be modified without concomitant modification of other nucleophilic sites such as lysine and histidine. Selective reduction of disulfide bonds in proteins can also be performed. Disulfide bonds can be formed and oxidized during the heat treatment of biopharmaceuticals. Woodward’s Reagent K may be used to modify specific glutamic acid residues. N-(3-(dimethylamino)propyl)-N′-ethylcarbodiimide can be used to form intra-molecular crosslinks between a lysine residue and a glutamic acid residue. For example, diethylpyrocarbonate and 4-hydroxy-2-nonenal can be used to modify histidyl residues in proteins. The reaction of lysine residues and other α-amino groups is, for example, useful in binding of peptides to surfaces or the cross-linking of proteins/peptides. Lysine is the site of attachment of poly(ethylene)glycol and the major site of modification in the glycosylation of proteins.
Attorney Docket #: 250298.000961 Methionine residues in proteins can be modified with e.g., iodoacetamide, bromoethylamine, and chloramine T. Tetranitromethane and N-acetylimidazole can be used for the modification of tyrosyl residues. Cross-linking via the formation of dityrosine can be accomplished with hydrogen peroxide/copper ions. N-bromosuccinimide, 2-hydroxy-5-nitrobenzyl bromide or 3-bromo-3- methyl-2-(2-nitrophenylmercapto)-3H-indole (BPNS-skatole) have been used in recent studies for the modification of tryptophan. Peptides described herein may comprise one or more (e.g., 1, 2, 3, or 4) amino acid substitutions and/or insertions and/or deletions. Amino acid substitution means that an amino acid residue is substituted for a replacement amino acid residue at the same position. Inserted amino acid residues may be inserted at any position and may be inserted such that some or all of the inserted amino acid residues are immediately adjacent one another or may be inserted such that none of the inserted amino acid residues is immediately adjacent another inserted amino acid residue. One or more (e.g., 1, 2, 3 or 4) amino acids may be substituted and/or inserted and/or deleted from the sequence of any one of SEQ ID NOs: 30-47 and 200-240. Each substitution and/or insertion and/or deletion can take place at any position of any one of SEQ ID NOs: 30-47 and 200- 240. In some embodiments, the peptides of the disclosure may comprise additional amino acids (e.g., 1, 2, 3 or 4) at the C-terminal end and/or at the N-terminal end of the sequence of any one SEQ ID NOs: 30-47 and 200-240. A peptide of the disclosure may comprise the amino acid sequence of any one of SEQ ID NOs: 30-47 and 200-240 except for one or more (e.g., 1, 2, 3, or 4) amino acid substitutions, insertions or deletions. Amino acid substitutions may be conservative, by which it is meant the substituted amino acid has similar chemical properties to the original amino acid. For example, the following groups of amino acids share similar chemical properties such as size, charge, and polarity: Group 1 - Ala, Ser, Thr, Pro, Gly; Group 2 - Asp, Asn, Glu, Gln; Group 3 - His, Arg, Lys; Group 4 - Met, Leu, Ile, Val, Cys; Group 5 - Phe, Thy, Trp. In another aspect, the disclosure provides a complex of a peptide of the disclosure and an MHC molecule (pMHC complex). Preferably, the peptide is bound to the peptide binding groove of the MHC molecule. In some embodiments, the peptide and the MHC molecule form a non- covalent complex. In other embodiments, the peptide and the MHC molecule may be covalently
Attorney Docket #: 250298.000961 linked, for example, via a linker. Accordingly, the present disclosure also provides libraries comprising one or more of the pMHC complexes described herein. An exemplary pMHC complex library of the present disclosure may comprise one or more pMHC complexes comprising an off-target peptide associated with MAGEA3168-176 target EVDPIGHLY (SEQ ID NO: 29) as described herein. In some embodiments, off-target peptides associated with MAGEA3168-176 target EVDPIGHLY (SEQ ID NO: 29) include those listed in Tables 2-4 herein. In some embodiments, an off-target peptide associated with MAGEA3168-176 target EVDPIGHLY (SEQ ID NO: 29) present in pMHC complexes comprises an amino acid sequence of any of SEQ ID NOs: 30-47 and 200-232, or a pharmaceutically acceptable salt thereof, or a fragment or derivative thereof. In some embodiments, an off-target peptide associated with MAGEA3168-176 target EVDPIGHLY (SEQ ID NO: 29) present in pMHC complexes comprises an amino acid sequence of any of SEQ ID NOs: 200-232, or a pharmaceutically acceptable salt thereof, or a fragment or derivative thereof. In some embodiments, an off-target peptide associated with MAGEA3168-176 target EVDPIGHLY (SEQ ID NO: 29) present in pMHC complexes comprises an amino acid sequence of any of SEQ ID NOs: 200-201, or a pharmaceutically acceptable salt thereof, or a fragment or derivative thereof. In some embodiments, an off-target peptide associated with MAGEA3168-176 target EVDPIGHLY (SEQ ID NO: 29) present in pMHC complexes comprises an amino acid sequence of any of SEQ ID NOs: 202-232, or a pharmaceutically acceptable salt thereof, or a fragment or derivative thereof. In some embodiments, an off-target peptide associated with MAGEA3168-176 target EVDPIGHLY (SEQ ID NO: 29) present in pMHC complexes comprises an amino acid sequence of any of SEQ ID NOs: 202-230, or a pharmaceutically acceptable salt thereof, or a fragment or derivative thereof. In some embodiments, an off-target peptide associated with MAGEA3168-176 target EVDPIGHLY (SEQ ID NO: 29) present in pMHC complexes comprises an amino acid sequence of any of SEQ ID NOs: 209, 231, and 232, or a pharmaceutically acceptable salt thereof, or a fragment or derivative thereof. In some embodiments, an off-target peptide associated with MAGEA3168-176 target EVDPIGHLY (SEQ ID NO: 29) present in a pMHC complex consists essentially of an amino acid sequence of any of SEQ ID NOs: 30-47 and 200-232. In some embodiments, an off-target peptide associated with MAGEA3168-176 target EVDPIGHLY (SEQ ID NO: 29) present in a pMHC complex consists essentially of an amino acid sequence of any of SEQ ID NOs: 200-232. In some
Attorney Docket #: 250298.000961 embodiments, an off-target peptide associated with MAGEA3168-176 target EVDPIGHLY (SEQ ID NO: 29) present in a pMHC complex consists essentially of an amino acid sequence of any of SEQ ID NOs: 200-201. In some embodiments, an off-target peptide associated with MAGEA3168-176 target EVDPIGHLY (SEQ ID NO: 29) present in a pMHC complex consists essentially of an amino acid sequence of any of SEQ ID NOs: 202-232. In some embodiments, an off-target peptide associated with MAGEA3168-176 target EVDPIGHLY (SEQ ID NO: 29) present in a pMHC complex consists essentially of an amino acid sequence of any of SEQ ID NOs: 202-230. In some embodiments, an off-target peptide associated with MAGEA3168-176 target EVDPIGHLY (SEQ ID NO: 29) present in a pMHC complex consists essentially of an amino acid sequence of any of SEQ ID NOs: 209, 231, and 232. In some embodiments, an off-target peptide associated with MAGEA3168-176 target EVDPIGHLY (SEQ ID NO: 29) present in a pMHC complex consists of an amino acid sequence of any one of SEQ ID NOs: 30-47 and 200-232. In some embodiments, an off-target peptide associated with MAGEA3168-176 target EVDPIGHLY (SEQ ID NO: 29) present in a pMHC complex consists of an amino acid sequence of any one of SEQ ID NOs: 200-232. In some embodiments, an off-target peptide associated with MAGEA3168-176 target EVDPIGHLY (SEQ ID NO: 29) present in a pMHC complex consists of an amino acid sequence of any one of SEQ ID NOs: 200-201. In some embodiments, an off-target peptide associated with MAGEA3168-176 target EVDPIGHLY (SEQ ID NO: 29) present in a pMHC complex consists of an amino acid sequence of any one of SEQ ID NOs: 202-232. In some embodiments, an off-target peptide associated with MAGEA3168-176 target EVDPIGHLY (SEQ ID NO: 29) present in a pMHC complex consists of an amino acid sequence of any one of SEQ ID NOs: 202-230. In some embodiments, an off-target peptide associated with MAGEA3168-176 target EVDPIGHLY (SEQ ID NO: 29) present in a pMHC complex consists of an amino acid sequence of any one of SEQ ID NOs: 209, 231, and 232. An exemplary pMHC complex library of the present disclosure may comprise one or more pMHC complexes comprising an off-target peptide associated with WT1126-134 target RMFPNAPYL (SEQ ID NO: 241) as described herein. In some embodiments, off-target peptides associated with WT1126-134 target RMFPNAPYL (SEQ ID NO: 241) include those listed in Table 5 herein. In some embodiments, an off-target peptide associated with WT1126-134 target RMFPNAPYL (SEQ ID NO: 241) present in pMHC complexes comprises an amino acid sequence
Attorney Docket #: 250298.000961 of any of SEQ ID NOs: 233-240, or a pharmaceutically acceptable salt thereof, or a fragment or derivative thereof. In some embodiments, an off-target peptide associated with WT1126-134 target RMFPNAPYL (SEQ ID NO: 241) present in a pMHC complex consists essentially of an amino acid sequence of any of SEQ ID NOs: 233-240. In some embodiments, an off-target peptide associated with WT1126-134 target RMFPNAPYL (SEQ ID NO: 241) present in a pMHC complex consists of an amino acid sequence of any one of SEQ ID NOs: 233-240. pMHC complex libraries of present disclosure may comprise any number of a plurality of pMHC complexes as desired. For example, a pMHC complex library of the present disclosure may comprise at least 2 pMHC complexes, at least 3 pMHC complexes, at least 4 pMHC complexes, at least 5 pMHC complexes, at least 6 pMHC complexes, at least 7 pMHC complexes, at least 8 pMHC complexes, at least 9 pMHC complexes, at least 10 pMHC complexes, at least 20 pMHC complexes, at least 30 pMHC complexes, at least 40 pMHC complexes, at least 50 pMHC complexes, or about 2-5 pMHC complexes, about 2-10 pMHC complexes, about 5-15 pMHC complexes, about 10-20 pMHC complexes, about 10-30 pMHC complexes, about 12-25 pMHC complexes, about 20-30 pMHC complexes, about 25-50 pMHC complexes, about 40-80 pMHC complexes, about 50-100 pMHC complexes, about 60-120 pMHC complexes, about 70-140 pMHC complexes, about 80-160 pMHC complexes, about 90-180 pMHC complexes, about 100- 200 pMHC complexes, about 110-220 pMHC complexes, about 120-240 pMHC complexes, about 130-260 pMHC complexes, about 140-280 pMHC complexes, about 150-300 pMHC complexes, about 160-320 pMHC complexes, about 170-340 pMHC complexes, about 180-360 pMHC complexes, about 190-380 pMHC complexes, about 200-400 pMHC complexes, about 210-420 pMHC complexes, about 220-440 pMHC complexes, about 230-460 pMHC complexes, about 240-480 pMHC complexes, about 250-500 pMHC complexes, about 260-520 pMHC complexes, about 270-540 pMHC complexes, about 280-560 pMHC complexes, about 290-580 pMHC complexes, about 300-600 pMHC complexes, about 310-620 pMHC complexes, about 320-640 pMHC complexes, about 330-660 pMHC complexes, about 340-680 pMHC complexes, about 350-700 pMHC complexes, about 360-720 pMHC complexes, about 370-740 pMHC complexes, about 380-760 pMHC complexes, about 390-780 pMHC complexes, about 400-800 pMHC complexes, about 410-820 pMHC complexes, about 420-840 pMHC complexes, about 430-860 pMHC complexes, about 440-880 pMHC complexes, about 450-900 pMHC complexes, about
Attorney Docket #: 250298.000961 460-920 pMHC complexes, about 470-940 pMHC complexes, about 480-960 pMHC complexes, about 490-980 pMHC complexes, about 500-1000 pMHC complexes, etc. MHC molecules used in pMHC complexes described herein include naturally occurring full-length MHC molecules as well as individual chains of MHC molecules (e.g., MHC class I α (heavy) chain, β2-microglobulin, MHC class II α chain, and MHC class II β chain), individual subunits of such chains of MHCs (e.g., α1, α2 and/or α3 subunits of MHC class I α chain, α1 and/or α2 subunits of MHC class II α chain, β1 and/or β2 subunits of MHC class II β chain) as well as fragments, mutants, and various derivatives thereof (including fusion proteins, e.g., fusions with viral envelope proteins or fusogens), wherein such fragments, mutants, and derivatives retain the ability to display an antigenic determinant for recognition by an antigen- recognition molecule. Naturally-occurring MHC molecules are encoded by a cluster of genes on human chromosome 6 or mouse chromosome 17. MHCs are also referred to as H-2 in mice and Human Leucocyte Antigen (HLA) in humans. MHC class I molecules specifically bind CD8 molecules expressed on cytotoxic T lymphocytes (CD8+ T cells), whereas MHC class II molecules specifically bind CD4 molecules expressed on helper T lymphocytes (CD4+ T cells). MHCs include, but are not limited to, HLA specificities such as A (e.g., A1-A74), B (e.g., B1-B77), C (e.g., C1-C11), D (e.g., D1-D26), E, G, DR (e.g., DR1-DR8), DQ (e.g., DQ1-DQ9) and DP (e.g., DP1-DP6). In some embodiments, the MHC molecule in a pMHC complex of the present disclosure is a human leukocyte antigen (HLA) molecule. The MHC molecule may be a human HLA molecule selected from the group consisting of HLA-A, HLA-B, HLA-C, HLA-E, HLA-F, and HLA-G. In some embodiments, the MHC class I or MHC II polypeptides may be derived from any functional human HLA-A, B, C, DR, or DQ molecules. Non-limiting examples of HLA-A alleles comprise, without limitation, A*01:01, A*02:01, A*02:02, A*03:01, A*11:01, A*23:01, A*24:02, A*25:01, A*26:01, A*29:01, A*29:02, A*31:01, A*32:01, A*33:01, A*34:01, A*36:01, A*43:01, A*66:01, A*68:01, A*69:01, A*74:01, and A*80:01. Non-limiting examples of HLA-B alleles comprise, without limitation, B*07:02. B*08:01, B*13:01, B*14:01, B*14:02, B*15:01, B*18:01, B*18:02, B*27:01, B*27:02, B*35:01, B*35:02, B*37:01, B*38:01, B*39:01, B*40:01, B*41:01, B*42:01, B*44:02, B*45:01, B*46:01, B*47:01, B*48:01, B*49:01, B*50:01, B*51:01, B*52:01, B*53:01, B*54:01, B*55:01, B*55:02, B*56:01, B*57:01, B*58:01, B*59:01, B*67:01, B*73:01, B*15:17, B*81:01, B*82:01, and B*83:01. Non-limiting examples of HLA-C
Attorney Docket #: 250298.000961 alleles comprise, without limitation, C*01:01, C*02:02, C*03:03, C*04:01, C*05:01, C*06:02, C*07:01, C*07:02, C*08:02, C*12:03, C*14:01, C*15:02, C*16:01, C*17:01, and. C*18:01. Non- limiting examples of HLA-DR alleles comprise, without limitation, DRB1*01:01, DRB1*01:03, DRB1*15:01, DRB1*15:02, DRB1*16:01, DRB1*16:02, DRB1*03:01, DRB1*04:01, DRB1*04:04, DRB1*11:01, DRB1*12:01, DRB1*13:01, DRB1*13:02, DRB1*14:01, DRB1*14:02, DRB1*07:01, DRB1*08:01, DRB1*08:02, DRB1*08:03, DRB1*09:01, and DRB1*10:01. In some embodiments, the MHC class I molecule may be selected from HLA-A*02, HLA-A*01, HLA-A*03, HLA-A*11, HLA-A*23, HLA-A*24, HLA-B*07, HLA-B*08, HLA- B*40, HLA-B*44, HLA-B*15, HLA-C*04, HLA*C*03, and HLA-C*07. There are also allelic variants of the above HLA types, all of which are encompassed by the present disclosure. In some embodiments, the MHC molecule may be HLA-A*02:01 or HLA-A*01:01. Naturally occurring MHC class I molecules consist of an α (heavy) chain associated with β2-microglobulin. The heavy chain consists of subunits α1-α3. The β2-microglobulin protein and α3 subunit of the heavy chain are associated. In certain embodiments, β2-microglobulin and α3 subunit are covalently bound. In certain embodiments, β2-microglobulin and α3 subunit are non- covalently bound. The α1 and α2 subunits of the heavy chain fold to form a groove for a peptide to be displayed and recognized by TCR. In some embodiments, the MHC contained in a pMHC complex of the disclosure comprises (i) a class I MHC polypeptide or a fragment, mutant or derivative thereof, and, optionally, (ii) a β2 microglobulin polypeptide or a fragment, mutant or derivative thereof. In one specific embodiment, the class I MHC polypeptide is linked to the β2 microglobulin polypeptide by a peptide linker. pMHC complexes of the disclosure may be isolated and/or in a substantially pure form. For example, the complex may be provided in a form which is substantially free of other peptides or proteins. MHC molecules as disclosed herein can include recombinant MHC molecules, non- naturally occurring MHC molecules, and functionally equivalent fragments of MHC, including derivatives or variants thereof, provided that peptide binding is retained. For example, MHC molecules may be attached to a solid support, in soluble form, attached to a tag, biotinylated and/or in multimeric form. A peptide disclosed herein may be covalently attached to the MHC.
Attorney Docket #: 250298.000961 Methods to produce soluble recombinant MHC molecules with which peptides disclosed herein can form a complex include, but are not limited to, expression and purification from E. coli cells or insect cells. Alternatively, MHC molecules may be produced synthetically, or using cell free systems. The peptides disclosed herein may be presented on the surface of a cell in complex with MHC. Thus, the present disclosure also provides a cell presenting on its surface a pMHC complex disclosed herein. Such a cell may be a mammalian cell, preferably a cell of the immune system, and a specialized antigen-presenting cell (APC) such as a dendritic cell or a B cell. The cell may be an immortalized cell. The cell may be a cell that does not naturally present the peptide of interest. In certain embodiments, the cell may be a cell that is deficient in presenting endogenous peptides in complex with MHC on the cell surface. For example, the cell may be a cell that has been genetically modified to be deficient in endogenous peptide presentation, such as a cell that has been modified to be transporter associated with antigen processing (TAP) deficient. The cell may be a cell that requires loading exogenous peptide on the cell surface in order to measurably detect antigen-recognition molecule binding to the peptide. Incubation of the cell with exogenous β2 microglobulin (β2m) may increase the loading/presentation of exogenously administered peptides. Preferred cells may include T2 cells, which are well known in the art. See, e.g., Elliot et al., J Exp Med. 1995 Apr 1;181(4):1481-91, which is herein incorporated by reference in its entirety. Cells presenting a peptide or pMHC complex of the disclosure may be isolated, preferably in the form of a homogenous population, or provided in a substantially pure form. Such cells may not naturally present a pMHC complex of the disclosure, or alternatively said cells may present the pMHC complex at a level higher than they would in nature. Cells presenting pMHC complexes may be obtained by pulsing said cells with one or more peptides (e.g., 2 to 10, 2 to 20, 2 to 30, 5 to 25, 5 to 20, or 10 to 15 peptides) of the disclosure, or genetically modifying the cells (via DNA or RNA transfer) to express one or more peptides (e.g., 2 to 10, 2 to 20, 2 to 30, 5 to 25, 5 to 20, or 10 to 15 peptides) of the disclosure. Pulsing involves incubating the cells with the peptide for several hours using peptide concentrations typically ranging from 10−5 to 10−12 M. Pulsing may also involve incubating the cells with exogenous β2m molecules, for example at the same or in similar concentrations as the peptide. Such cells may additionally be transduced with HLA molecules, such as HLA-A*02 to further induce presentation of the peptide(s). Cells may be
Attorney Docket #: 250298.000961 produced recombinantly. Cells presenting peptides of the disclosure may be used to isolate antigen-binding molecules (e.g., antibodies, T cells, TCRs and CARs) which can bind to the cells. Peptides or pMHC complexes disclosed herein may be fused or conjugated to one or more heterologous molecules. Peptides or pMHC complexes of the disclosed herein may also be in multimeric form. Accordingly, the present disclosure also provides fusion proteins, conjugates, and oligomeric complexes comprising a peptide or a pMHC complex of the disclosure. In some embodiments, peptides are fused or conjugated to one or more heterologous molecules which can include an MHC molecule (or fragments thereof). Heterologous molecules suitable for genetical fusion and/or chemical conjugation with the peptides or the pMHC complexes of the disclosure include, but are not limited to, peptides, polypeptides, small molecules, polymers, nucleic acids, lipids, sugars, etc. The heterologous molecule(s) may be fused at the N- and/or C-terminus of the peptide and/or another polypeptide chain in the pMHC complex. Heterologous peptides and polypeptides include, but are not limited to, an epitope (e.g., FLAG) or a tag sequence (e.g., His6 (SEQ ID NO: 149), and the like) to allow for the detection and/or isolation of a fusion protein; a transmembrane receptor protein or a portion thereof, such as an extracellular domain or a transmembrane and intracellular domain; a ligand or a portion thereof which binds to a transmembrane receptor protein; an enzyme or portion thereof which is catalytically active; a polypeptide or peptide which promotes oligomerization, such as a leucine zipper domain; a polypeptide or peptide which increases stability, such as an immunoglobulin constant region (e.g., an Fc domain); a half-life-extending sequence comprising a combination of two or more (e.g., 2, 5, 10, 15, 20, 25, etc.) naturally occurring or non-naturally occurring charged and/or uncharged amino acids (e.g., Ser, Gly, Glu or Asp) designed to form a predominantly hydrophilic or predominantly hydrophobic fusion partner for a fusion protein; a functional or non- functional antibody (e.g., an antibody that is specific for dendritic cells), or a heavy or light chain thereof; and a polypeptide which has an activity different from fusion proteins of the present disclosure. In some embodiments, fusion proteins of the disclosure may comprise one or more affinity tags, e.g., to allow for affinity purification or coupling to another molecule. Examples of affinity tags include, but are not limited to, a His6 (SEQ ID NO: 149) tag, an Avi-tag, a biotin, a hemagglutinin (HA) tag, a FLAG tag, a Myc tag, a GST tag, a MBP tag, a chitin binding protein
Attorney Docket #: 250298.000961 tag, a calmodulin tag, a V5 tag, a streptavidin binding tag, a green fluorescent protein (GFP), YFP, RFP, CFP, mCherry, tdTomato, SUMO tag, and Ubiquitin tag. Peptides or pMHC complexes of the disclosure may be provided in soluble form, or may be immobilized by attachment to a suitable solid support. Examples of solid supports include, but are not limited to, a bead (e.g., a magnetic bead), a membrane, sepharose, a plate, a tube, a column, etc. pMHC complexes may be attached, for example, to an ELISA plate, a magnetic bead, or a surface plasmon resonance biosensor chip. Methods of attaching peptides or pMHC complexes to a solid support are known to the skilled person, and include, for example, using an affinity binding pair, e.g., biotin and streptavidin, or antibodies and antigens. In some embodiments, peptides or pMHC complexes are labeled with biotin and attached to streptavidin-coated surfaces. In another aspect, the disclosure provides an isolated polynucleotide comprising a nucleic acid sequence encoding one or more peptide(s) and/or peptide-based molecules (such as complexes (e.g., pMHC complexes), fusion proteins, or conjugates comprising the described peptides) of the disclosure. The polynucleotide may be, for example, DNA, cDNA, PNA, RNA or combinations thereof, either single- and/or double-stranded, or native or stabilized forms of polynucleotides, such as, for example, polynucleotides with a phosphorothioate backbone and it may or may not contain introns so long as it codes for the peptide. In some embodiments, a polynucleotide described herein encodes a peptide comprising an amino acid sequence of any one of SEQ ID NOs: 30-47 and 200-232, or a fragment or derivative thereof. In some embodiments, the polynucleotide described herein encodes a peptide consisting essentially of an amino acid sequence of any one of SEQ ID NOs: 30-47 and 200-232, or a fragment or derivative thereof. In some embodiments, the polynucleotide described herein encodes a peptide consisting of an amino acid sequence of any one of SEQ ID NOs: 30-47 and 200-232, or a fragment or derivative thereof. In some embodiments, a polynucleotide described herein encodes a peptide comprising an amino acid sequence of any one of SEQ ID NOs: 200-232, or a fragment or derivative thereof. In some embodiments, the polynucleotide described herein encodes a peptide consisting essentially of an amino acid sequence of any one of SEQ ID NOs: 200-232, or a fragment or derivative thereof. In some embodiments, the polynucleotide described herein encodes a peptide consisting of an amino acid sequence of any one of SEQ ID NOs: 200-232, or a fragment or derivative thereof
Attorney Docket #: 250298.000961 In some embodiments, a polynucleotide described herein encodes a peptide comprising an amino acid sequence of any one of SEQ ID NOs: 200-201, or a fragment or derivative thereof. In some embodiments, the polynucleotide described herein encodes a peptide consisting essentially of an amino acid sequence of any one of SEQ ID NOs: 200-201, or a fragment or derivative thereof. In some embodiments, the polynucleotide described herein encodes a peptide consisting of an amino acid sequence of any one of SEQ ID NOs: 200-201, or a fragment or derivative thereof. In some embodiments, a polynucleotide described herein encodes a peptide comprising an amino acid sequence of SEQ ID NOs: 202-232, or a fragment or derivative thereof. In some embodiments, the polynucleotide described herein encodes a peptide consisting essentially of an amino acid sequence of SEQ ID NOs: 202-232, or a fragment or derivative thereof. In some embodiments, the polynucleotide described herein encodes a peptide consisting of an amino acid sequence of SEQ ID NOs: 202-232, or a fragment or derivative thereof. In some embodiments, a polynucleotide described herein encodes a peptide comprising an amino acid sequence of SEQ ID NOs: 202-230, or a fragment or derivative thereof. In some embodiments, the polynucleotide described herein encodes a peptide consisting essentially of an amino acid sequence of SEQ ID NOs: 202-230, or a fragment or derivative thereof. In some embodiments, the polynucleotide described herein encodes a peptide consisting of an amino acid sequence of SEQ ID NOs: 202-230, or a fragment or derivative thereof. In some embodiments, a polynucleotide described herein encodes a peptide comprising an amino acid sequence of SEQ ID NOs: 209, 231, and 232, or a fragment or derivative thereof. In some embodiments, the polynucleotide described herein encodes a peptide consisting essentially of an amino acid sequence of SEQ ID NOs: 209, 231, and 232, or a fragment or derivative thereof. In some embodiments, the polynucleotide described herein encodes a peptide consisting of an amino acid sequence of SEQ ID NOs: 209, 231, and 232, or a fragment or derivative thereof. In some embodiments, a polynucleotide described herein encodes a peptide comprising an amino acid sequence of SEQ ID NOs: 233-240, or a fragment or derivative thereof. In some embodiments, the polynucleotide described herein encodes a peptide consisting essentially of an amino acid sequence of SEQ ID NOs: 233-240, or a fragment or derivative thereof. In some
Attorney Docket #: 250298.000961 embodiments, the polynucleotide described herein encodes a peptide consisting of an amino acid sequence of SEQ ID NOs: 233-240, or a fragment or derivative thereof. In a further aspect, the disclosure provides a vector comprising a nucleic acid sequence of the disclosure. The vector may include, in addition to a nucleic acid sequence encoding only a peptide of the disclosure, one or more additional nucleic acid sequences encoding one or more additional peptides. Such additional peptides may, once expressed, be fused to the N-terminus or the C-terminus of the peptide of the disclosure. Examples of such additional peptides are detailed in the sections above. In one embodiment, the vector includes a nucleic acid sequence encoding a peptide or protein tag such as, for example, a biotinylation site, a FLAG-tag, a MYC-tag, an HA- tag, a GST-tag, a Strep-tag or a poly-histidine tag. The off-target peptides identified herein may be used for evaluation and/or screening of therapeutic molecules for molecules that have minimal off-target effects. Such therapeutic molecules can include antigen-recognition molecules such as T cell Receptors (TCRs), chimeric antigen receptors (CARs), and antibodies or antigen-binding fragments thereof. In various embodiments of the present disclosure, the antigen-recognition molecule is present in a solution. In various embodiments of the present disclosure, the antigen-recognition molecule is present on a cell (e.g., T cell, B cell or hybridoma). In certain embodiments, the antigen-recognition molecule is present on a heterologous cell. For example, a heterologous host cell may be genetically modified to express the antigen recognition molecule as is known in the art. In one aspect, the disclosure provides an in vitro method of assessing off-target effects of an antigen-recognition molecule, said method comprising a) contacting the antigen-recognition molecule with a target peptide presented in a complex with a major histocompatibility complex (MHC) molecule (MHC-target peptide complex); b) contacting the antigen-recognition molecule with one or more off-target peptides associated with said target peptide, wherein each of said off- target peptides is presented in a complex with the same MHC molecule as in a) (MHC-off-target peptide complex); and c) determining and comparing the level of binding of the antigen- recognition molecule to MHC-target peptide complex and each of the MHC-off-target peptide complexes. In one aspect, the disclosure provides an in vitro method of assessing off-target effects of an antigen-recognition molecule, comprising a) contacting the antigen-recognition molecule with one or more off-target peptides associated with a target peptide that is recognized by the antigen-
Attorney Docket #: 250298.000961 recognition molecule, wherein each of said off-target peptides is presented in a complex with a major histocompatibility complex (MHC) molecule (MHC-off-target peptide complex); and b) determining the level of binding of the antigen-recognition molecule to each of the MHC-off-target peptide complexes. In one aspect, the disclosure provides an in vitro method of assessing off-target effects of an antigen-recognition molecule that targets MAGEA3168-176 and/or WT1126-134 peptide, comprising a) contacting the antigen-recognition molecule with MAGEA3168-176 and/or WT1126- 134 peptide presented in a complex with a major histocompatibility complex (MHC) molecule, or a fragment or derivative thereof (MHC-MAGEA3168-176 and/or MHC-WT1126-134 peptide complex); b) contacting the antigen-recognition molecule with one or more off-target peptides, wherein each of said off-target peptides (i) comprises an amino acid sequence of any one of SEQ ID NOs: 30-47 and 200-232 and/or 233-240, or a pharmaceutically acceptable salt thereof, or a fragment or derivative thereof, wherein the peptide is 8-12 amino acids in length, and (ii) is presented in a complex with the same kind of MHC molecule, or the fragment or derivative thereof, as in (a) (MHC-off-target peptide complex); and c) determining the level of binding of the antigen- recognition molecule to the MHC-MAGEA3168-176 and/or MHC-WT1126-134 peptide complex and each of the MHC-off-target peptide complexes. In one aspect, the disclosure provides an in vitro method of assessing off-target effects of an antigen-recognition molecule that targets MAGEA3168-176 and/or WT1126-134 peptide, comprising a) contacting the antigen-recognition molecule with MAGEA3168-176 and/or WT1126- 134 peptide presented in a complex with a major histocompatibility complex (MHC) molecule, or a fragment or derivative thereof (MHC-MAGEA3168-176 and/or MHC-WT1126-134 peptide complex); b) contacting the antigen-recognition molecule with one or more off-target peptides, wherein each of said off-target peptides (i) comprises an amino acid sequence of any one of SEQ ID NOs: 30-47 and 200-232 and/or 233-240, or a pharmaceutically acceptable salt thereof, or a fragment or derivative thereof, wherein the peptide is 8-12 amino acids in length, and (ii) is presented in a complex with the same kind of MHC molecule, or the fragment or derivative thereof, as in (a) (MHC-off-target peptide complex); and c) determining the binding affinity of the antigen- recognition molecule to the MHC-MAGEA3168-176 and/or MHC-WT1126-134 peptide complex and each of the MHC-off-target peptide complexes.
Attorney Docket #: 250298.000961 In one aspect, the disclosure provides an in vitro method of assessing off-target effects of an antigen-recognition molecule that targets MAGEA3168-176 and/or WT1126-134, comprising a) contacting the antigen-recognition molecule with one or more off-target peptides, wherein each of said off-target peptides comprises an amino acid sequence of any one of SEQ ID NOs: 30-47 and 200-232 and/or 233-240, or a pharmaceutically acceptable salt thereof, or a fragment or derivative thereof, wherein the peptide is 8-12 amino acids in length, and wherein each of said off-target peptides is presented in one of the following forms: (1) as a complex with a major histocompatibility complex (MHC) molecule, or a fragment or derivative thereof (MHC-off-target peptide complex), (2) as a peptide, or (3) as a protein or fragment thereof comprising said peptide; and b) determining the level of binding of the antigen-recognition molecule to said one or more MHC-off-target peptide complexes, off-target peptides or proteins or fragments thereof comprising said off-target peptides. In one aspect, the disclosure provides an in vitro method of assessing off-target effects of an antigen-recognition molecule that targets MAGEA3168-176 and/or WT1126-134, comprising a) contacting the antigen-recognition molecule with one or more off-target peptides, wherein each of said off-target peptides comprises an amino acid sequence of any one of SEQ ID NOs: 30-47 and 200-232 and/or 233-240, or a pharmaceutically acceptable salt thereof, or a fragment or derivative thereof, wherein the peptide is 8-12 amino acids in length, and wherein each of said off-target peptides is presented in one of the following forms: (1) as a complex with a major histocompatibility complex (MHC) molecule, or a fragment or derivative thereof (MHC-off-target peptide complex), (2) as a peptide, or (3) as a protein or fragment thereof comprising said peptide; and b) determining the binding affinity of the antigen-recognition molecule to said one or more MHC-off-target peptide complexes, off-target peptides or proteins or fragments thereof comprising said off-target peptides. In some embodiments of the above-described in vitro methods of assessing off-target effects of an antigen-recognition molecule that targets MAGEA3168-176, the MAGEA3168-176 peptide comprises the amino acid sequence of EVDPIGHLY (SEQ ID NO: 29) . In some embodiments of the in vitro methods, the MAGEA3168-176 peptide consists of the amino acid sequence EVDPIGHLY (SEQ ID NO: 29). In some embodiments of the above-described in vitro methods of assessing off-target effects of an antigen-recognition molecule that targets MAGEA3168-176, the off-target peptide
Attorney Docket #: 250298.000961 comprises an amino acid sequence of any one of SEQ ID NOs: 30-47 and 200-232. In some embodiments of the in vitro methods, the off-target peptide consists of an amino acid sequence of any one of SEQ ID NOs: 30-47 and 200-232. In some embodiments of the above-described in vitro methods of assessing off-target effects of an antigen-recognition molecule that targets MAGEA3168-176, the off-target peptide comprises an amino acid sequence of any one of SEQ ID NOs: 200-232. In some embodiments of the in vitro methods, the off-target peptide consists of an amino acid sequence of any one of SEQ ID NOs: 200-232. In some embodiments of the above-described in vitro methods of assessing off-target effects of an antigen-recognition molecule that targets MAGEA3168-176, the off-target peptide comprises an amino acid sequence of any one of SEQ ID NOs: 200-201. In some embodiments of the in vitro methods, the off-target peptide consists of an amino acid sequence of any one of SEQ ID NOs: 200-201. In some embodiments of the above-described in vitro methods of assessing off-target effects of an antigen-recognition molecule that targets MAGEA3168-176, the off-target peptide comprises an amino acid sequence of SEQ ID NO: 202-232. In some embodiments of the in vitro methods, the off-target peptide consists of an amino acid sequence of SEQ ID NO: 202-232. In some embodiments of the above-described in vitro methods of assessing off-target effects of an antigen-recognition molecule that targets MAGEA3168-176, the off-target peptide comprises an amino acid sequence of SEQ ID NO: 202-230. In some embodiments of the in vitro methods, the off-target peptide consists of an amino acid sequence of SEQ ID NO: 202-230. In some embodiments of the above-described in vitro methods of assessing off-target effects of an antigen-recognition molecule that targets MAGEA3168-176, the off-target peptide comprises an amino acid sequence of SEQ ID NO: 209, 231, and 232. In some embodiments of the in vitro methods, the off-target peptide consists of an amino acid sequence of SEQ ID NO: 209, 231, and 232. In some embodiments of the above-described in vitro methods of assessing off-target effects of an antigen-recognition molecule that targets WT1126-134, the WT1126-134 peptide comprises the amino acid sequence of RMFPNAPYL (SEQ ID NO: 241) . In some embodiments of the in vitro methods, the WT1126-134 peptide consists of the amino acid sequence RMFPNAPYL (SEQ ID NO: 241).
Attorney Docket #: 250298.000961 In some embodiments of the above-described in vitro methods of assessing off-target effects of an antigen-recognition molecule that targets WT1126-134, the off-target peptide comprises an amino acid sequence of any one of SEQ ID NOs: 233-240. In some embodiments of the in vitro methods, the off-target peptide consists of an amino acid sequence of any one of SEQ ID NOs: 233-240. In some embodiments of the in vitro methods described above, the level of binding is determined by detecting the amount of antigen-recognition molecules bound to the MHC-peptide complexes. For example, if a signal correlated to binding of an antigen-recognition molecule to an MHC peptide complex has a S:N ratio of at least about 2.0, 2.5, 3.0, 3.5, 4.0, or 5.0 then the antigen-recognition molecule may be determined to exhibit detectable binding to the MHC peptide complex. Noise levels (N) may be determined, for example, by measuring a signal/level of binding to a cell with no peptide and/or with no primary/capture antibody/agent (i.e., with only a detection antibody/agent). If an antigen-recognition molecule specifically binds to one or more MHC-off- target peptide complexes, then the antigen-recognition molecule may exhibit off-target effects. In some specific embodiments, a S:N ratio of at least about 3 is indicative of detectable binding. In some embodiments, binding affinity may be determined. Binding affinity may be determined by measuring an equilibrium dissociation constant (KD) of the binding reaction. Alternatively, binding affinity may be characterized using other methods. In some embodiments, the in vitro methods include determining that the antigen- recognition molecule is likely to have off-target effects if it detectably binds to at least one MHC- off-target peptide complex, wherein the off-target peptide is expressed in essential, normal tissues. In some embodiments, the in vitro methods include determining that the antigen-recognition molecule is likely to have off-target effects if it detectably binds to at least one MHC-off-target peptide complex, wherein the off-target peptide is expressed in diseased tissues. In some embodiments, the in vitro methods include determining that an antigen- recognition molecule is likely to have off-target effects if it detectably binds to at least one MHC- off-target peptide complex, off-target peptide or protein or fragment thereof, comprising said off- target peptide. In some embodiments, the antigen-recognition molecule is likely to have off-target effects if it detectably binds to at least one MHC-off-target peptide complex, or off-target peptide
Attorney Docket #: 250298.000961 or protein or fragment thereof comprising said off-target peptide, when the off-target peptide is present on a surface of a cell at a copy number of at least about 500 to about 10,000 copies/cell. In some embodiments, the antigen-recognition molecule is likely to have off-target effects if it detectably binds to at least one MHC-off-target peptide complex, or off-target peptide or protein or fragment thereof comprising said off-target peptide, when the off-target peptide is present on a surface of a cell at a copy number of at least about 500, at least about 600, at least about 700, at least about 800, or at least about 900 copies/cell. In some embodiments, the antigen-recognition molecule is likely to have off-target effects if it detectably binds to at least one MHC-off-target peptide complex, or off-target peptide or protein or fragment thereof comprising said off-target peptide, when the off-target peptide is present on a surface of a cell at a copy number of at least about 1,000, at least about 2,000, at least about 3,000, at least about 4,000, at least about 5,000, at least about 6,000, at least about 7,000, at least about 8,000, at least about 9,000, or at least about 10,000 copies/cell. In some embodiments, the antigen-recognition molecule is likely to have off-target effects if it detectably binds to at least one MHC-off-target peptide complex, or off-target peptide or protein or fragment thereof comprising said off-target peptide, when the off-target peptide is present on a surface of a cell at a copy number of at least about at least about 550 to about 9,800, at least about 600 to about 9,600, at least about 650 to about 9,400, at least about 700 to about 9,200, at least about 750 to about 9,000, at least about 800 to about 8,800, at least about 850 to about 8,600, at least about 900 to about 8,400, at least about 950 to about 8,200, at least about 1000 to about 8,000, at least about 1,050 to about 7,800, at least about 1,100 to about 7,600, at least about 1,150 to about 7,400, at least about 1,200 to about 7,200, at least about 1,250 to about 7,000, at least about 1,300 to about 6,800, at least about 1,350 to about 6,600, at least about 1,400 to about 6,400, at least about 1,450 to about 6,200, or at least about 1,500 to about 6,000 copies/cell. In some embodiments, the antigen-recognition molecule is likely to have off-target effects if it detectably binds to at least one MHC-off-target peptide complex, or off-target peptide or protein or fragment thereof comprising said off-target peptide, when the off-target peptide is present on a surface of a cell at a copy number of at least about 1,000 to about 10,000, at least about 1,000 to about 9,500, at least about 1,000 to about 9,000, at least about 1,000 to about 8,500, at least about 1,000 to about 8,000, at least about 1,000 to about 7,500, at least about 1,000 to about
Attorney Docket #: 250298.000961 7,000, at least about 1,000 to about 6,500, at least about 1,000 to about 6,000, at least about 1,000 to about 5,500, at least about 1,000 to about 5,000, at least about 1,000 to about 4,500, at least about 1,000 to about 4,000, at least about 1,000 to about 3,500, at least about 1,000 to about 3,000, at least about 1,000 to about 2,500, at least about 1,000 to about 2,000, or at least about 1,000 to about 1,500 copies/cell. In some embodiments, the antigen-recognition molecule is likely to have off-target effects if it detectably binds to at least one MHC-off-target peptide complex, or off-target peptide or protein or fragment thereof comprising said off-target peptide, when the off-target peptide is present on a surface of a cell at a copy number of at least about 1,000, at least about 1,010, at least about 1,020, at least about 1,030, at least about 1,040, at least about 1,050, at least about 1,100, at least about 1,150, at least about 1,200, at least about 1,250, at least about 1,300, at least about 1,350, at least about 1,400, at least about 1,450, at least about 1,500, at least about 1,550, at least about 1,600, at least about 1,650, at least about 1,700, at least about 1,750, at least about 1,800, at least about 1,850, at least about 1,900, at least about 1,950, or at least about 2,000 copies/cell. In some embodiments, the antigen-recognition molecule is likely to have off-target effects if it detectably binds to at least one MHC-off-target peptide complex, or off-target peptide or protein or fragment thereof comprising said off-target peptide, when the off-target peptide is present on a surface of a cell at a copy number of at least about 1,000 copies/cell. In another aspect, the disclosure provides a method of selecting an antigen-recognition molecule that binds a target pMHC complex with minimal off-target effects. Such method can comprise a) contacting a plurality of antigen-recognition molecules with a target peptide presented in a complex with a major histocompatibility complex (MHC) molecule (MHC-target peptide complex); b) contacting the same plurality of antigen-recognition molecules with one or more off- target peptides associated with said target peptide, wherein each of said off-target peptides is presented in a complex with the same MHC molecule as in a) (MHC-off-target peptide complex); c) selecting one or more antigen-recognition molecules based at least in part on the number of MHC-off-target peptide complexes detectably bound by each of the antigen-recognition molecules; and d) optionally, repeating steps (a)-(c) using selected antigen-recognition molecules. In some embodiments, the method includes selecting the antigen-recognition molecule(s) that binds to the fewest MHC-off-target peptide complexes. In some embodiments, the selected antigen-recognition molecule(s) detectably bind no more than five (e.g., no more than four, no
Attorney Docket #: 250298.000961 more than three, no more than two, or no more than one) MHC-off-target peptide complexes, wherein the off-target peptides are expressed in essential, normal tissues. In some embodiments, the selected antigen-recognition molecule(s) do not detectably bind to any of the MHC-off-target peptide complexes, wherein the off-target peptides are expressed in essential, normal tissues. In some embodiments, if several selected antigen-recognition molecules bind to at least one MHC-off-target peptide complex, the method may also include a step of comparing the level of binding of the antigen-recognition molecule(s) to the MHC-target peptide complex versus to the MHC-off-target peptide complex(es). The antigen-recognition molecule(s) may likely bind to the MHC-target peptide complex stronger than to the MHC-off-target peptide complex(es). For example, the antigen-recognition molecule(s) may bind to the MHC-target peptide complex about 1000 times, about 500 times, about 200 times, about 100 times, about 90 times, about 80 times, about 70 times, about 60 times, about 50 times, about 40 times, about 30 times, about 20 times, or about 10 times stronger than to the MHC-off-target peptide complex(es). The method may further include selecting the antigen-recognition molecule(s) based at least in part on the MHC-target peptide complex/MHC-off-target peptide complex binding ratio for one or more off-target peptides, wherein a higher MHC-target peptide complex/MHC-off-target peptide complex binding ratio is more desirable. In some embodiments, selection of antigen-recognition molecules may take into consideration the level of binding of the plurality of antigen-recognition molecules to the MHC- target peptide complex, wherein stronger binding to the MHC-target peptide complex is more desirable. Accordingly, the method may include selecting the antigen-recognition molecule(s) based at least in part on the level of binding to the MHC-target peptide complex. In some embodiments, the selected antigen-recognition molecules(s) bind to at least one potential secondary target peptide in addition to the MHC-target peptide complex. In another aspect, the disclosure provides a method for selecting an antigen-recognition molecule that targets MAGEA3168-176 and/or WT1126-134 peptide, comprising a) contacting a plurality of antigen-recognition molecules with MAGEA3168-176 and/or WT1126-134 peptide presented in a complex with a major histocompatibility complex (MHC) molecule, or a fragment or derivative thereof (MHC-MAGEA3168-176 and/or MHC-WT1126-134peptide complex) or presented as MAGEA3168-176 and/or WT1126-134 peptide or MAGEA3 and/or WT1 protein or
Attorney Docket #: 250298.000961 fragment thereof comprising said MAGEA3168-176 and/or WT1126-134 peptide; b) contacting the same plurality of antigen-recognition molecules with one or more off-target peptides, (i) wherein each of said off-target peptides comprises an amino acid sequence of any one of SEQ ID NOs: 30- 47 and 200-232 and/or 233-240, or a pharmaceutically acceptable salt thereof, or a fragment or derivative thereof, wherein the peptide is 8-12 amino acids in length, and (ii) wherein each of said off-target peptides is presented in one of the following forms: (1) as a complex with the same kind of MHC molecule, or the fragment or derivative thereof as in (a) (MHC-off-target peptide complex), (2) as a peptide, or (3) as a protein or fragment thereof comprising said off-target peptide; c) selecting one or more antigen-recognition molecules based on their ability to detectably bind to MAGEA3168-176 and/or WT1126-134 peptide complex or MAGEA3168-176 and/or WT1126-134 peptide or MAGEA3 and/or WT1 protein or fragment thereof comprising said MAGEA3168-176 and/or WT1126-134 peptide and based at least in part on the number of MHC-off-target peptide complexes, off-target peptides or proteins or fragments thereof comprising said off-target peptides detectably bound by each of the antigen-recognition molecules; and d) optionally, repeating steps (a)-(c) using the one or more selected antigen-recognition molecules. In another aspect, the disclosure provides a method for selecting an antigen-recognition molecule that specifically binds MAGEA3168-176 and/or WT1126-134 peptide, comprising a) contacting a plurality of antigen-recognition molecules with MAGEA3168-176 and/or WT1126-134 peptide presented in a complex with a major histocompatibility complex (MHC) molecule, or a fragment or derivative thereof (MHC-MAGEA3168-176 and/or MHC-WT1126-134 peptide complex) or presented as MAGEA3168-176 and/or WT1126-134 peptide or MAGEA3 and/or WT1 protein or fragment thereof comprising said MAGEA3168-176 and/or WT1126-134 peptide; b) contacting the same plurality of antigen-recognition molecules with one or more off-target peptides, (i) wherein each of said off-target peptides comprises an amino acid sequence of any one of SEQ ID NOs: 30- 47 and 200-232 and/or 233-240, or a pharmaceutically acceptable salt thereof, or a fragment or derivative thereof, wherein the peptide is 8-12 amino acids in length, and (ii) wherein each of said off-target peptides is presented in one of the following forms: (1) as a complex with the same kind of MHC molecule, or the fragment or derivative thereof as in (a) (MHC-off-target peptide complex), (2) as a peptide, or (3) as a protein or fragment thereof comprising said off-target peptide; c) selecting one or more antigen-recognition molecules based on their ability to detectably bind to MAGEA3168-176 and/or WT1126-134 peptide complex or MAGEA3168-176 and/or WT1126-134
Attorney Docket #: 250298.000961 peptide or MAGEA3 and/or WT1 protein or fragment thereof comprising said MAGEA3168-176 and/or WT1126-134 peptide and based at least in part on the number of MHC-off-target peptide complexes, off-target peptides or proteins or fragments thereof comprising said off-target peptides detectably bound by each of the antigen-recognition molecules; and d) optionally, repeating steps (a)-(c) using the one or more selected antigen-recognition molecules. In some embodiments of the above-described methods for selecting an antigen- recognition molecule that targets MAGEA3168-176 peptide, the MAGEA3168-176 peptide comprises the amino acid sequence of EVDPIGHLY (SEQ ID NO: 29). In some embodiments of the selecting methods, the MAGEA3168-176peptide consists of the amino acid sequence EVDPIGHLY (SEQ ID NO: 29). In some embodiments of the above-described methods for selecting an antigen- recognition molecule that targets MAGEA3168-176 peptide, the off-target peptide comprises an amino acid sequence of any one of SEQ ID NOs: 30-47 and 200-232. In some embodiments of the selecting methods, the off-target peptide consists of an amino acid sequence of any one of SEQ ID NOs: 30-47 and 200-232. In some embodiments of the above-described methods for selecting an antigen- recognition molecule that targets MAGEA3168-176 peptide, the off-target peptide comprises an amino acid sequence of any one of SEQ ID NOs: 200-232. In some embodiments of the selecting methods, the off-target peptide consists of an amino acid sequence of any one of SEQ ID NOs: 200-232. In some embodiments of the above-described methods for selecting an antigen- recognition molecule that targets MAGEA3168-176 peptide, the off-target peptide comprises an amino acid sequence of any one of SEQ ID NOs: 200-201. In some embodiments of the selecting methods, the off-target peptide consists of an amino acid sequence of any one of SEQ ID NOs: 200-201. In some embodiments of the above-described methods for selecting an antigen- recognition molecule that targets MAGEA3168-176 peptide, the off-target peptide comprises an amino acid sequence of SEQ ID NO: 202-232. In some embodiments of the selecting methods, the off-target peptide consists of an amino acid sequence of SEQ ID NO: 202-232. In some embodiments of the above-described methods for selecting an antigen- recognition molecule that targets MAGEA3168-176 peptide, the off-target peptide comprises an
Attorney Docket #: 250298.000961 amino acid sequence of SEQ ID NO: 202-230. In some embodiments of the selecting methods, the off-target peptide consists of an amino acid sequence of SEQ ID NO: 202-230. In some embodiments of the above-described methods for selecting an antigen- recognition molecule that targets MAGEA3168-176 peptide, the off-target peptide comprises an amino acid sequence of SEQ ID NO: 209, 231, and 232. In some embodiments of the selecting methods, the off-target peptide consists of an amino acid sequence of SEQ ID NO: 209, 231, and 232. In some embodiments of the above-described methods for selecting an antigen- recognition molecule that targets WT1126-134 peptide, the WT1126-134 peptide comprises the amino acid sequence of RMFPNAPYL (SEQ ID NO: 241). In some embodiments of the selecting methods, the WT1126-134 peptide consists of the amino acid sequence RMFPNAPYL (SEQ ID NO: 241). In some embodiments of the above-described methods for selecting an antigen- recognition molecule that targets WT1126-134 peptide, the off-target peptide comprises an amino acid sequence of any one of SEQ ID NOs: 233-240. In some embodiments of the selecting methods, the off-target peptide consists of an amino acid sequence of any one of SEQ ID NOs: 233-240. In some embodiments of the above-described methods for selecting an antigen- recognition molecule that specifically binds MAGEA3168-176 and/or WT1126-134 peptide, each of said one or more off-target peptides is presented as an MHC-off-target peptide complex, or a fragment or derivative thereof, on a surface of a cell at a copy number of at least about 500 to about 10,000 copies/cell. In some embodiments of the above-described methods for selecting an antigen- recognition molecule that specifically binds MAGEA3168-176 and/or WT1126-134 peptide, each of said one or more off-target peptides is presented as an MHC-off-target peptide complex, or a fragment or derivative thereof, on a surface of a cell at a copy number of at least about 500, at least about 600, at least about 700, at least about 800, or at least about 900 copies/cell. In some embodiments of the above-described methods for selecting an antigen- recognition molecule that specifically binds MAGEA3168-176 and/or WT1126-134 peptide, each of said one or more off-target peptides is presented as an MHC-off-target peptide complex, or a fragment or derivative thereof, on a surface of a cell at a copy number of at least about 1,000, at
Attorney Docket #: 250298.000961 least about 2,000, at least about 3,000, at least about 4,000, at least about 5,000, at least about 6,000, at least about 7,000, at least about 8,000, at least about 9,000, or at least about 10,000 copies/cell. In some embodiments of the above-described methods for selecting an antigen- recognition molecule that specifically binds MAGEA3168-176 and/or WT1126-134peptide, each of said one or more off-target peptides is presented as an MHC-off-target peptide complex, or a fragment or derivative thereof, on a surface of a cell at a copy number of a cell at a copy number of at least about at least about 550 to about 9,800, at least about 600 to about 9,600, at least about 650 to about 9,400, at least about 700 to about 9,200, at least about 750 to about 9,000, at least about 800 to about 8,800, at least about 850 to about 8,600, at least about 900 to about 8,400, at least about 950 to about 8,200, at least about 1000 to about 8,000, at least about 1,050 to about 7,800, at least about 1,100 to about 7,600, at least about 1,150 to about 7,400, at least about 1,200 to about 7,200, at least about 1,250 to about 7,000, at least about 1,300 to about 6,800, at least about 1,350 to about 6,600, at least about 1,400 to about 6,400, at least about 1,450 to about 6,200, or at least about 1,500 to about 6,000 copies/cell. In some embodiments of the above-described methods for selecting an antigen- recognition molecule that specifically binds MAGEA3168-176 and/or WT1126-134peptide, each of said one or more off-target peptides is presented as an MHC-off-target peptide complex, or a fragment or derivative thereof, on a surface of a cell at a copy number of at least about 1,000 to about 10,000, at least about 1,000 to about 9,500, at least about 1,000 to about 9,000, at least about 1,000 to about 8,500, at least about 1,000 to about 8,000, at least about 1,000 to about 7,500, at least about 1,000 to about 7,000, at least about 1,000 to about 6,500, at least about 1,000 to about 6,000, at least about 1,000 to about 5,500, at least about 1,000 to about 5,000, at least about 1,000 to about 4,500, at least about 1,000 to about 4,000, at least about 1,000 to about 3,500, at least about 1,000 to about 3,000, at least about 1,000 to about 2,500, at least about 1,000 to about 2,000, or at least about 1,000 to about 1,500 copies/cell. In some embodiments of the above-described methods for selecting an antigen- recognition molecule that specifically binds MAGEA3168-176 and/or WT1126-134 peptide, each of said one or more off-target peptides is presented as an MHC-off-target peptide complex, or a fragment or derivative thereof, on a surface of a cell at a copy number of at least about 1,000, at least about 1,010, at least about 1,020, at least about 1,030, at least about 1,040, at least about
Attorney Docket #: 250298.000961 1,050, at least about 1,100, at least about 1,150, at least about 1,200, at least about 1,250, at least about 1,300, at least about 1,350, at least about 1,400, at least about 1,450, at least about 1,500, at least about 1,550, at least about 1,600, at least about 1,650, at least about 1,700, at least about 1,750, at least about 1,800, at least about 1,850, at least about 1,900, at least about 1,950, or at least about 2,000 copies/cell. In some embodiments of the above-described methods for selecting an antigen- recognition molecule that specifically binds MAGEA3168-176 and/or WT1126-134 peptide, each of said one or more off-target peptides is presented as an MHC-off-target peptide complex, or a fragment or derivative thereof, on a surface of a cell at a copy number of at least about 1,000 copies/cell. In some embodiments, the one or more selected antigen-recognition molecules detectably bind no more than five MHC-off-target peptide complexes, off-target peptides or proteins or fragments thereof comprising said off-target peptides. In some embodiments, the one or more selected antigen-recognition molecules do not detectably bind to any of the tested MHC-off-target peptide complexes, off-target peptides or proteins or fragments thereof comprising said off-target peptides. In some embodiments, the one or more selected antigen-recognition molecules do not detectably bind to any of the tested MHC-off-target peptide complexes, or off-target peptides or proteins or fragments thereof comprising said off-target peptides, when the off-target peptide is present on a surface of a cell at a copy number of at least about 500 to about 10,000 copies/cell. In some embodiments, the one or more selected antigen-recognition molecules do not detectably bind to any of the tested MHC-off-target peptide complexes, or off-target peptides or proteins or fragments thereof comprising said off-target peptides, when the off-target peptide is present on a surface of a cell at a copy number of at least about 500, at least about 600, at least about 700, at least about 800, or at least about 900 copies/cell. In some embodiments, the one or more selected antigen-recognition molecules do not detectably bind to any of the tested MHC-off-target peptide complexes, or off-target peptides or proteins or fragments thereof comprising said off-target peptides, when the off-target peptide is present on a surface of a cell at a copy number of a cell at a copy number of at least about 1,000, at least about 2,000, at least about 3,000, at least about 4,000, at least about 5,000, at least about
Attorney Docket #: 250298.000961 6,000, at least about 7,000, at least about 8,000, at least about 9,000, or at least about 10,000 copies/cell. In some embodiments, the one or more selected antigen-recognition molecules do not detectably bind to any of the tested MHC-off-target peptide complexes, or off-target peptides or proteins or fragments thereof comprising said off-target peptides, when the off-target peptide is present on a surface of a cell at a copy number of at least about at least about 550 to about 9,800, at least about 600 to about 9,600, at least about 650 to about 9,400, at least about 700 to about 9,200, at least about 750 to about 9,000, at least about 800 to about 8,800, at least about 850 to about 8,600, at least about 900 to about 8,400, at least about 950 to about 8,200, at least about 1000 to about 8,000, at least about 1,050 to about 7,800, at least about 1,100 to about 7,600, at least about 1,150 to about 7,400, at least about 1,200 to about 7,200, at least about 1,250 to about 7,000, at least about 1,300 to about 6,800, at least about 1,350 to about 6,600, at least about 1,400 to about 6,400, at least about 1,450 to about 6,200, or at least about 1,500 to about 6,000 copies/cell. In some embodiments, the one or more selected antigen-recognition molecules do not detectably bind to any of the tested MHC-off-target peptide complexes, or off-target peptides or proteins or fragments thereof comprising said off-target peptides, when the off-target peptide is present on a surface of a cell at a copy number of at least about 1,000 to about 10,000, at least about 1,000 to about 9,500, at least about 1,000 to about 9,000, at least about 1,000 to about 8,500, at least about 1,000 to about 8,000, at least about 1,000 to about 7,500, at least about 1,000 to about 7,000, at least about 1,000 to about 6,500, at least about 1,000 to about 6,000, at least about 1,000 to about 5,500, at least about 1,000 to about 5,000, at least about 1,000 to about 4,500, at least about 1,000 to about 4,000, at least about 1,000 to about 3,500, at least about 1,000 to about 3,000, at least about 1,000 to about 2,500, at least about 1,000 to about 2,000, or at least about 1,000 to about 1,500 copies/cell. In some embodiments, the one or more selected antigen-recognition molecules do not detectably bind to any of the tested MHC-off-target peptide complexes, or off-target peptides or proteins or fragments thereof comprising said off-target peptides, when the off-target peptide is present on a surface of a cell at a copy number of at least about 1,000, at least about 1,010, at least about 1,020, at least about 1,030, at least about 1,040, at least about 1,050, at least about 1,100, at least about 1,150, at least about 1,200, at least about 1,250, at least about 1,300, at least about
Attorney Docket #: 250298.000961 1,350, at least about 1,400, at least about 1,450, at least about 1,500, at least about 1,550, at least about 1,600, at least about 1,650, at least about 1,700, at least about 1,750, at least about 1,800, at least about 1,850, at least about 1,900, at least about 1,950, or at least about 2,000 copies/cell. In some embodiments, the one or more selected antigen-recognition molecules do not detectably bind to any of the tested MHC-off-target peptide complexes, or off-target peptides or proteins or fragments thereof comprising said off-target peptides, when the off-target peptide is present on a surface of a cell at a copy number of at least about 1,000 copies/cell. In some embodiments, the plurality of antigen-recognition molecules is in a library. The library can be, without limitation, a phage display library or a yeast library. In some embodiments, one or more of the MAGEA3168-176 and/or WT1126-134 peptide, MAGEA3 and/or WT1 protein or fragment thereof comprising said MAGEA3168-176 and/or WT1126-134 peptide, MHC- MAGEA3168-176 and/or MHC-WT1126-134 peptide complexes, MHC-off- target peptide complexes, off-target peptides, or proteins or fragments thereof comprising said off- target peptides are immobilized on a solid support. In some embodiments, one or more of the MAGEA3168-176 and/or WT1126-134 peptide, MAGEA3 and/or WT1 protein or fragment thereof comprising said MAGEA3168-176 and/or WT1126-134 peptide, MHC- MAGEA3168-176 and/or MHC-WT1126-134 peptide complexes, MHC-off- target peptide complexes, off-target peptides, or proteins or fragments thereof comprising said off- target peptides are present on cells (e.g., on the surface of the cells). As a non-limiting example, off-target peptides can be present on cancer cells (e.g., tumor cells), lung cells, liver cells, brain cells, and/or cardiovascular cells. In some embodiments, off-target peptides can be present on cancer cells and/or an antigen-binding molecule targeting MAGEA3168-176 and/or WT1126-134 may be screened for binding and/or reactivity against cancer cells or otherwise immortalized cell lines. In some embodiments, the cancer is a hematologic malignancy, for example, a lymphoma, a leukemia, or a myeloma. In some embodiments, the lymphoma is Hodgkin's lymphoma or non-Hodgkin’s lymphoma. In some embodiments, the leukemia is chronic lymphocytic leukemia (CLL), acute lymphocytic leukemia (ALL), chronic myeloid leukemia (CML), or acute myeloid leukemia (AML). In some embodiments, the cancer is a solid cancer. In one embodiment, the cancer is a head and neck cancer. In one embodiment, the cancer is a renal cell carcinoma. In one embodiment, the cancer is a breast cancer, e.g., triple-negative breast cancer (TNBC). In one embodiment, the cancer is a
Attorney Docket #: 250298.000961 non-small cell lung cancer (NSCLC), for example, lung adenocarcinoma. In one embodiment, the cancer is liver hepatocellular carcinoma (HCC). In one embodiment, the cancer is lung squamous cell carcinoma (SCC). In one embodiment, the cancer is bladder cancer. In one embodiment, the cancer is esophageal cancer. In one embodiment, the cancer is uveal melanoma. In one embodiment, the cancer is nasopharyngeal cancer. In one embodiment, the cancer is synovial sarcoma. In one embodiment, the cancer is ovarian cancer. In one embodiment, the cancer is uterine cancer. In one embodiment, the cancer is endometrial cancer. In one embodiment, the cancer is melanoma. In some embodiments, the cancer cell is derived from a tumor cell line (e.g., an endogenous cell line). Non-limiting examples of tumor cell lines include U-87 MG, T98G, NCI- H2023.PRAME-KO, HEP-G2, U-251 MG, NCI-H1915.HLA-A*02:01, OVCAR-3, and NCI- H2023. In some embodiments, the off-target peptides of the present disclosure are present on primary normal cells, which can be derived, e.g., from brain or vasculature, and/or an antigen- binding molecule targeting MAGEA3168-176 and/or WT1126-134 may be screened for binding and/or reactivity against primary normal cells (e.g., from brain or vasculature). Non-limiting examples of normal cell lines derived from brain include astrocytes (HA-1 cells), brain microvascular endothelial cells (HBMEC-3 cells), and brain vascular pericityes (HBVP-1 cells). Non-limiting examples of normal cell lines derived from vasculature include coronary artery smooth muscle cells (HCASMC-1 cells) and aortic smooth muscle cells (HAoSMC-1 cells). In some embodiments, the off-target peptides of the present disclosure are present on cardiovascular tissue or cells isolated therefrom and/or an antigen-binding molecule targeting MAGEA3168-176 and/or WT1126-134 may be screened for binding and/or reactivity against cardiovascular tissue or cells isolated therefrom. Such cardiovascular tissue or cells may be derived from, without limitation, coronary artery (with or without plaques), aorta, or ventricle. In some embodiments, the cardiovascular cells can comprise, e.g., cardiomyocytes, endothelial cells, smooth muscle cells, fibroblasts, pericytes, and/or pacemaker cells. In some embodiments, the off-target peptides of the present disclosure are present on cells and/or an antigen-binding molecule targeting MAGEA3168-176 and/or WT1126-134 may be screened for binding and/or reactivity against cells, which may be part of or sampled from tissues of a subject having a disease, disorder, or condition such as, but not limited to, a cardiovascular disease, disorder, or condition disclosed herein (e.g., myocardial infarction, heart failure, cardiac
Attorney Docket #: 250298.000961 insufficiency, coronary insufficiency, pulmonary edema, atherosclerosis, etc.) or a cancer disclosed herein. In some embodiments, the off-target peptides of the present disclosure are present on cells which may be part of or sampled from a diseased tissue of a subject. For example, the cells can be cardiovascular cells which are part of or sampled from a diseased heart tissue such as, but not limited to, diseased ventricle, aorta, and/or coronary artery. In some embodiments, the cells may be part of or sampled from tissues of a subject not having a disease, disorder or condition. In some embodiments, the cells may be part of sampled from a normal tissue of a subject. In various embodiments, the cells are part of or sampled from, for example, without limitation, heart tissue, brain tissue, lung tissue, and/or liver tissue. The heart tissue, brain tissue, lung tissue, and/or liver tissue may be either diseased or normal. In some embodiments, one or more of the MAGEA3168-176 and/or WT1126-134, MAGEA3 and/or WT1 protein or fragment thereof comprising said MAGEA3168-176 and/or WT1126-134, MHC- MAGEA3168-176 and/or MHC-WT1126-134 peptide complexes, MHC-off-target peptide complexes, off-target peptides, or proteins or fragments thereof comprising said off-target peptides are present in a soluble form. In some embodiments, the level of binding is determined by detecting the amount of antigen-recognition molecules bound to the MAGEA3168-176 and/or WT1126-134 peptide, MAGEA3 and/or WT1 protein or fragment thereof comprising said MAGEA3168-176 and/or WT1126-134 peptide, MHC- MAGEA3168-176 and/or MHC-WT1126-134 peptide complexes, MHC-off-target peptide complexes, off-target peptides, or proteins or fragments thereof comprising said off-target peptides. In some embodiments of any of the above-described methods, the method is performed in a high-throughput format. In some embodiments, the method is performed in a multi-well plate (e.g., a 96-well plate). Methods to determine binding to pMHC complexes include, for example, surface plasmon resonance (e.g., BIACORETM), or any other biosensor technique, enzyme-linked immunosorbent assay (ELISA), ELISpot, luminescence assay, flow cytometry, chromatography, microscopy, or any suitable immunoassay. Alternatively, or in addition, binding may be determined by functional assays in which a biological response is detected upon binding, for example, cytokine release or cell apoptosis.
Attorney Docket #: 250298.000961 For example, antibodies and TCRs may be obtained from display libraries in which a pMHC complex of the disclosure (e.g., a target pMHCs, such as an MHC-MAGEA3168-176 and/or MHC-WT1126-134 peptide complex) is used to pan the library. TCRs can be displayed on the surface of phage particles and yeast particles, for example, and such libraries have been used for the isolation of high affinity variants of TCR derived from T cell clones. TCR phage libraries can be used to isolate TCRs with novel antigen specificity. Such libraries can be constructed with α- and β- chain sequences corresponding to those found in a natural repertoire. However, the random combination of these α- and β- chain sequences, which occurs during library creation, can produce a repertoire of TCRs that may not be naturally occurring. In some embodiments, a pMHC complex of the disclosure may be used to screen a library of diverse TCRs displayed on the surface of phage particles. The TCRs displayed by said library may not correspond to those contained in a natural repertoire, for example, they may contain α- and β- chain pairing that would not be present in vivo, and/or the TCRs may contain non-natural mutations and/or the TCRs may be in soluble form. Screening may involve panning the phage library with pMHC complexes of the disclosure and subsequently isolating bound phage particles. For this purpose, pMHC complexes may be attached to a solid support, such as a magnetic bead, or column matrix and phage bound pMHC complexes isolated, with a magnet, or by chromatography, respectively. The panning steps may be repeated several times. Isolated phage may be further expanded in E. coli cells. Isolated phage particles may be tested for specific binding to pMHC complexes of the disclosure. Binding can be detected using techniques described herein such as, but not limited to, ELISA, or SPR for example using a BIACORETM instrument. The DNA sequence of the T cell receptor displayed by pMHC binding phage can be further identified by PCR methods. Various methods for generating and/or isolating antigen-recognition molecules, such as antibodies and TCRs, including those that target peptides presented as part of pMHC complexes are well known in the art. Antigen-recognition molecules may be generated by immunizing immunocompetent host animals, such as non-human host animals (e.g., rodents such as mice or rats) with antigens such as peptides or pMHC complexes. In certain implementations, the host animal may be genetically modified such that it generates human or humanized antigen recognition molecules in response to the immunization. For instance, TCRs may be generated by immunizing with antigenic peptide (e.g., a target peptide such as MAGEA3168-176 and/or WT1126-134) a rodent
Attorney Docket #: 250298.000961 genetically modified to express human or humanized TCRs and, optionally, to further express human or humanized MHC (e.g., MHC I α, MHC II α, and/or MHC II β), human or humanized β2 microglobulin (β2M), and/or human or humanized T Cell co receptor (e.g., CD4, CD8 α, and/or CD8 β). See, e.g., U.S. Pat. No. 11,259,510; U.S. Pub. No. 2022/0322648; Moore et al., Sci Immunol. 2021 Dec 17; 6(66):eabj4026, which are each herein incorporated by reference in their entirety. Likewise, antibodies, or more specifically TCR-mimetic antibodies, may be generated by immunizing with antigenic pMHC complexes (e.g., a target pMHC complex, such as an MHC- MAGEA3168-176 and/or MHC-WT1126-134 peptide complex) a rodent genetically modified to express human or humanized immunoglobulins (e.g., IgGs) and, optionally, further express human or humanized MHC (e.g., MHC I α, MHC II α, and/or MHC II β) and/or human or humanized β2 microglobulin (β2M). See, e.g., U.S. Pat No. 8,791,323; U.S. Pat. No. 8,642,835; U.S. Pat. No. 8,754,287; U.S. Pat. No. 10,143,186; U.S. Pat. Pub. No. 2019/0292263; Proc Natl Acad Sci U S A. 2014 Apr 8;111(14):5153-8, each of which is herein incorporated by reference in its entirety. Methods of screening antigen-recognition molecules (e.g., cells expressing antigen-recognition molecules such as T cells and B cells) and isolating and cloning sequences, such as variable domain coding sequences, are well known in the art. Alternatively, antigen binding T cells and TCRs can be isolated from fresh blood obtained from patients or healthy donors. Such a method involves stimulating T cells using autologous dendritic cells (DCs), followed by autologous B cells, and then pulsing with a target/off-target peptide disclosed herein. Several rounds of stimulation may be carried out, for example three or four rounds. Activated T cells may then be tested for recognition of the target/off-target peptide by measuring cytokine release in the presence of cells (e.g., T2 cells) loaded with the target/off- target peptide of the disclosure (for example using an IFNγ ELISpot assay). Activated cells may then be sorted by fluorescence-activated cell sorting (FACS) using labelled antibodies to detect intracellular cytokine production (e.g., IFNγ), or expression of a cell surface marker (such as CD137). Sorted cells may be expanded and further validated, for example, by ELISpot assay and/or cytotoxicity against target cells and/or staining by peptide-MHC multimer (e.g., tetramer). The TCR chains from validated clones may then be amplified by rapid amplification of cDNA ends (RACE) and sequenced. Exemplary methods for isolation and validation of TCRs specific to a target antigen are described in Moore et al., Sci. Immunol. 6, eabj4026 (2021); Deering et al.,
Attorney Docket #: 250298.000961 Sci Rep. 2023 May 25;13(1):8452; and U.S. Pat. Pub. No. 2021/0102942, each of which is herein incorporated by reference in its entirety. Antigen-recognition molecules may be screened for binding to one or more peptides (e.g., target and/or off-target peptides) according to various methods known in the art. The antigen- recognition molecules (e.g., antibodies, TCRs, CARs) and/or the one or more peptides may be presented in solution, presented on a cell surface (e.g., a reporter cell), or immobilized on a solid support, including the specific cells or solid supports described elsewhere herein. For example, the antigen-recognition molecule may be presented on a cell surface (e.g., on a heterologous host cell) or immobilized on a solid support and the one or more peptides may be presented in solution (e.g., as soluble pMHC complexes), presented on a cell surface (e.g., loaded onto MHC of a heterologous cell), or immobilized on a solid support. Similarly, the one or more peptides may be presented on a cell surface (e.g., loaded onto MHC of a heterologous cell) or immobilized on a solid support (e.g., as a pMHC complex) and the antigen-recognition molecule may be presented in solution (e.g., as soluble pMHC complexes), presented on a cell surface (e.g., on a heterologous host cell), or immobilized on a solid support. In some instances, antigen-recognition molecule binding to a peptide presented on a cell surface may be detected using a genetically modified reporter cell that emits a signal in response to the binding, including in cell-to-cell screening systems in which the antigen-recognition molecule and one or more peptides are presented on different cell surfaces. For example, TCR screening can be performed using TCR activation assays. For example, JRT3-T3.5 cells (ATCC TIB-153), a Jurkat subline lacking endogenous TCR surface expression may be utilized as described in Moore et al., Sci. Immunol. 6, eabj4026 (2021), which is herein incorporated by reference in its entirety. T cell receptor alpha (TCRA) and T cell receptor beta (TCRB) sequences of interest can be introduced into the cells by lentiviral transduction, and surface TCR+ cells can be sorted. Antigen-presenting cells (e.g., 293T cells) that are pulsed with a target peptide or off-target peptide can be incubated with the TCR-transduced or parental JRT3 cells. A readout (e.g., luciferase activity) is then measured as an indication of TCR-mediated activation. In some embodiments, monoclonal antibody screening can be performed with cells isolated from the spleens and lymphoid tissue harvested from mice with optimal titers using hybridoma and B cell sorting (BST) platforms. Counter-screening approaches using one or more off-target peptides can help to identify and eliminate B-cells and hybridomas that show cross-
Attorney Docket #: 250298.000961 reactivity with peptides that form pHLA complexes resembling the targeted complex. For example, antigen positive (Ag+) clones that have cross-reactivity to off-target peptides can be identified by examining cell supernatants for antibody binding to cells (e.g., T2 cells) pulsed with the target peptide or off-target peptides using a cell binding assay. As another example, Ag+ B- cells can be captured using a biotin-labeled HLA-target peptide complex in the presence of high concentrations of one or more unlabeled, HLA-off-target peptide complexes to enrich for antibodies specific for the HLA-target peptide complex. The antibody variable domains of Ag+ B- cells can be subsequently cloned as full-length mAbs and expressed (e.g., in CHO cells) for further screening. Antibody binding to target/off-target peptides can be determined using ELISA. For example, MHC-target peptide complexes or MHC-off-target peptide complexes can be coated onto a plate (e.g., 96-well microtiter plate). A sample comprising test antibodies can be added to the plate and the reaction can be incubated under a condition to allow binding to occur. The plate is then washed, and a secondary antibody can be then added to the plate to detect the antibodies bound to an MHC-peptide complex. Typically, the secondary antibody can produce a signal that is indicative of the amount of the antibodies bound to the MHC-peptide complex. In various embodiments of the methods described herein, pMHC complexes may be provided in soluble form, or may be immobilized by attachment to a suitable solid support. Examples of solid supports include, but are not limited to, a bead (e.g., a magnetic bead), a membrane, sepharose, a plate, a tube, a column. pMHC complexes may be attached to an ELISA plate, a magnetic bead, or a surface plasmon resonance biosensor chip. Methods of attaching pMHC complexes to a solid support are known to the skilled person, and include, for example, using an affinity binding pair, e.g., biotin and streptavidin, or antibodies and antigens. In some embodiments, pMHC complexes are labeled with biotin and attached to streptavidin-coated surfaces. In various embodiments of the methods described herein, pMHC complexes may be present on a cell (e.g., on a surface of a cell). Such a cell may be a mammalian cell, preferably a cell of the immune system, and a specialized antigen-presenting cell (APC) such as a dendritic cell or a B cell. Other preferred cells include T2 cells. Cells presenting the peptide or pMHC complex of the disclosure may be isolated, preferably in the form of a homogenous population, or provided in a substantially pure form. Such cells may be obtained by pulsing said cells with one or more
Attorney Docket #: 250298.000961 peptides (e.g., 2 to 10, 2 to 20, 2 to 30, 5 to 25, 5 to 20, or 10 to 15 peptides) of the disclosure, or genetically modifying the cells (via DNA or RNA transfer) to express one or more peptides (e.g., 2 to 10, 2 to 20, 2 to 30, 5 to 25, 5 to 20, or 10 to 15 peptides) of the disclosure. Pulsing involves incubating the cells with the peptide for several hours using peptide concentrations typically ranging from 10−5 to 10−12 M. Such cells may additionally be transduced with HLA molecules, such as HLA-A*02 (e.g., HLA-A*02:01) or HLA-A*01 (e.g.)HLA-A*01:01 to further induce presentation of the peptide(s). Cells may be produced recombinantly. In various embodiments of the methods described herein, the method is performed in a high-throughput format (e.g., a 96-well plate). In yet another aspect, provided herein is a method of enriching a sample for antigen- recognition molecules that specifically bind a target peptide, comprising (a) contacting a sample comprising a plurality of antigen-recognition molecules with the target peptide in the presence of one or more off-target peptides associated with said target peptide, wherein each of said target peptide and said one or more off-target peptides is presented in a complex with a major histocompatibility complex (MHC) molecule (MHC-target peptide complex or MHC-off-target peptide complex); and (b) enriching the sample by isolating the antigen-recognition molecules that are bound to the MHC-target peptide complex. The method may further comprise repeating steps (a)-(b) to further enrich the sample. In still yet another aspect, provided herein is a method of enriching a sample for antigen- recognition molecules that specifically bind MAGEA3168-176 and/or WT1126-134 peptide, comprising a) contacting a sample comprising a plurality of antigen-recognition molecules with MAGEA3168-176 and/or WT1126-134 peptide or MAGEA3 and/or WT1 protein or fragment thereof comprising said MAGEA3168-176 and/or WT1126-134 peptide or MAGEA3168-176 and/or WT1126-134 peptide presented in a complex with a major histocompatibility complex (MHC) molecule, or a fragment or derivative thereof (MHC- MAGEA3168-176 and/or MHC-WT1126-134 peptide complex) in the presence of one or more off-target peptides, wherein each of said off-target peptides (i) comprises an amino acid sequence of any one of SEQ ID Nos: 30-47 and 200-232 and/or 233-240, or a pharmaceutically acceptable salt thereof, or a fragment or derivative thereof, wherein the peptide is 8-12 amino acids in length, wherein each of said one or more off-target peptides is presented in one of the following forms: (1) as a complex with an MHC molecule, or a fragment or derivative thereof (MHC-off-target peptide complex), (2) as a peptide, or (3) as a protein or
Attorney Docket #: 250298.000961 fragment thereof comprising said off-target peptide; b) enriching the sample by isolating the antigen-recognition molecules that are detectably bound to said MAGEA3168-176 and/or WT1126-134 peptide or MAGEA3 and/or WT1 protein or fragment thereof comprising said MAGEA3168-176 and/or WT1126-134 peptide or to the MHC- MAGEA3168-176 and/or MHC-WT1126-134 peptide complex; and c) optionally, repeating steps (a)-(b) using the enriched sample. In some embodiments of the above-described methods of enriching a sample for antigen- recognition molecules that specifically bind MAGEA3168-176 peptide, the off-target peptide comprises an amino acid sequence of any one of SEQ ID NOs: 30-47 and 200-232. In some embodiments of the sample enrichment methods, the off-target peptide consists of an amino acid sequence of any one of SEQ ID NOs: 30-47 and 200-232. In some embodiments of the above-described methods of enriching a sample for antigen- recognition molecules that specifically bind MAGEA3168-176peptide, the off-target peptide comprises an amino acid sequence of any one of SEQ ID NOs: 200-232. In some embodiments of the sample enrichment methods, the off-target peptide consists of an amino acid sequence of any one of SEQ ID NOs: 200-232. In some embodiments of the above-described methods of enriching a sample for antigen- recognition molecules that specifically bind MAGEA3168-176 peptide, the off-target peptide comprises an amino acid sequence of any one of SEQ ID NOs: 200-201. In some embodiments of the sample enrichment methods, the off-target peptide consists of an amino acid sequence of any one of SEQ ID NOs: 200-201. In some embodiments of the above-described methods of enriching a sample for antigen- recognition molecules that specifically bind MAGEA3168-176 peptide, the off-target peptide comprises an amino acid sequence of SEQ ID NO: 202-232. In some embodiments of the sample enrichment methods, the off-target peptide consists of an amino acid sequence of any one of SEQ ID NO: 202-232. In some embodiments of the above-described methods of enriching a sample for antigen- recognition molecules that specifically bind MAGEA3168-176 peptide, the off-target peptide comprises an amino acid sequence of SEQ ID NO: 202-230. In some embodiments of the sample enrichment methods, the off-target peptide consists of an amino acid sequence of any one of SEQ ID NO: 202-230.
Attorney Docket #: 250298.000961 In some embodiments of the above-described methods of enriching a sample for antigen- recognition molecules that specifically bind MAGEA3168-176 peptide, the off-target peptide comprises an amino acid sequence of SEQ ID NO: 209, 231, and 232. In some embodiments of the sample enrichment methods, the off-target peptide consists of an amino acid sequence of any one of SEQ ID NO: 209, 231, and 232. In some embodiments of the above-described methods of enriching a sample for antigen- recognition molecules that specifically bind WT1126-134 peptide, the off-target peptide comprises an amino acid sequence of SEQ ID NO: 233-240. In some embodiments of the sample enrichment methods, the off-target peptide consists of an amino acid sequence of any one of SEQ ID NO: 233- 240. In some embodiments, said MAGEA3168-176 and/or WT1126-134 peptide or MAGEA3 and/or WT1 protein or fragment thereof comprising said MAGEA3168-176 and/or WT1126-134 peptide or MHC-MAGEA3168-176 and/or MHC-WT1126-134 peptide complex is present on a cell (e.g., on a surface of a cell) disclosed herein or solid support disclosed herein and said off-target peptides or proteins or fragments thereof comprising said off-target peptides or MHC-off-target peptide complexes are present in solution. In some embodiments, said MAGEA3168-176 and/or WT1126-134 peptide or MAGEA3 and/or WT1 protein or fragment thereof comprising said MAGEA3168-176 and/or WT1126-134 peptide or MHC- MAGEA3168-176 and/or MHC-WT1126-134 peptide complex is labeled and said off-target peptides or proteins or fragments thereof comprising said off-target peptides or MHC- off-target peptide complexes are not labeled or are labeled differently. In some embodiments of the above-described methods of enriching a sample for antigen- recognition molecules that specifically bind MAGEA3168-176 peptide, the MAGEA3168-176 peptide comprises the amino acid sequence EVDPIGHLY (SEQ ID NO: 29) . In some embodiments of the enriching methods, the MAGEA3168-176peptide consists of the amino acid sequence EVDPIGHLY (SEQ ID NO: 29). In some embodiments of the above-described methods of enriching a sample for antigen- recognition molecules that specifically bind MAGEA3168-176 peptide, the off-target peptide comprises an amino acid sequence of any one of SEQ ID NOs: 30-47 and 200-232. In some embodiments of the enriching methods, the off-target peptide consists of an amino acid sequence of any one of SEQ ID NOs: 30-47 and 200-232.
Attorney Docket #: 250298.000961 In some embodiments of the above-described methods of enriching a sample for antigen- recognition molecules that specifically bind MAGEA3168-176 peptide, the off-target peptide comprises an amino acid sequence of any one of SEQ ID NOs: 200-232. In some embodiments of the enriching methods, the off-target peptide consists of an amino acid sequence of any one of SEQ ID NOs: 200-232. In some embodiments of the above-described methods of enriching a sample for antigen- recognition molecules that specifically bind MAGEA3168-176 peptide, the off-target peptide comprises an amino acid sequence of any one of SEQ ID NOs: 200-201. In some embodiments of the enriching methods, the off-target peptide consists of an amino acid sequence of any one of SEQ ID NOs: 200-201. In some embodiments of the above-described methods of enriching a sample for antigen- recognition molecules that specifically bind MAGEA3168-176 peptide, the off-target peptide comprises an amino acid sequence of SEQ ID NO: 202-232. In some embodiments of the enriching methods, the off-target peptide consists of an amino acid sequence of SEQ ID NO: 202-232. In some embodiments of the above-described methods of enriching a sample for antigen- recognition molecules that specifically bind MAGEA3168-176 peptide, the off-target peptide comprises an amino acid sequence of SEQ ID NO: 202-230. In some embodiments of the enriching methods, the off-target peptide consists of an amino acid sequence of SEQ ID NO: 202-230. In some embodiments of the above-described methods of enriching a sample for antigen- recognition molecules that specifically bind MAGEA3168-176 peptide, the off-target peptide comprises an amino acid sequence of SEQ ID NO: 209, 231, and 232. In some embodiments of the enriching methods, the off-target peptide consists of an amino acid sequence of SEQ ID NO: 209, 231, and 232. In some embodiments of the above-described methods of enriching a sample for antigen- recognition molecules that specifically bind WT1126-134 peptide, the WT1126-134 peptide comprises the amino acid sequence RMFPNAPYL (SEQ ID NO: 241). In some embodiments of the enriching methods, the WT1126-134 peptide consists of the amino acid sequence EVDPIGHLY(SEQ ID NO: 29) RMFPNAPYL (SEQ ID NO: 241). In some embodiments of the above-described methods of enriching a sample for antigen- recognition molecules that specifically bind WT1126-134 peptide, the off-target peptide comprises an amino acid sequence of any one of SEQ ID NOs: 233-240. In some embodiments of the
Attorney Docket #: 250298.000961 enriching methods, the off-target peptide consists of an amino acid sequence of any one of SEQ ID NOs: 233-240. There are various ways that can allow isolation of the antigen-recognition molecules that are bound to the MHC-target peptide complex. For example, the MHC-target peptide complex may be present on antigen-presenting cells while the MHC-off-target peptide complexes are not present on antigen-presenting cells (e.g., in a soluble form). Alternatively, the MHC-target peptide complex may be immobilized on a solid support while the MHC-off-target peptide complexes may be soluble or immobilized to a different solid support. The MHC-target peptide complex and the MHC-off-target peptide complexes may also be differentially labeled such that specific detection and isolation of the MHC-target peptide complex and the antigen-recognition molecules bound thereon can be achieved. The antigen-recognition molecules may be eluted from the MHC-target peptide complex after isolation. Certain embodiments and implementations of the disclosed technology are described above with reference to block and flow diagrams of systems and methods and/or computer program products according to example embodiments or implementations of the disclosed technology. It will be understood that one or more blocks of the block diagrams and flow diagrams, and combinations of blocks in the block diagrams and flow diagrams, respectively, can be implemented by computer-executable program instructions. Likewise, some blocks of the block diagrams and flow diagrams may not necessarily need to be performed in the order presented, may be repeated, or may not necessarily need to be performed at all, according to some embodiments or implementations of the disclosed technology. These computer-executable program instructions may be loaded onto a general-purpose computer, a special-purpose computer, a processor, or other programmable data processing apparatus to produce a particular machine, such that the instructions that execute on the computer, processor, or other programmable data processing apparatus create means for implementing one or more functions specified in the flow diagram block or blocks. These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means that implement one or more functions specified in the flow diagram block or blocks.
Attorney Docket #: 250298.000961 As an example, embodiments or implementations of the disclosed technology may provide for a computer program product, including a computer-usable medium having a computer- readable program code or program instructions embodied therein, said computer-readable program code adapted to be executed to implement one or more functions specified in the flow diagram block or blocks. Likewise, the computer program instructions may be loaded onto a computer or other programmable data processing apparatus to cause a series of operational elements or steps to be performed on the computer or other programmable apparatus to produce a computer- implemented process such that the instructions that execute on the computer or other programmable apparatus provide elements or steps for implementing the functions specified in the flow diagram block or blocks. Accordingly, blocks of the block diagrams and flow diagrams support combinations of means for performing the specified functions, combinations of elements or steps for performing the specified functions, and program instruction means for performing the specified functions. It will also be understood that each block of the block diagrams and flow diagrams, and combinations of blocks in the block diagrams and flow diagrams, can be implemented by special-purpose, hardware-based computer systems that perform the specified functions, elements or steps, or combinations of special-purpose hardware and computer instructions. Certain implementations of the disclosed technology are described above with reference to customer devices that may include mobile computing devices. Those skilled in the art will recognize that there are several categories of mobile devices, generally known as portable computing devices that can run on batteries but are not usually classified as laptops. For example, mobile devices can include, but are not limited to portable computers, tablet PCs, internet tablets, PDAs, ultra-mobile PCs (UMPCs), wearable devices, and smart phones. Additionally, implementations of the disclosed technology can be utilized with internet of things (IoT) devices, smart televisions and media devices, appliances, automobiles, toys, and voice command devices, along with peripherals that interface with these devices. While certain embodiments of this disclosure have been described in connection with what is presently considered to be the most practical and various embodiments, it is to be understood that this disclosure is not to be limited to the disclosed embodiments, but on the contrary, is intended to cover various modifications and equivalent arrangements included within
Attorney Docket #: 250298.000961 the scope of the appended claims. Although specific terms are employed herein, they are used in a generic and descriptive sense only and not for purposes of limitation. This written description uses examples to disclose certain embodiments of the technology and also to enable any person skilled in the art to practice certain embodiments of this technology, including making and using any apparatuses or systems and performing any incorporated methods. The patentable scope of certain embodiments of the technology is defined in the claims, and may include other examples that occur to those skilled in the art. Such other examples are intended to be within the scope of the claims if they have structural elements that do not differ from the literal language of the claims, or if they include equivalent structural elements with insubstantial differences from the literal language of the claims. Exemplary Compositions In one aspect, the disclosure provides isolated peptides which can be identified, e.g., using any of various methods described herein. In some embodiments, the isolated peptide may be identified for a MAGEA3 and/or WT1 target peptide (e.g., for MAGEA3168-176 target EVDPIGHLY (SEQ ID NO: 29) and/or for WT1126-134 target RMFPNAPYL (SEQ ID NO: 241)) as described herein. In some embodiments, one or more isolated peptides of the present disclosure comprise an amino acid sequence that is at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 85%, at least about 90%, at least about 95%, or at least about 99% identical to the amino acid sequence of any one of SEQ ID NOs: 30-47 and 200- 232 and/or 233-240, or a pharmaceutically acceptable salt thereof, or a fragment or derivative thereof. In some embodiments, one or more isolated peptides of the present disclosure comprise an amino acid sequence of any one of SEQ ID NOs: 30-47 and 200-232 and/or 233-240. In some embodiments, one or more isolated peptides of the present disclosure consist essentially of an amino acid sequence of any one of SEQ ID NOs: 30-47 and 200-232 and/or 233-240. In some embodiments, the one or more isolated peptide consist of an amino acid sequence of any one of SEQ ID NOs: 30-47 and 200-232 and/or 233-240. In some embodiments, the one or more isolated peptides comprise a plurality of isolated peptides. In some embodiments, the one or more isolated peptides comprise two or more sequences selected from any one or SEQ ID NOs: 30-47 and 200- 232 and/or 233-240, or a pharmaceutically acceptable salt thereof, or a fragment or derivative thereof.
Attorney Docket #: 250298.000961 In some embodiments, one or more isolated peptides of the present disclosure may have been detected by mass spectrometry as being presented by an HLA molecule in a human tissue sample. In some embodiments, one or more isolated peptides of the present disclosure comprise an amino acid sequence that is at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 85%, at least about 90%, at least about 95%, or at least about 99% identical to the amino acid sequence of any one of SEQ ID NOs: 30, 31, 32, 39, 41, 43, 44, 45, and 47, or a pharmaceutically acceptable salt thereof, or a fragment or derivative thereof. In some embodiments, one or more isolated peptides of the present disclosure comprise an amino acid sequence of any one of SEQ ID NOs: 30, 31, 32, 39, 41, 43, 44, 45, and 47. In some embodiments, one or more isolated peptides of the present disclosure consist essentially of an amino acid sequence of any one of SEQ ID NOs: 30, 31, 32, 39, 41, 43, 44, 45, and 47. In some embodiments, the one or more isolated peptides comprise a plurality of isolated peptides. In some embodiments, the one or more isolated peptides consist of an amino acid sequence of any one of SEQ ID NOs: 30, 31, 32, 39, 41, 43, 44, 45, and 47. In some embodiments, the one or more isolated peptides comprise two or more sequences selected from any one or SEQ ID NOs: 30, 31, 32, 39, 41, 43, 44, 45, and 47, or a pharmaceutically acceptable salt thereof, or a fragment or derivative thereof. In some embodiments, one or more isolated peptides of the present disclosure may have been identified through, e.g., a broad bioinformatics screen and/or an X-scan analysis described herein. In some embodiments, one or more isolated peptides of the present disclosure comprise an amino acid sequence that is at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 85%, at least about 90%, at least about 95%, or at least about 99% identical to the amino acid sequence of any one of SEQ ID NOs: 30-47 and 200- 232 and/or 233-240, or a pharmaceutically acceptable salt thereof, or a fragment or derivative thereof. In some embodiments, one or more isolated peptides of the present disclosure comprise an amino acid sequence of any one of SEQ ID NOs: 30-47 and 200-232 and/or 233-240. In some embodiments, one or more isolated peptides of the present disclosure consist essentially of an amino acid sequence of any one of SEQ ID NOs: 30-47 and 200-232 and/or 233-240. In some embodiments, the one or more isolated peptides consist of an amino acid sequence of any one of SEQ ID NOs: 30-47 and 200-232 and/or 233-240. In some embodiments, the one or more isolated peptides comprise a plurality of isolated peptides. In some embodiments, the one or more isolated
Attorney Docket #: 250298.000961 peptides comprise two or more sequences selected from any one or SEQ ID NOs: 30-47 and 200- 232 and/or 233-240, or a pharmaceutically acceptable salt thereof, or a fragment or derivative thereof. In certain aspects, the present disclosure provides one or more isolated peptides comprising or consisting of an amino acid sequence of any one of SEQ ID NOs: 30-47 and 200- 232, or a pharmaceutically acceptable salt thereof, or a fragment or derivative thereof. In certain aspects, the present disclosure provides one or more isolated peptides comprising or consisting of an amino acid sequence of any one of SEQ ID NOs: 200-232, or a pharmaceutically acceptable salt thereof, or a fragment or derivative thereof. In certain aspects, the present disclosure provides one or more isolated peptides comprising or consisting of an amino acid sequence of any one of SEQ ID NOs: 200-201, or a pharmaceutically acceptable salt thereof, or a fragment or derivative thereof. In certain aspects, the present disclosure provides one or more isolated peptides comprising or consisting of an amino acid sequence of SEQ ID NOs: 202-232, or a pharmaceutically acceptable salt thereof, or a fragment or derivative thereof. In certain aspects, the present disclosure provides one or more isolated peptides comprising or consisting of an amino acid sequence of SEQ ID NOs: 202-230, or a pharmaceutically acceptable salt thereof, or a fragment or derivative thereof. In certain aspects, the present disclosure provides one or more isolated peptides comprising or consisting of an amino acid sequence of SEQ ID NOs: 209, 231, and 232, or a pharmaceutically acceptable salt thereof, or a fragment or derivative thereof. In certain aspects, the present disclosure provides one or more isolated peptides comprising or consisting of an amino acid sequence of SEQ ID NOs: 233-240, or a pharmaceutically acceptable salt thereof, or a fragment or derivative thereof. In certain aspects, the present disclosure provides one or more isolated peptides comprising or consisting of an amino acid sequence selected from any subset of sequences formed by identifying the common sequences within one or more sets of sequences disclosed herein. In some embodiments, the isolated peptide, or fragment or derivative thereof, is 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, or more, amino acids in length. In some embodiments, the isolated peptides of the disclosure may be about 8-12 amino acids in length. For example, an isolated peptide disclosed herein may be 8 amino acids, 9 amino acids, 10 amino acids, 11 amino acids, or 12 amino acids in length. In some embodiments, the isolated peptides of the disclosure may be about 12-20
Attorney Docket #: 250298.000961 amino acids in length. For example, an isolated peptide disclosed herein may be 12 amino acids, 13 amino acids, 14 amino acids, 15 amino acids, 16 amino acids, 17 amino acids, 18 amino acids, 19 amino acids, or 20 amino acids in length. In certain embodiments, the isolated peptide is 8-12 amino acids in length. In certain embodiments, the isolated peptide is 8-11 amino acids in length. In certain embodiments, the isolated peptide is 8-10 amino acids in length. In certain embodiments, the isolated peptide is 9-12 amino acids in length. In certain embodiments, the isolated peptide is 9-11 amino acids in length. In certain embodiments, the isolated peptide is 9-10 amino acids in length. In certain embodiments, the isolated peptide is 9 amino acids in length. Amino acids sequences derived disclosed herein may include naturally occurring proteogenic amino acids as well as non-proteogenic amino acids and non-naturally occurring amino acids such as amino acid analogs. In some embodiments, the amino acids that may be used in the practice of the present disclosure may include, for example, without limitation, naturally occurring proteogenic (L)-amino acids, their optical (D)-isomers, chemically modified amino acids, including, e.g., amino acid analogs such as, e.g., selenocysteine (Sec), penicillamine (3- mercapto-D-valine), pyroglutamic acid (5-oxoproline), etc., naturally occurring non-proteogenic amino acids such as norleucine, and chemically synthesized amino acids that have properties known in the art to be characteristic of an amino acid, and amino acid equivalents. Inserted amino acids and substituted amino acids may be naturally occurring amino acids or may be non-naturally occurring amino acids and, for example, may contain a non-natural side chain, and/or be linked together via non-native peptide bonds. Such altered peptide ligands are discussed further in Douat-Casassus et al., J. Med. Chem, 2007; 50(7):1598-609 and Hoppes et al., J. Immunol 2014; 193(10):4803-13 and references therein. If more than one amino acid residue is substituted and/or inserted, the replacement/inserted amino acid residues may be the same as each other or different from one another. Each replacement amino acid may have a different side chain to the amino acid being replaced. D-amino acids may be substituted for the L-amino acids in the peptides of the disclosure. In addition, non-standard amino acids (i.e., other than the common naturally occurring proteinogenic amino acids such as β-γ-δ-amino acids, as well as many derivatives of L-α-amino acids) may also be used for substitutions or additions to produce peptides of the present disclosure. One can also replace the naturally occurring side chains of the 20 genetically encoded amino acids (or the stereoisomeric D-amino acids) with other side chains, for instance with groups
Attorney Docket #: 250298.000961 such as alkyl, lower alkyl, cyclic 4-, 5-, 6-, to 7-membered alkyl, amide, amide lower alkyl, amide di(lower alkyl), lower alkoxy, hydroxy, carboxy and the lower ester derivatives thereof, and with 4-, 5-, 6-, to 7-membered heterocyclic. For example, proline analogues in which the ring size of the proline residue is changed from 5 members to 4, 6, or 7 members can be employed. Cyclic groups can be saturated or unsaturated, and if unsaturated, can be aromatic or non-aromatic. Heterocyclic groups preferably contain one or more nitrogen, oxygen, and/or sulfur heteroatoms. Examples of such groups include the furazanyl, furyl, imidazolidinyl, imidazolyl, imidazolinyl, isothiazolyl, isoxazolyl, morpholinyl (e.g. morpholino), oxazolyl, piperazinyl (e.g., 1-piperazinyl), piperidyl (e.g., 1-piperidyl, piperidino), pyranyl, pyrazinyl, pyrazolidinyl, pyrazolinyl, pyrazolyl, pyridazinyl, pyridyl, pyrimidinyl, pyrrolidinyl (e.g., 1-pyrrolidinyl), pyrrolinyl, pyrrolyl, thiadiazolyl, thiazolyl, thienyl, thiomorpholinyl (e.g., thiomorpholino), and triazolyl. These heterocyclic groups can be substituted or unsubstituted. Where a group is substituted, the substituent can be alkyl, alkoxy, halogen, oxygen, or substituted or unsubstituted phenyl. Other examples of amino acid replacements include stereoisomers (e.g., D-amino acids) and unnatural amino acids such as, for example, L-ornithine, L-homocysteine, L-homoserine, L- citrulline, 3-sulfino-L-alanine, N-(L-arginino)succinate, 3,4-dihydroxy-L-phenylalanine, 3-iodo- L-tyrosine, 3,5-diiodo-L-tyrosine, triiodothyronine, L-thyroxine, L-selenocysteine, N-(L- arginino)taurine, 4-aminobutylate, (R,S)-3-amino-2-methylpropanoate, a,a-disubstituted amino acids, N-alkyl amino acids, lactic acid, β-alanine, 3-pyridylalanine, 4-hydroxyproline, O- phosphoserine, N-methylglycine, N-acetylserine, N-formylmethionine, 3-methylhistidine, 5- hydroxylysine, nor-leucine, and other similar amino acids and imino acids. In some embodiments, an isolated peptide described herein, or a pharmaceutically acceptable salt thereof, or a fragment or derivative thereof, can comprise one or more reverse peptide bonds, one or more non-peptide bonds, one or more D-isomers of amino acids, one or more chemical modifications, or any combination thereof. In some embodiments, an isolated peptide described herein, or a pharmaceutically acceptable salt thereof, or a fragment or derivative thereof, is produced by expression in a heterologous host cell. In some embodiments an isolated peptide described herein, or a pharmaceutically acceptable salt thereof, or a fragment or derivative thereof, is produced synthetically.
Attorney Docket #: 250298.000961 In some embodiments, the peptide disclosed herein forms a complex with one or more MHC class I α heavy chains. In some embodiments, the MHC class I α heavy chain is fully human. In some embodiments, the MHC class I α heavy chain is humanized. Humanized MHC class I α heavy chains are described, e.g., in U.S. Pat. Pub. Nos. 2013/0111617, 2013/0185819 and 2014/0245467. In some embodiments, the MHC class I α heavy chain comprises a human extracellular domain (human α1, α2, and/or α3 domains) and a cytoplasmic domain of another species. In some embodiments, the class I α heavy chain polypeptide is HLA-A, HLA-B, HLA- C, HLA-E, HLA-F, HLA-G, HLA-K, or HLA-L. In various aspects, the peptide-MHC can include all the domains of an MHC class I heavy chain. In some embodiments, the MHC molecule is a class I MHC molecule. In some embodiments, the class I MHC molecule is a class I human leukocyte antigen (HLA) molecule. In some embodiments, the class I HLA molecule is HLA-A molecule. In some embodiments, the HLA-A molecule is an HLA-A2 molecule. In some specific embodiments, the HLA-A2 molecule is HLA-A*02:01 molecule. In some embodiments, the MHC molecule comprises a β2-microglobulin. In some embodiments, the β2-microglobulin is fully human. In some embodiments, the β2-microglobulin is humanized. In some embodiments, the MHC class I molecule comprises a mutation in a β2- microglobulin (β2m or Β2M) polypeptide and in the Heavy Chain sequence to affect a disulfide bond between the Β2M and the Heavy Chain. In some cases, the Heavy Chain is an HLA and wherein the disulfide bond links one of the following pairs of residues: Β2M residue 12, HLA residue 236; Β2M residue 12, HLA residue 237; Β2M residue 8, HLA residue 234; Β2M residue 10, HLA residue 235; Β2M residue 24, HLA residue 236; Β2M residue 28, HLA residue 232; Β2M residue 98, HLA residue 192; Β2M residue 99, HLA residue 234; Β2M residue 3, HLA residue 120; Β2M residue 31, HLA residue 96; Β2M residue 53, HLA residue 35; Β2M residue 60, HLA residue 96; Β2M residue 60, HLA residue 122; Β2M residue 63, HLA residue 27; Β2M residue Arg3, HLA residue Gly120; Β2M residue His31, HLA residue Gln96; Β2M residue Asp53, HLA residue Arg35; Β2M residue Trp60, HLA residue Gln96; Β2M residue Trp60, HLA residue Asp122; Β2M residue Tyr63, HLA residue Tyr27; Β2M residue Lys6, HLA residue Glu232; Β2M residue Gln8, HLA residue Arg234; Β2M residue Tyr10, HLA residue Pro235; Β2M residue Ser11, HLA residue Gln242; Β2M residue Asn24, HLA residue Ala236; Β2M residue Ser28, HLA residue Glu232; Β2M residue Asp98, HLA residue His192; and Β2M residue Met99, HLA residue
Attorney Docket #: 250298.000961 Arg234, first linker position Gly2, Heavy Chain (HLA) position Tyr84; Light Chain (Β2M) position Arg12, HLA Ala236; and/or Β2M residue Arg12, HLA residue Gly237. In some embodiments, at least one chain of the MHC and the peptide are comprised within a fusion protein. In one specific embodiment, the MHC and the peptide are separated by a linker sequence. For example, the single chain molecule can comprise, from amino to carboxy terminal, an antigenic determinant, a β2-microglobulin sequence, and a class I α (heavy) chain sequence. Alternatively, the single chain molecule can comprise, from amino to carboxy terminal, an antigenic determinant, a class I α (heavy) chain sequence, and a β2-microglobulin sequence. The single-chain molecule can further comprise a signal peptide sequence at the amino terminal. In certain embodiments, there can be a linker sequence between the peptide sequence and the β2- microglobulin sequence. In certain embodiments, there can be a linker sequence between the β2- microglobulin sequence and the class I α (heavy) chain sequence. A single-chain molecule can further comprise a signal peptide sequence at the amino terminal, as well as first linker sequence extending between the peptide sequence and the β2-microglobulin sequence, and/or a second linker sequence extending between the β2-microglobulin sequence and the class I heavy chain sequence. In certain embodiments, the β2-microglobulin and the class I α (heavy) chain sequences can be human, murine, or porcine. In some embodiments, a single-chain molecule can comprise a first flexible linker between the peptide ligand segment and the β2-microglobulin segment. For example, linkers can extend from and connect the carboxy terminal of the peptide ligand segment to the amino terminal of the β2-microglobulin segment. Preferably, the linkers are structured to allow the linked peptide ligand to fold into the binding groove resulting in a functional MHC-antigen peptide. In some embodiments, this linker can comprise at least about 10 amino acids, up to about 15 amino acids. In some embodiments, a single-chain molecule can comprise a second flexible linker inserted between the β2-microglobulin and heavy chain segments. For example, linkers can extend from and connect the carboxy terminal of the β2-microglobulin segment to the amino terminal of the heavy chain segment. In certain embodiments, the β2-microglobulin and the heavy chain can fold into the binding groove resulting in a molecule which can function in promoting T cell expansion. Suitable linkers used in the MHCs can be of any of a number of suitable lengths, such as from 1 amino acid (e.g., Gly) to 20 amino acids, from 2 amino acids to 15 amino acids, from 3 amino acids to 12 amino acids, including 4 amino acids to 10 amino acids, 5 amino acids to 9
Attorney Docket #: 250298.000961 amino acids, 6 amino acids to 8 amino acids, or 7 amino acids to 8 amino acids, and can be 1, 2, 3, 4, 5, 6, or 7 amino acids. Non-limiting examples of linkers include, e.g., glycine polymers (G)n, glycine-serine polymers (including, for example, (GS)n, (GSGGS)n (SEQ ID NO: 147) and (GGGS)n (SEQ ID NO: 148), where n is an integer of at least one), glycine-alanine polymers, alanine-serine polymers, and other flexible linkers. Glycine and glycine-serine polymers can be used; both Gly and Ser are relatively unstructured, and therefore can serve as a neutral tether between components. Glycine polymers can be used; glycine accesses significantly more phi-psi space than even alanine, and is much less restricted than residues with longer side chains). Exemplary linkers can comprise amino acid sequences including, but not limited to, those listed in Table 1. In some embodiments, a linker peptide includes a cysteine residue that can form a disulfide bond with a cysteine residue present in a second polypeptide. Table 1. Examples of Linker Sequences Linker amino acid SEQ Linker codon-optimized SEQ Linker codon-optimized SEQ sequence ID nucleotide sequence ID nucleotide sequence ID :
Attorney Docket #: 250298.000961 GGGASGGGGSGG 110 GGGGGGGGCGCTTCA 126 GGAGGCGGCGCTTCTG 142 GGS GGCGGAGGTGGAAGT GGGGCGGGGGTAGTG
lently attached to an MHC class I α (heavy) chain via a disulfide bridge (i.e., a disulfide bond between two cystines). In certain embodiments, the disulfide bond comprises a first cysteine, comprising a linker extending from the carboxy terminal of an antigen peptide, and a second cysteine comprising an MHC class I heavy chain (e.g., an MHC class I α (heavy) chain which has a non- covalent binding site for the antigen peptide). In certain embodiments, the second cysteine can be a mutation (addition or substitution) in the MHC class I α (heavy) chain. In certain embodiments, the single-chain molecule can comprise one contiguous polypeptide chain as well as a disulfide bridge. In certain embodiments, the single-chain molecule can comprise two contiguous polypeptide chains which are attached via the disulfide bridge as the only covalent linkage. In some embodiments, the linking sequences can comprise at least one amino acid in addition to the Cys residues, including one or more Gly residues, one or more Ala residues, and/or one or more Ser residues. In certain embodiments, the disulfide bridge can link an antigen peptide described herein in the class I groove of the pMHC complex if the pMHC complex comprises a first cysteine in a Gly-Ser linker extending between the C-terminus of the peptide and the β2-microglobulin, and a second cysteine in a proximal heavy chain position.
Attorney Docket #: 250298.000961 Attaching the peptide to the MHC class I or MHC class II molecule via a flexible linker has the can help ensure that the peptide will occupy and stay associated with the MHC molecule during biosynthesis, transport, and display. However, there may be situations in which this linker can interfere with peptide binding to the MHC molecule or with recognition of the complex by an antigen-recognition molecule. As an alternate approach, in some embodiments, the MHC molecule and the peptide are expressed separately. In some embodiments, the β2-microglobulin sequence can comprise a full-length β2- microglobulin sequence. In certain embodiments, the β2-microglobulin sequence lacks the leader peptide sequence. As such, in some configurations, the β2-microglobulin sequence can comprise about 99 amino acids, and can be a mouse β2-microglobulin sequence (e.g., GenBank Accession No. X01838). In some other configurations, the β2-microglobulin sequence can comprise about 99 amino acids, and can be a human β2-microglobulin sequence (e.g., GenBank Accession No. AF072097.1). In some embodiments, a peptide of the present disclosure may be covalently linked to an MHC molecule, such as by a disulfide bond. In some embodiments, the pMHC complex can contain MHC sequences as disclosed in U.S. Patent Nos. 4,478,823; 6,011,146; 8,518,697; 8,895,020; 8,992,937; WO 96/04314; Mottez et al. J. Exp. Med. 181: 493-502, 1995; Madden et al. Cell 70: 1035-1048, 1992; Matsumura et al., Science 257: 927-934, 1992; Mage et al., Proc. Natl. Acad. Sci. USA 89: 10658-10662, 1992; Toshitani et al., Proc. Nat’l Acad. Sci.93: 236-240, 1996; Chung et al, J. Immunol. 163:3699-3708, 1999; Uger and Barber, J. Immunol. 160: 1598- 1605, 1998; Uger et al., J. Immunol. 162, pp. 6024-6028, 1999; White et al., J. Immunol. 162: 2671-2676, 1999; Yu et al., J. Immunol. 168:3145-3149, 2002; Truscott et al., J. Immunol. 178: 6280–6289, 2007; Mitaksov et al., Chem Biol.2007 August; 14(8):909–922; Hansen et al., Trends Immunol. 2010 Oct; 31(10):363-9, all of which are incorporated by reference in their entireties. In some embodiments, the MHC comprises a class II MHC polypeptide or a fragment, mutant or derivative thereof. In one specific embodiment, the MHC comprises α and β polypeptides of a class II MHC complex or a fragment, mutant or derivative thereof. In one specific embodiment, the α and β polypeptides are linked by a peptide linker. In one specific embodiment, the MHC comprises α and β polypeptides of a human class II MHC complex selected from the group consisting of HLA-DP, HLA-DR, HLA-DQ, HLA-DM and HLA-DO. In another
Attorney Docket #: 250298.000961 specific embodiment, the MHC comprises α and β polypeptides of a murine H-2A or H-2E class II MHC complex. Naturally occurring MHC class II molecules can contain two polypeptide chains, α and β. The chains may come from the DP, DQ, or DR gene groups. There are about 40 known different human MHC class II molecules. All have the same basic structure but can vary subtly in their molecular structure. MHC class II molecules can bind peptides of 13-18 amino acids in length. In some embodiments, the MHC class II α chain is fully human. In some embodiments, the MHC class II α chain is humanized. Humanized MHC class II α chains are described, e.g., in U.S. Pat. Nos. 8,847,005, 9,043,996, and 10,154,658, which are incorporated herein by reference in their entireties. In some embodiments, the humanized MHC class II α chain polypeptide comprises a human extracellular domain and a cytoplasmic domain of another species. In some embodiments, the class II α chain is HLA-DMA, HLA-DOA, HLA-DPA, HLA-DQA or HLA- DRA. In some embodiments, the class II α chain polypeptide is humanized HLA-DMA, HLA- DOA, HLA-DPA, HLA-DQA and/or HLA-DRA. In some embodiments, the peptide of the present disclosure forms a complex with one or more MHC class II β chains. In some embodiments, the MHC class II β chain is fully human. In some embodiments, the MHC class II β chain polypeptide is humanized. Humanized MHC class II β chain polypeptides are described, e.g., in U.S. Pat. Nos.8,847,005, 9,043,996, and 10,154,658, which are incorporated herein by reference in their entireties. In some embodiments, the humanized MHC class II β chain comprises a human extracellular domain and a cytoplasmic domain of another species. In some embodiments, the class II β chain is HLA-DMB, HLA-DOB, HLA-DPB, HLA-DQB or HLA-DRB. In some embodiments, the class II β chain is humanized HLA-DMB, HLA-DOB, HLA-DPB, HLA-DQB and/or HLA-DRB. In some embodiments, a peptide of the present disclosure may be covalently linked to an MHC class II molecule, such as by a disulfide bond. In some embodiments, the α chain and β chain of an MHC class II molecule may be linked, such as by a Jun-Fos zipper, electrostatic engineering, knobs-into-holes, an immunoglobulin scaffold, an immunoglobulin Fc region, or a linker. See, e.g., U.S. Pat. Pub. No. 2022/0409732, which is incorporated herein by reference in its entirety. Peptides or pMHC complexes disclosed herein may be fused or conjugated to one or more heterologous molecules. Peptides or pMHC complexes of the disclosed herein may also be
Attorney Docket #: 250298.000961 in multimeric form. Accordingly, the present disclosure also provides fusion proteins, conjugates, and oligomeric complexes comprising a peptide or a pMHC complex of the disclosure. In some embodiments, peptides are fused or conjugated to one or more heterologous molecules which includes an MHC molecule (or fragments or derivatives thereof). In some embodiments, the MHC molecule is a class I MHC molecule. In some embodiments, the class I MHC molecule is a class I human leukocyte antigen (HLA) molecule. In some embodiments, the class I HLA molecule is HLA-A molecule. In some embodiments, the HLA-A molecule is an HLA-A2 molecule. In some specific embodiments, the HLA-A2 molecule is HLA-A*02:01 molecule. Heterologous molecules suitable for genetical fusion and/or chemical conjugation with the peptides or the pMHC complexes of the disclosure include, but are not limited to, peptides, polypeptides, small molecules, polymers, nucleic acids, lipids, sugars, etc. The heterologous molecule(s) may be fused at the N- and/or C-terminus of the peptide and/or another polypeptide chain in the pMHC complex. Heterologous peptides and polypeptides include, but are not limited to, an epitope (e.g., FLAG) or a tag sequence (e.g., His6 (SEQ ID NO: 149), and the like) to allow for the detection and/or isolation of a fusion protein; a transmembrane receptor protein or a portion thereof, such as an extracellular domain or a transmembrane and intracellular domain; a ligand or a portion thereof which binds to a transmembrane receptor protein; an enzyme or portion thereof which is catalytically active; a polypeptide or peptide which promotes oligomerization, such as a leucine zipper domain; a polypeptide or peptide which increases stability, such as an immunoglobulin constant region (e.g., an Fc domain); a half-life-extending sequence comprising a combination of two or more (e.g., 2, 5, 10, 15, 20, 25, etc.) naturally occurring or non-naturally occurring charged and/or uncharged amino acids (e.g., Ser, Gly, Glu or Asp) designed to form a predominantly hydrophilic or predominantly hydrophobic fusion partner for a fusion protein; a functional or non- functional antibody (e.g., an antibody that is specific for dendritic cells), or a heavy or light chain thereof; and a polypeptide which has an activity, such as a therapeutic activity, different from fusion proteins of the present disclosure. In some embodiments, the one or more heterologous molecules enhances a peptide-specific immune response in a subject. In some embodiments, the one or more heterologous molecules mediates peptide delivery to a specific site within a subject.
Attorney Docket #: 250298.000961 In some embodiments, fusion proteins of the disclosure may comprise one or more affinity tags, e.g., to allow for affinity purification or coupling to another molecule. Examples of affinity tags include, but are not limited to, a His6 tag (SEQ ID NO: 149), an Avi-tag, a biotin, a hemagglutinin (HA) tag, a FLAG tag, a Myc tag, a GST tag, a MBP tag, a chitin binding protein tag, a calmodulin tag, a V5 tag, a streptavidin binding tag, a green fluorescent protein (GFP), YFP, RFP, CFP, mCherry, tdTomato, SUMO tag, and Ubiquitin tag. Peptides or pMHC complexes of the disclosure may be conjugated to additional moieties such as carrier molecules or adjuvants for use as vaccines. Examples of adjuvants used in vaccines include microbes, such as the bacterium Bacillus Calmette-Guérin (BCG), and/or substances produced by bacteria, such as Detox B (an oil droplet emulsion of monophosphoryl lipid A and mycobacterial cell wall skeleton). KLH (keyhole limpet hemocyanin), bovine serum albumin (BSA), the E2 core protein of the pyruvate dehydrogenase complex are examples of suitable carrier proteins used in vaccine compositions. Additional examples of carrier proteins suitable for use in the compositions of the present disclosure include, but are not limited to, ovalbumin (OVA), blue carrier protein (BCP), thyroglobulin (THY), a soybean trypsin inhibitor (STI), and multiple attachment peptide (MAP), albumin, serum albumin, c-reactive protein, conalbumin, lactalbumin, ion carrier protein, acyl carrier protein, signal transduction adapter protein, androgen binding protein, calcium binding protein, calmodulin binding protein, ceruloplasmin, cholesterol Ester transfer protein, f box protein, fatty acid binding protein, follistatin, follistatin related protein, GTP binding protein, insulin-like growth factor binding protein, iron binding protein, latent TGF beta binding protein, light-harvesting protein complex, lymph Sphere antigen, membrane transport protein, neurophysin, periplasmic binding protein, phosphate binding protein, phosphatidylethanolamine binding protein, phospholipid transport protein, retinol binding protein, RNA binding protein, s-phase kinase related protein, sex hormone binding globulin, Thyroxine binding protein, transcobalamin, transcortin, transferrin binding protein, and/or vitamin D binding protein. Peptides or pMHC complexes of the present disclosure may also be attached, covalently (e.g., via a linker) or non-covalently, to a moiety capable of eliciting a therapeutic effect, such as antibodies, or cytokines. Alternatively or additionally, the peptides or pMHC complexes may be encapsulated into liposomes.
Attorney Docket #: 250298.000961 In certain aspects the present disclosure contemplates a non-covalent complex comprising (i) an isolated peptide described herein or a pharmaceutically acceptable salt thereof, or a fragment or derivative thereof, and (ii) an MHC molecule, or a fragment or derivative thereof. In some embodiments, the MHC molecule is a class I MHC molecule. In some embodiments, the class I MHC molecule is a class I human leukocyte antigen (HLA) molecule. In some embodiments, the class I HLA molecule is HLA-A molecule. In some embodiments, the HLA-A molecule is an HLA-A2 molecule. In some specific embodiments, the HLA-A2 molecule is HLA- A*02:01 molecule. Other suitable heterologous molecules include, but are not limited to, fluorescent, or luminescent labels, radiolabels, nucleic acid probes, and contrast reagents, antibodies, or enzymes that produce a detectable product. Methods for detecting heterologous molecules may include flow cytometry, microscopy, electrophoresis, or scintillation counting. In some embodiments, peptides or pMHC complexes of the disclosure may be conjugated with fluorocarbon to increase cellular immunogenicity. Where the peptide or another polypeptide chain of the pMHC complex is linked to a fluorocarbon, the terminus of the peptide or polypeptide chain, such as the terminus that is not conjugated to the fluorocarbon, or other attachment, can be altered, for example to promote solubility of the fluorocarbon-peptide/polypeptide construct via the formation of micelles. To facilitate large-scale synthesis of the construct, the N- or C-terminal amino acid residues of the peptide or another polypeptide chain of the pMHC complex can be modified. When the desired peptide or another polypeptide chain of the pMHC complex is particularly sensitive to cleavage by peptidases, the normal peptide bond can be replaced by a non- cleavable peptide mimetic. Such bonds and methods of synthesis are well known in the art. Peptides or pMHC complexes of the disclosure may be provided in soluble form, or may be immobilized by attachment to a suitable solid support. Examples of solid supports include, but are not limited to, a bead (e.g., a magnetic bead), a membrane, sepharose, a plate, a tube, and a column. pMHC complexes may be attached, for instance, to an ELISA plate, a magnetic bead, or a surface plasmon resonance biosensor chip. Methods of attaching peptides or pMHC complexes to a solid support are known to the skilled person, and include, for example, using an affinity binding pair, e.g., biotin and streptavidin, or antibodies and antigens. In some embodiments, peptides or pMHC complexes are labeled with biotin and attached to streptavidin-coated surfaces.
Attorney Docket #: 250298.000961 In some embodiments, the peptides or pMHC complexes of the disclosure may be conjugated to a particle or a solid support described herein. Peptides or pMHC complexes of the disclosure may be in multimeric form, for example, dimeric, or tetrameric, or pentameric, or octameric, or greater. Accordingly, in some aspects, the present disclosure provides oligomeric complexes comprising the peptides or pMHC complexes of the present disclosure. In certain embodiments, an oligomeric complex of the present disclosure may comprise, for example, two or more isolated peptides described herein, or a pharmaceutically acceptable salt thereof, or a fragment or derivative thereof. As used herein, the terms “oligomer”, “oligomeric”, “oligomerize” and “oligomerization” or the like encompass a dimer, trimer, tetramer, pentamer, hexamer, heptamer, octamer, or higher species of polymerized monomers that comprise the peptide or pMHC complex. Having multiple copies of the peptides or pMHC complexes in a large complex may enhance their biological activity, e.g., immunogenic activity. For example, the peptides of the disclosure may be oligomerized using the biotin/streptavidin system. Biotinylated analogs of peptide monomers may be synthesized by standard techniques. For example, the peptide may be C-terminally biotinylated. These biotinylated peptide monomers are then oligomerized by incubation with streptavidin [e.g., at a 4:1 molar ratio at room temperature in phosphate buffered saline (PBS) or HEPES-buffered RPMI medium for 1 hour]. In a variation of this embodiment, biotinylated peptide monomers may be oligomerized by incubation with anti-biotin antibodies [e.g., goat anti-biotin IgG]. In general, oligomeric pMHC complexes may be produced using pMHC tagged with a biotin residue and complexed through fluorescently labeled streptavidin. A biotinylation site may be introduced to the pMHC complex to which biotin can be added, for example, using the BirA enzyme. Alternatively, oligomeric pMHC complexes may be formed by using immunoglobulin as a molecular scaffold. In this system, the extracellular domains of MHC molecules are fused with the constant region of an immunoglobulin heavy chain separated by a short amino acid linker. Oligomeric pMHC complexes have also been produced using carrier molecules such as dextran. Oligomeric pMHC complexes can be useful for improving the detection of binding moieties, such as T cell receptors, which bind said complex, because of avidity effects. In other embodiments, the peptides or pMHC complexes of the disclosure can be oligomerized by covalent attachment to at least one linker. The linker moiety can be a peptide linker, such as those described herein. In some embodiments, polyethylene glycol (PEG) may
Attorney Docket #: 250298.000961 serve as the linker that oligomerizes the peptide monomers. For example, a single PEG moiety may be simultaneously attached to the N-termini of both peptide chains of a peptide dimer. Alternatively, oligomeric peptide or pMHC complexes may also contain one or more intramolecular disulfide bonds between cysteine residues of the peptide or pMHC monomers. Preferably, the two monomers contain at least one intramolecular disulfide bond. Most preferably, both monomers contain an intramolecular disulfide bond, such that each monomer contains a cyclic group. Such disulfide bonds may be formed by oxidation of the cysteine residues of the peptide core sequence. In one embodiment the control of cysteine bond formation is exercised by choosing an oxidizing agent of the type and concentration effective to optimize formation of the desired isomer. For example, oxidation of a peptide dimer to form two intramolecular disulfide bonds (one on each peptide chain) is preferentially achieved (over formation of intermolecular disulfide bonds) when the oxidizing agent is DMSO. The formation of cysteine bonds can be controlled by the selective use of thiol-protecting groups during peptide synthesis. In some embodiments, peptides or pMHC complexes described herein may be fused or conjugated to a dimerization moiety. The dimerization moiety may contain, for example, an immunoglobulin domain, such as from an IgG antibody (e.g., human IgG), which connects two monomers generating a homodimer or heterodimer molecule. As a non-limiting example, the dimerization motif in the proteins according to the present disclosure may be constructed to include a hinge region and an immunoglobulin domain (e.g., Cγ3 domain), e.g., carboxyterminal C domain (CH3 domain), or a sequence that is substantially identical to the C domain. The hinge region may be Ig derived and contributes to the dimerization through the formation of an interchain covalent bond(s), e.g., disulfide bridge(s). In addition, such homodimer or heterodimer molecules may further comprise one or more targeting moieties that bind to target molecules present on, for example, antigen-presenting cells (APCs) such as dendritic cells or B cells. In such instances, the hinge region may function as a flexible spacer between the domains allowing the two targeting units to bind simultaneously to two target molecules on the APC expressed with variable distances. The immunoglobulin domains contribute to dimerization through non-covalent interactions, e.g., hydrophobic interactions. In a preferred embodiment the CH3 domain is derived from IgG. These dimerization moieties may be exchanged with other multimerization moieties from e.g., other Ig isotypes/subclasses. Preferably the dimerization motif is derived from native human proteins, such
Attorney Docket #: 250298.000961 as human IgG. Examples of such homodimer protein construct are described in US 10,590,195, which is incorporated herein by reference in its entirety. In one aspect, the present disclosure provides a composition comprising (i) one or more isolated peptides described herein, one or more fusion proteins described herein, one or more conjugates described herein, one or more oligomeric complexes of claim described herein, or one or more non-covalent complexes described herein, or any combination thereof; and (ii) a carrier or excipient. In some embodiments, such composition described herein may be formulated as a pharmaceutical composition. A pharmaceutical composition of the disclosure may be in any suitable form (depending upon the desired method of administering to a patient). Suitable compositions and methods of administration are known to those skilled in the art, for example see, Johnson et al., Blood. 2009; 114(3):535-46. The pharmaceutical composition may comprise the peptides or peptide-based molecules of the disclosure either in the free form or in the form of a pharmaceutically acceptable salt. The term “pharmaceutically acceptable salt” as used herein refers to a derivative of the disclosed peptides wherein the peptide is modified by making acid or base salts of the agent. For example, acid salts are prepared from the free base (typically wherein the neutral form of the drug has a neutral —NH2 group) involving reaction with a suitable acid. Suitable acids for preparing acid salts include both organic acids, e.g., acetic acid, benzoic acid, citric acid, propionic acid, glycolic acid, trifluoroacetic acid, pyruvic acid, oxalic acid, malic acid, malonic acid, maleic acid, succinic acid, fumaric acid, tartaric acid, cinnamic acid, mandelic acid, methanesulfonic acid, ethanesulfonic acid, p-toluenesulfonic acid, salicylic acid, and the like, as well as inorganic acids, e.g., hydrochloric acid, hydrobromic acid, sulfuric acid, nitric acid phosphoric acid and the like. Conversely, preparation of basic salts of acid moieties which may be present on a peptide are prepared using a pharmaceutically acceptable base such as sodium hydroxide, potassium hydroxide, ammonium hydroxide, calcium hydroxide, trimethylamine or the like. The pharmaceutical composition may be adapted for administration by any appropriate route such as, e.g., parenteral (including subcutaneous, intramuscular, or intravenous), enteral (including oral or rectal), inhalation, or intranasal routes. Such compositions may be prepared by any method known in the art of pharmacy, for example, by mixing the active ingredient with the carrier(s) or excipient(s) under sterile conditions.
Attorney Docket #: 250298.000961 In another aspect, the present disclosure provides an isolated cell comprising one or more fusion proteins described herein, one or more conjugates described herein, one or more oligomeric complexes described herein, or one or more non-covalent complexes described herein, or any combination thereof. In some embodiments, the isolated cell is an immune cell. In some embodiments, the isolated cell is an antigen-presenting cell (APC). In another aspect, the disclosure provides an isolated polynucleotide comprising a nucleic acid sequence encoding one or more peptide(s) and/or peptide-based molecules (such as complexes (e.g., pMHC complexes), fusion proteins, or conjugates comprising the described peptides) of the disclosure. In some embodiments, the polynucleotide described herein encodes a peptide comprising an amino acid sequence that is at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 85%, at least about 90%, at least about 95%, or at least about 99% identical to the amino acid sequence of any one of SEQ ID NOs: 30-47 and 200- 232, or a fragment or derivative thereof. In some embodiments, the polynucleotide described herein encodes a peptide comprising an amino acid sequence of any one of SEQ ID NOs: 30-47 and 200-232, or a fragment or derivative thereof. In some embodiments, the polynucleotide described herein encodes more than one peptide selected from any one of SEQ ID NOs: 30-47 and 200-232, or fragments thereof. For example, the polynucleotide described herein may encode 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, or 32 peptides as described herein (e.g., SEQ ID NOs: 30-47 and 200-232), or fragments thereof. The peptides may be arranged in any order, and may be identical or different. In some embodiments, the polynucleotide described herein encodes a peptide comprising an amino acid sequence that is at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 85%, at least about 90%, at least about 95%, or at least about 99% identical to the amino acid sequence of any one of SEQ ID NOs: 200-232, or a fragment or derivative thereof. In some embodiments, the polynucleotide described herein encodes a peptide comprising an amino acid sequence of any one of SEQ ID NOs: 200-232, or a fragment or derivative thereof. In some embodiments, the polynucleotide described herein encodes more than one peptide selected from any one of SEQ ID NOs: 200-232, or fragments thereof. For example, the
Attorney Docket #: 250298.000961 polynucleotide described herein may encode 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, or 30 peptides as described herein (e.g., SEQ ID NOs: 200-232), or fragments thereof. The peptides may be arranged in any order, and may be identical or different. In some embodiments, the polynucleotide described herein encodes a peptide comprising an amino acid sequence that is at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 85%, at least about 90%, at least about 95%, or at least about 99% identical to the amino acid sequence of any one of SEQ ID NOs: 200-201, or a fragment or derivative thereof. In some embodiments, the polynucleotide described herein encodes a peptide comprising an amino acid sequence of any one of SEQ ID NOs: 200-201, or a fragment or derivative thereof. In some embodiments, the polynucleotide described herein encodes more than one peptide selected from any one of SEQ ID NOs: 200-201, or fragments thereof. For example, the polynucleotide described herein may encode 2, 3, or 4 peptides as described herein (e.g., SEQ ID NOs: 200-201), or fragments thereof. The peptides may be arranged in any order, and may be identical or different. In some embodiments, the polynucleotide described herein encodes a peptide comprising an amino acid sequence that is at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 85%, at least about 90%, at least about 95%, or at least about 99% identical to the amino acid sequence of any one of SEQ ID NOs: 202-232, or a fragment or derivative thereof. In some embodiments, the polynucleotide described herein encodes a peptide comprising an amino acid sequence of any one of SEQ ID NOs: 202-232, or a fragment or derivative thereof. In some embodiments, the polynucleotide described herein encodes more than one peptide selected from any one of SEQ ID NOs: 202-232, or fragments thereof. For example, the polynucleotide described herein may encode 2, 3, or 4 peptides as described herein (e.g., SEQ ID NOs: 202-232), or fragments thereof. The peptides may be arranged in any order, and may be identical or different. In some embodiments, the polynucleotide described herein encodes a peptide comprising an amino acid sequence that is at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 85%, at least about 90%, at least about 95%, or at
Attorney Docket #: 250298.000961 least about 99% identical to the amino acid sequence of any one of SEQ ID NOs: 202-230, or a fragment or derivative thereof. In some embodiments, the polynucleotide described herein encodes a peptide comprising an amino acid sequence of any one of SEQ ID NOs: 202-230, or a fragment or derivative thereof. In some embodiments, the polynucleotide described herein encodes more than one peptide selected from any one of SEQ ID NOs: 202-230, or fragments thereof. For example, the polynucleotide described herein may encode 2, 3, or 4 peptides as described herein (e.g., SEQ ID NOs: 202-230), or fragments thereof. The peptides may be arranged in any order, and may be identical or different. In some embodiments, the polynucleotide described herein encodes a peptide comprising an amino acid sequence that is at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 85%, at least about 90%, at least about 95%, or at least about 99% identical to the amino acid sequence of any one of SEQ ID NOs: 209, 231, and 232, or a fragment or derivative thereof. In some embodiments, the polynucleotide described herein encodes a peptide comprising an amino acid sequence of any one of SEQ ID NOs: 209, 231, and 232, or a fragment or derivative thereof. In some embodiments, the polynucleotide described herein encodes more than one peptide selected from any one of SEQ ID NOs: 209, 231, and 232, or fragments thereof. For example, the polynucleotide described herein may encode 2, 3, or 4 peptides as described herein (e.g., SEQ ID NOs: 209, 231, and 232), or fragments thereof. The peptides may be arranged in any order, and may be identical or different. In some embodiments, the polynucleotide described herein encodes a peptide comprising an amino acid sequence that is at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 85%, at least about 90%, at least about 95%, or at least about 99% identical to the amino acid sequence of any one of SEQ ID NOs: 233-240, or a fragment or derivative thereof. In some embodiments, the polynucleotide described herein encodes a peptide comprising an amino acid sequence of any one of SEQ ID NOs: 233-240, or a fragment or derivative thereof. In some embodiments, the polynucleotide described herein encodes more than one peptide selected from any one of SEQ ID NOs: 233-240, or fragments thereof. For example, the polynucleotide described herein may encode 2, 3, or 4 peptides as described herein (e.g., SEQ ID
Attorney Docket #: 250298.000961 NOs: 233-240), or fragments thereof. The peptides may be arranged in any order, and may be identical or different. In some embodiments, the polynucleotide described herein may comprise a nucleotide sequence which is operably linked to a promoter. A promoter of the present disclosure can be selected from a viral promoter, a bacterial promoter, a mammalian promoter, an avian promoter, a fish promoter, an insect promoter, and any combination thereof. In some embodiments, the nucleotide is under the control of a human promoter. In some embodiments, the nucleotide is under the control of a non-human promoter. Non-limiting examples of promoters include cytomegalovirus (CMV) promoter (U.S. Pat. Nos.5,385,839 and 5,168,062), the SV40 early promoter region (Benoist, et al., (1981) Nature 290:304-310), the promoter contained in the 3' long terminal repeat of Rous sarcoma virus (Yamamoto, et al., (1980) Cell 22:787-797), the herpes thymidine kinase promoter (Wagner, et al., (1981) Proc. Natl. Acad. Sci. USA 78:1441-1445), the regulatory sequences of the metallothionein gene (Brinster, et al., (1982) Nature 296:39-42); prokaryotic expression vectors such as the beta-lactamase promoter (VIIIa-Komaroff, et al., (1978) Proc. Natl. Acad. Sci. USA 75:3727-3731), or the tac promoter (DeBoer, et al., (1983) Proc. Natl. Acad. Sci. USA 80:21-25); see also "Useful proteins from recombinant bacteria" in Scientific American (1980) 242:74-94; and promoter elements from yeast or other fungi such as the Gal4 promoter, the ADC (alcohol dehydrogenase) promoter, PGK (phosphoglycerol kinase) promoter or the alkaline phosphatase promoter. In some embodiment, the polynucleotide described herein is a DNA molecule. In some embodiments, the polynucleotide described herein is an RNA molecule. For example, the RNA molecule may be mRNA or a self-replicating RNA. A nucleic acid molecule described herein may be generated synthetically. One method is the phosphoramidite method. Without wishing to be bound by theory, in this chemistry, a phosphoramidite (a nucleoside with side protecting groups that preserve the integrity of the sugar, the phosphodiester linkage, and the base during chain extension steps) is coupled through its reactive 3' phosphorous group to the 5' hydroxyl group of a nucleoside immobilized on a solid support column. The steps of oligonucleotide synthesis can include the following: (1) Detritylation, in which the dimethoxytrityl (DMT or trityl) group on the 5' hydroxyl of the support nucleoside is removed by treatment with trichloroacetic acid (TCA). (2) In the coupling step, a
Attorney Docket #: 250298.000961 phosphoramidite, made reactive by tetrazole (a weak acid), is chemically coupled to the last base added to the column support material. (3) In the capping step, any free 5' hydroxyl groups of unreacted column nucleotides are acetylated by treatment with acetic anhydride and N- methylimidazole. (4) In the oxidation step, the unstable internucleotide phosphate linkage between the previously coupled base and the most recently added base is oxidized by treatment with iodine and water to a more stable phosphotriester linkage. Following coupling of all bases in the oligonucleotide's sequence, the completed nucleic acid chain may be cleaved from the column by treatment with ammonium hydroxide, and the base protecting groups are removed by heating in the ammonium hydroxide solution. By way of a non-limiting example, a synthesis cycle may comprise growth of the nucleotide chain from an initial protected nucleoside derivatized via its terminal 3′ hydroxyl to a solid support. Reagents and solvents can be pumped through the support to induce the consecutive removal and addition of sugar protecting groups in order to isolate the reactivity of a specific chemical moiety on the monomer and effect its stepwise addition to the growing oligonucleotide chain. Assembly of the protected oligonucleotide chain can be carried out in chemical steps, for example, without limitation, deblocking, activation/coupling, oxidation, and capping. Cleavage and deprotection then reveal the single-stranded nucleic acid. Nucleic acid synthesis methods disclosed herein can comprise, for example, oligonucleotide synthesis, column-based oligonucleotide synthesis, microarray-based oligonucleotide synthesis, gene synthesis from oligonucleotides, gene synthesis from array- derived oligonucleotide pools, and any of various error correction and sequence validation steps, or any combination thereof. RNA chemical synthesis may be similar to that used for DNA. In some embodiments, RNA chemical synthesis methods may comprise an additional protecting group at the 2′ hydroxyl of ribose. The 2′ hydroxyl of ribose position may be protected with tert-butyldimethyl silyl groups, which can be stable throughout the synthesis, and can be removed at the final deprotection step by addition of a basic fluoride ion such as tetrabutylammonium fluoride (TBAF). The remaining positions on both the sugar and the bases can be protected in the same fashion as for DNA. By adjusting several parameters in the DNA synthesis protocol such as, but not limited to the coupling times, monomer delivery rate, frequency of washing steps, and types of capping reagents, stepwise coupling efficiencies of up to 99% can be obtained.
Attorney Docket #: 250298.000961 In a further aspect, the disclosure provides a vector comprising a nucleic acid sequence described herein. The vector utilized in the context of the present disclosure desirably comprises sequences appropriate for introduction into cells. In some embodiments, the vector is an expression vector. In the context of the present disclosure, the term “vector” encompasses a DNA molecule, such as a plasmid, bacteriophage, phagemid, virus or other vehicle, which contains one or more heterologous or recombinant nucleotide sequences (e.g., an above-described nucleic acid molecule of the disclosure, under the control of a functional promoter and, possibly, also an enhancer) and is capable of functioning as a vector in the sense understood by those of ordinary skill in the art. The following vectors are provided by way of example: bacteriophages such as lambda (X) bacteriophage, EMBL bacteriophage; bacterial vectors such as pBs, phagescript, PsiX174, pBluescript SK, pBs KS, pNH8a, pNH16a, pNH18a, pNH46a; pTrc99A, pKK223-3, pKK233-3, pDR540, and pRIT5; eukaryotic vectors such as pWLneo, pSV2cat, pOG44, PXR1, pSG, pSVK3, pBPV, pMSG and pSVL; and transposons such as Sleeping Beauty transposon and PiggyBac transposon. In some embodiments, the vector is a viral vector. Viral vectors can be derived from naturally occurring virus genomes, which typically are modified to be replication incompetent, e.g., non-replicating. Non-replicating viruses require the provision of proteins in trans for replication. Typically, those proteins are stably or transiently expressed in a viral producer cell line, thereby allowing replication of the virus. The viral vectors are, thus, typically infectious and non-replicating. Viral vectors may be adenovirus vectors, adeno-associated virus (AAV) vectors (e.g., AAV type 5 and type 2), alphavirus vectors (e.g., Venezuelan equine encephalitis virus (VEE), Sindbis virus (SIN), Semliki forest virus (SFV), and VEE-SIN chimeras), herpes virus vectors (e.g., vectors derived from cytomegaloviruses, like rhesus cytomegalovirus (RhCMV)), arena virus vectors (e.g. lymphocytic choriomeningitis virus (LCMV) vectors), measles virus vectors, pox virus vectors (e.g., vaccinia virus, modified vaccinia virus Ankara (MVA), NYVAC (derived from the Copenhagen strain of vaccinia), and avipox vectors (canarypox (ALVAC) and fowlpox (FPV) vectors), vesicular stomatitis virus (VSV) vectors, retrovirus vectors, lentivirus vectors, simian virus 40 (SV40), bovine papilloma viruses, Epstein-Barr viruses, Moloney murine leukemia viruses, Harvey murine sarcoma viruses, murine mammary tumor viruses, Rous sarcoma viruses, poxvirus viral like particles, baculoviral vectors and bacterial spores.
Attorney Docket #: 250298.000961 As further examples, adenovirus vectors may be derived from human adenovirus (Ad) but also from adenoviruses that infect other species, such as bovine adenovirus (e.g. bovine adenovirus 3, BAdV3), a canine adenovirus (e.g. CAdV2), a porcine adenovirus (e.g. PAdV3 or 5), or great apes, such as Chimpanzee (Pan), Gorilla (Gorilla), Orangutan (Pongo), Bonobo (Pan paniscus) and common chimpanzee (Pan troglodytes). Poxvirus (Poxviridae) vectors may be derived from smallpox virus (variola), vaccinia virus, cowpox virus or monkeypox virus. Exemplary vaccinia viruses are the Copenhagen vaccinia virus (W), New York Attenuated Vaccinia Virus (NYVAC), ALVAC, TROVAC and Modified Vaccinia Ankara (MVA). In certain embodiments, the present disclosure provides a composition comprising (i) an isolated polynucleotide described herein or a vector described herein; and (ii) a carrier or excipient. In some embodiments, such composition described herein may be formulated as a pharmaceutical composition described herein. A non-limiting example of a carrier is a lipid nanoparticle carrier. A carrier can also be a solvent or dispersion medium containing, for example, water, ethanol, polyol (for example, glycerol, propylene glycol, and liquid polyethylene glycol, and the like), suitable mixtures thereof, and vegetable oils. The proper fluidity can be maintained, for example, by the use of a coating, such as lecithin, by the maintenance of the required particle size in the case of dispersion and by the use of surfactants. The prevention of the action of microorganisms can be brought about by various antibacterial and antifungal agents known in the art. In many cases, it will be preferable to include isotonic agents, for example, sugars or sodium chloride. Prolonged absorption of the injectable compositions can be brought about by the use in the compositions of agents delaying absorption, for example, aluminum monostearate and gelatin. The present disclosure further encompasses compositions comprising (i) one or more isolated peptides described herein, one or more fusion proteins described herein, one or more conjugates described herein, one or more oligomeric complexes described herein, one or more non-covalent complexes described herein, or one or more cells described herein, or any combination thereof, conjugated to a solid support. Examples of solid supports include, but are not limited to, a bead, a membrane, sepharose, a magnetic bead, a plate, a tube, a column. pMHC complexes may be attached to an ELISA plate, a magnetic bead, or a surface plasmon resonance biosensor chip. Methods of attaching peptides, fusion proteins, conjugates, oligomeric complexes, non-covalent, and cells described herein to a solid support are known to the skilled person, and
Attorney Docket #: 250298.000961 include, for example, using an affinity binding pair, e.g., biotin and streptavidin, or antibodies and antigens. In some embodiments, peptides or pMHC complexes are labeled with biotin and attached to streptavidin-coated surfaces. In a preferred embodiment, a suitable solid can be a multi-well plate (e.g., a 96-well plate). In yet another aspect, the disclosure provides a host cell comprising the vector of the disclosure. The host cell can be either a prokaryotic cell or a eukaryotic (e.g., an immune cell such as, but not limited to, an antigen-presenting cell (APC)). Bacterial cells may be preferred prokaryotic host cells in some circumstances and typically are a strain of E. coli such as, for example, the E. coli strains DH5 and RR1. Non-limiting examples of eukaryotic host cells include yeast, insect, and mammalian cells (e.g., from a mouse, rat, monkey, or human cell lines). Non- limiting examples of yeast host cells include, e.g., YPH499, YPH500, and YPH501. Non-limiting examples of mammalian host cells include Chinese hamster ovary (CHO) cells, NIH Swiss mouse embryo cells NIH/3T3, monkey kidney-derived COS-1 cells, and 293 cells which are human embryonic kidney cells. Examples of insect cells include Sf9 cells, which can be transfected with baculovirus expression vectors. Transformation of appropriate cell hosts with a DNA construct of the present disclosure is accomplished by well-known methods that typically depend on the type of vector used. Successfully transformed cells, i.e., cells that contain a DNA construct of the present disclosure, can be identified by, for example, PCR. Alternatively, the presence of the protein in the supernatant can be detected using antibodies. It will be appreciated that certain host cells of the disclosure are useful in the preparation of the peptides or peptide-based molecules of the disclosure, for example bacterial, yeast, and insect cells. However, other host cells may be useful in certain methods. For example, antigen- presenting cells (APCs), such as dendritic cells or B cells, may be used to express the peptides of the disclosure such that the peptides may be loaded into appropriate MHC molecules. A further aspect of the disclosure provides a method of producing peptides or peptide- based molecules of the disclosure, the method comprising culturing a host cell and isolating the peptide or peptide-based molecule from the host cell or its culture medium. The present disclosure further comprises a kit which may comprise any of various compositions of the present disclosure, including the peptides, peptide-based molecules (such as
Attorney Docket #: 250298.000961 complexes (e.g., peptide-MHC (pMHC) complexes), fusion proteins, or conjugates comprising the peptide(s)), nucleic acid molecules, vectors, cells, or binding moieties of the disclosure. In one aspect, the present disclosure may include a kit comprising, for example: (i) a) one or more isolated peptides described herein; b) one or more fusion proteins of described herein; c) one or more conjugates described herein; d) one or more oligomeric complexes described herein; e) one or more non-covalent complexes described herein; f) one or more compositions described herein; g) one or more cells described herein; h) one or more polynucleotides described herein; and/or i) one or more vectors described herein; and (ii) optionally, packaging and/or instructions for use for the same. In various embodiments the kit or any composition described herein which comprises one or more off-target peptides and/or one or more polynucleotides or vectors encoding one or more off-target peptides may further comprise one or more antigen-recognition molecules, such as an antibody or TCR (e.g., as expressed on a cell), that targets the relevant target peptide (e.g., that targets MAGEA3168-176 target EVDPIGHLY (SEQ ID NO: 29) and/or WT1126-134 target RMFPNAPYL (SEQ ID NO: 241)). The one or more antigen-recognition molecules may, for example, be in solution with a composition comprising one or more off-target peptides disclosed herein (e.g., in a container) and/or may be bound to one or more off-target peptides disclosed herein (e.g., bound to one or more pMHC complexes comprising off-target peptides). Aspects of the embodiments of the structure-based off-target peptide analysis are described in relation to the Figures. FIG. 1 is a flow diagram illustrating an exemplary method 100 for ranking a plurality of potential off-target peptides of a target peptide. At block 110, an MHC-target model is obtained. The MHC-target model can be obtained from a database, generated, and/or refined at block 110. In some embodiments, block 110 includes performing experimental methods and/or computationally generating the MHC-target model. In some embodiments, block 110 includes obtaining the MHC-target model, or an initial model from a database. In such embodiments, the database can include experimentally determined and/or computationally generated 3D computational models of peptides (e.g., in docking conformations for binding MHC molecules), MHC molecules, and/or MHC-peptide complexes. In some embodiments, the MHC-target model can be based at least in part on an experimentally developed 3D computational model. Obtaining the MHC-target model at block 110 can include extracting
Attorney Docket #: 250298.000961 the experimental model from a database or performing experimental methods to obtain the MHC- target model. In some embodiments, the MHC-target model is derived from a predicted structure. In one embodiment, a template based modeling process can determine the predicted structure by performing the steps of (a) searching a database of known protein structures each comprising a 3D computational model of a template peptide bound to the MHC molecule to identify a plurality of template structures based, at least in part, on amino acid sequence similarity of the template peptide with the peptide sequence of the target peptide; (b) aligning the peptide sequence to each of the 3D computational models of the template structures based on a comparison of the sequences; (c) calculating an energy score for each of the aligned peptide sequences; (d) selecting a template structure based on the calculated energy scores; and (e) assigning a predicted structure based on the selected template structure. In addition to, or as an alternative to, the template based modeling process, the predicted structure can be determined using a sequence-based machine learning algorithm, which may optionally use a neural network-based model. In some embodiments, the predicted structure can be pre-packed (“prepacked predicted structure”), wherein the prepacked predicted structure is optimized based at least in part on the selection of rotamer combinations for the peptide and MHC molecule to eliminate steric clashes. Optionally, the predicted structure can be based at least in part on templated-based modeling or experimental measurements followed by prepacking. In some embodiments, the MHC-target model can be generated by refining the predicted structure. In such embodiments, the predicted structure can comprise a coarse-grained MHC- target peptide model. The coarse-grained MHC-target model can be refined by using a computational peptide docking algorithm for computationally docking the peptide to the MHC molecule. Optionally, the computational peptide docking algorithm comprises a Monte Carlo search with minimization algorithm. The computational peptide docking algorithm can include some or all of the following steps performed in various orders, including alternative steps, and/or including intermediate steps as understood by a person skilled in the pertinent art. The computational peptide docking algorithm can include the step of: (a) modifying an energy function used to evaluate an MHC- peptide model by reducing the weight of van der Waals repulsive forces and/or increasing the weight of van der Waals attractive forces, optionally both, to an extent that will permit sampling
Attorney Docket #: 250298.000961 of alternative conformations of the MHC-peptide model while preventing a peptide of the MHC- peptide model from separating from a binding pocket within the groove of the MHC molecule during a subsequent energy minimization reconfiguration of the MHC-peptide model. The computational peptide docking algorithm can include the step of: (b) optimizing a rigid body orientation of the peptide of the MHC-peptide model by applying a random rigid body perturbation, optionally a Gaussian rigid body perturbation, comprising a rotation and/or translation to affect the orientation of the peptide within the MHC-peptide model with respect to the groove, repacking the side chains of rotamers within a peptide-MHC interface of the MHC- peptide model following the random rigid body perturbation, and applying an energy minimization step following the repacking to arrive at a reconfigured rigid body orientation, wherein the repacking comprises optimizing the selection of rotamer combinations for the peptide-MHC interface to eliminate steric clashes, wherein the energy minimization step comprises using a deterministic algorithm to find a local energy minimum, wherein the reconfigured rigid body orientation is accepted only if an energy function criterion, optionally the Metropolis criterion, is met, and wherein the applying of the random rigid body perturbation, the repacking, and the energy minimization step are sequentially repeated for a plurality of cycles, optionally a predefined number of cycles and/or until an energy criterion is satisfied. The computational peptide docking algorithm can include the step of: (c) optimizing the peptide backbone conformation, following optimization of the rigid body orientation, by applying a random torsion angle perturbation to the peptide backbone, optionally comprising a Rosetta small move or a Rosetta shear move, repacking the side chains of rotamers within the peptide-MHC interface following the random torsion angle perturbation, and applying an energy minimization step following the repacking to arrive at a reconfigured peptide backbone conformation, wherein the repacking comprises optimizing the selection of rotamer combinations for the peptide-MHC interface to eliminate steric clashes, wherein the energy minimization step comprises using a deterministic algorithm to find a local energy minimum, wherein the reconfigured peptide backbone conformation is accepted only if an energy function criterion, optionally the Metropolis criterion, is met, and wherein the applying of the random torsion angle perturbation, the repacking, and the energy minimization step are sequentially repeated for a plurality of cycles, optionally a predefined number of cycles and/or until an energy criterion is satisfied, optionally wherein the random torsion angle perturbation alternates between Rosetta small moves and Rosetta shear moves each cycle. The Metropolis
Attorney Docket #: 250298.000961 criterion includes an acceptance criterion from the metropolis algorithm. Specifically, in the context of simulated protein folding, the Metropolis criterion accepts all moves (perturbations) that lower an energy score of the protein and accepts moves (perturbations) that increase the energy score with a predetermined acceptance probability, which may decrease as the energy increase grows larger and/or as the temperature decreases. Alternative random walk algorithms and criterion can be utilized as understood by a person skilled in the pertinent art. The computational peptide docking algorithm can include the step of: (d) repeating steps (b) and (c) for a plurality of cycles wherein the van der Waals forces are gradually ramped back towards normal values such that the last cycle is performed with the normal values to arrive at one or more refined MHC- targets. In some embodiments, multiple refined MHC-target can be generated using the computational peptide docking algorithm, an alternative computational peptide docking algorithm, or another suitable algorithm as understood by a person skilled in the pertinent art. In such embodiments, the MHC-target peptide model obtained at block 110 may be selected from the multiple refined MHC-target peptides based on lowest energy score and/or highest stability of the refined MHC-targets. At block 120, a plurality of comparison MHC-off-target models are obtained such that a potential off-target peptide is respectively represented in one or more of the plurality of comparison MHC-off-target models. In order to rank different potential off-target peptides, one or more comparison MHC-off-target models may be obtained for each of a plurality of potential off-target peptides. According to various embodiments, for any given target peptide at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 35, 40, 45, 50, 60, 70, 80, 90, or 100 different potential off-target peptides are assessed (e.g., compared and/or ranked) according to one or more methods or systems disclosed herein. According to some embodiments, potential off-target peptides of peptide-MHC class I target complexes may generally comprise a different residue, optionally a positional mismatch (relative to the amino acid position within the peptide sequence), at one or more peptide positions from a given target peptide and at least 3, 4, 5, 6, 7, 8, 9, 10, or 11 identical residues to the given target peptide at the same peptide positions. Additionally or alternatively, potential off-target peptides of peptide-MHC class I target complexes may generally comprise at least 1, 2, or 3 identical residues at the same peptide positions, and at least 1 but no more than 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 different residues, optionally positional mismatches, relative
Attorney Docket #: 250298.000961 to a given target peptide. According to some embodiments, potential off-target peptides of peptide- MHC class II target complexes may generally comprise a different residue, optionally a positional mismatch, at one or more peptide positions from a given target peptide and at least 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, or 22 identical residues to the given target peptide at the same peptide positions. Additionally or alternatively, potential off-target peptides of peptide-MHC class II target complexes may generally comprise at least 1, 2, or 3 identical residues at the same peptide positions, and at least 1 but no more than 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, or 15 different residues, optionally positional mismatches, relative to a given target peptide. Potential off-target peptides generally must be of a suitable length for loading onto an MHC molecule (e.g., an HLA molecule), optionally of a suitable length specifically for loading onto an MHC class I molecule or MHC class II molecule. In some embodiments, potential off-target peptides may include peptides that are of a suitable length for loading onto an MHC molecule, as described elsewhere herein, and that comprise within their sequence a peptide sequence which itself qualifies as a potential off-target peptide (i.e. may include peptides which add one or more additional amino acids to the N-terminus and/or C-terminus of another potential off-target peptide, but which are still of a suitable length to load onto an MHC molecule). Additionally or alternatively, potential off-target peptides may be identified by comparing sequence identity to fragments of target peptides, wherein the fragments themselves are of suitable lengths for loading onto an MHC molecule (e.g., an HLA molecule), optionally of a suitable length specifically for loading onto an MHC class I molecule or MHC class II molecule. Potential off-target peptides may be limited to those that are predicted to bind an MHC molecule or particular MHC molecule (e.g., the same as the target peptide), as described elsewhere herein. In various embodiments, an off-target peptide is derivable from a human proteome, e.g., expressed in normal tissue, optionally essential, normal tissue, as described elsewhere herein. According to some embodiments, the potential off-target peptides may be no more than one amino acid shorter or longer than the target peptide, and preferably the same length as the target peptide. Each of the comparison MHC-off-target models can be obtained from a database, generated, and/or refined at block 120. In some embodiments, the off-target peptides represented by the plurality of comparison MHC-off-target models can be a large pool, theoretically all peptides represented in the human proteome or substantial subsets thereof, e.g., essential normal tissues, or tissues otherwise of concern regarding off-target toxicity. However, in practicality, the
Attorney Docket #: 250298.000961 availability of 3D computational structures of peptides (e.g., in docking conformations for binding MHC molecules) and MHC-peptide complexes is limited, and therefore database mining is not presently a practical option for evaluating excessively large pools of potential off-target peptides. Experimental determination of each of these structures on a large scale can be highly impractical, and even present computational methods for 3D computational modeling of this large of a data set can be impractical. Future advancements in database offerings, experimental techniques, and computational methods may allow for the potential off-target peptides to include larger pools of peptides. In specific examples of implementations of method 100 presented herein, however, the potential off-target peptides represented in one or more of the plurality of comparison MHC-off- target models constitute peptides pre-filtered from the larger pool of peptides so that the number of potential off-target peptides obtained at block 120 is computationally practical for 3D model comparison. In some embodiments, at block 120, the plurality of comparison MHC-off-target models are based on peptide sequences of the potential off-target peptides identified in a pre-filter step. One example of a suitable pre-filter step is the PIGSPRED method described elsewhere herein. With a pre-filter step, some or all of the plurality of comparison MHC-off-target models may be obtained experimentally. However, in practice, presently, databases of experimentally determined peptide conformations and MHC-peptide complexes are still inadequate for the purposes of determining off-target toxicity. Further, even with a pre-filter step to limit the number of off-target peptides, experimental determination for every potential off-target peptide may be impractically resource intensive if not impossible in many applications. For at least these reasons, in silico development of at least a portion of the comparison MHC-off-target models may be desirable in some applications of the method 100. Because method 100 ranks a plurality of potential off-target peptides of a target peptide, it can be preferable that the comparison MHC-off- target models are obtained via the same methodology so that the comparison MHC-off-target models may be more comparable to each other. Therefore, preferably, each of the comparison MHC-off-target models are, at least in part, computationally generated. To date, there has not existed a comprehensive database of computationally generated peptides suitable for evaluating off-target effects; however, one or more databases can be constructed according to methods disclosed herein. See, for instance, database 600 illustrated in FIG. 10. The plurality of comparison MHC-off-target models could be obtained, at block 120, from such a database.
Attorney Docket #: 250298.000961 In some embodiments, some or all of the MHC-off-target models for a given off-target peptide are derived from a predicted structure specific to the given off-target peptide (e.g., based at least in part on an amino acid sequence of the off-target peptide). In general, an MHC-off-target model may be obtained in any of the ways in which an MHC-target model is obtained. In some embodiments, one or more (e.g., all) of the off-target models for a given target peptide are obtained in the same manner as the MHC-target model for the given target peptide. In some embodiments, the plurality of comparison MHC-off-target models are obtained via a method 120 for providing one or more comparison MHC-off-target models for comparison to an MHC-target model as illustrated in FIG. 2. As described in greater detail in relation to FIG. 2, in method 120, the MHC-target peptide model is used as a template, and the peptide sequence of the template structure is substituted by the off-target sequence (a.k.a. threading technique). A coarse-grained MHC-off-target model can be generated by packing of the sidechains of off-target peptide and MHC monomers to remove internal clashing. Additionally, or alternatively, a template based modeling process can determine the predicted structure for a given off-target peptide by performing steps (a)-(e) of the template based modeling process described in relation to block 110. In addition to, or as an alternative to a template based modeling process, the predicted structure can be determined using a sequence- based machine learning algorithm, which may optionally use a neural network-based model. In some embodiments, the predicted structure can be pre-packed (“prepacked predicted structure”), wherein the prepacked predicted structure is optimized based at least in part on the selection of rotamer combinations for the peptide and MHC molecule to eliminate steric clashes. Optionally, the predicted structure can be based at least in part on a templated-based modeling or experimental measurements followed by prepacking. In some embodiments, one or more MHC-off-target models can be generated by refining the predicted structure for a respective off-target peptide. In such embodiments, the predicted structure can include a coarse-grained MHC-off-target peptide model. The coarse-grained MHC- off-target model can be refined by using a computational peptide docking algorithm. Optionally, the computational peptide docking algorithm comprises a Monte Carlo search with minimization algorithm. The computational peptide docking algorithm can include some or all of steps (a)-(c) as described in relation to block 110. The computational peptide docking algorithm can include the step of: (d) repeating steps (b) and (c) for a plurality of cycles wherein the van der Waals forces
Attorney Docket #: 250298.000961 are gradually ramped back towards normal values such that the last cycle is performed with the normal values to arrive at one or more refined MHC-off-target models for a given off-target peptide. In some embodiments, multiple refined MHC-off-target models for a given off-target peptide can be generated using the computational peptide docking algorithm, an alternative computational peptide docking algorithm, or another suitable algorithm as understood by a person skilled in the pertinent art. In such embodiments, the MHC-off-target peptide models obtained at block 120 may be selected from the multiple refined MHC-target peptides based on lowest energy score and/or highest stability of the refined MHC-off-target models. In some embodiments, the MHC-target model obtained at block 110 and the MHC-off- target models obtained at block 120 are generated using identical or similar methodologies. For instance, a predictive structure (e.g., coarse-grained model) can be obtained via the same methodology for each target peptide and off-target peptide, and the predictive structure can be refined by the same refinement methodology each target peptide and off-target peptide. At block 130, a structural similarly metric for each of the potential off-target peptides is computed. The structural similarity metric indicates a degree of similarity between the MHC- target model and some or all of the comparison MHC-off-target models associated with a respective potential off-target peptide. In some embodiments, the structural similarity metric can be calculated according to method 130 illustrated in FIG. 3. At block 140, the potential off-target peptides can be ranked based at least in part, optionally entirely, on the structural similarity metric. In some embodiments, off-target peptides can be ranked according to method 140 illustrated in FIG. 4. FIG. 2 is a flow diagram illustrating an exemplary method for providing one or more comparison MHC-off-target models for comparison to an MHC-target model. The one or more comparison MHC-off-target models represent a single off-target peptide in a groove of an MHC molecule. The MHC-off-target models can be used to rank potential off-target peptides, such as described in relation to the method 100 of FIG. 1. Additionally, or alternatively, the one or more comparison MHC-off-target models may be useful for other in silico analysis and testing, particularly if models of other structures, such as antigen-recognition molecules, are available for comparison and/or interaction studies.
Attorney Docket #: 250298.000961 At block 121, a coarse-grained MHC-off-target model is generated by a substituting, in the MHC-target model, a sequence of the potential off-target peptide in place of a sequence of the target peptide. Amino acids of the target peptide are replaced by amino acids of the off-target peptide in corresponding positions. The MHC-target model is preferably experimentally generated; however, the MHC-target model may be obtained by other means, for instance as described in greater detail in relation to block 110 of FIG. 1. Methods for performing computational protein structure prediction / protein folding by “threading” (aligning) a peptide sequence onto a known structural template are well known in the art. See, e.g., Xu, Y., Liu, Z., Cai, L., Xu, D. (2007). Protein Structure Prediction by Protein Threading. In: Xu, Y., Xu, D., Liang, J. (eds) Computational Methods for Protein Structure Prediction and Modeling. Biological and Medical Physics, Biomedical Engineering. Springer, New York, NY. https://doi.org/10.1007/978-0-387-68825-1_1, which his herein incorporated by reference in its entirety. Unlike other applications of protein structure prediction methods based on protein threading, the selection of the target peptide structure for use as the template structure in method 120 is not based on the template necessarily being the best fit when assessed via objective measures, e.g., energy calculations, that are agnostic to the intended use of the 3D model (agnostic to the identity of the target peptide) and/or may not have the highest sequence identity to the off- target peptide as compared to other available templates. However, using the target peptide as a template for threading may bias or prioritize the docking conformation of the target peptide-MHC complex within a noisy solution space of conformations and help ensure that off-target peptide- MHC complex conformations which more closely resemble that of the peptide-MHC complex conformation are analyzed, as these conformations may be more likely to induce off-target effects (via binding of antigen-recognition molecules targeting the target peptide MHC complex) according to the molecular mimicry theory. As a consequence of block 121, the coarse-grained MHC-off-target model is dependent upon the associated target peptide, meaning that for a theoretical off-target peptide which is modeled separately for two different targets, there should be generated two distinct coarse-grained MHC off-target models, one for each of the two targets. In some embodiments, after substitution of the sequence of the potential off-target peptide in place of a sequence of the target peptide, sidechains of the off-target peptide and MHC monomers can be packed to remove internal clashing. Additionally, or alternatively selection of rotamer
Attorney Docket #: 250298.000961 combinations for the off-target peptide and MHC molecule can be optimized to eliminate steric clashes. At block 122, a plurality of refined MHC-off-target models can be generated by computationally optimizing the coarse-grained MHC-off-target model multiple times such that each optimization of the coarse-grained MHC-off-target model results in a respective refined MHC-off-target model of the plurality of refined MHC-off-target models. Therefore block 122 generates a plurality of refined MHC-off-target models that represent a single potential off-target peptide in a groove of an MHC molecule. In some embodiments, each coarse-grained MHC-off-target model can be refined using a computational peptide docking algorithm to optimize peptide-MHC backbone and side chains. The peptide docking algorithm may use random conformation sampling and/or deterministic methods to refine the model. Optionally, the computational peptide docking algorithm performs Monte-Carlo sampling with minimization approach of the backbone and on-the-fly side-chain optimization thereby generating multiple models of an off-target peptide-MHC complex from a single coarse-grained MHC-off target model. The computational peptide docking algorithm can include some or all of steps (a)-(c) as described in relation to block 110. The computational peptide docking algorithm can include the step of: (d) repeating steps (b) and (c) for a plurality of cycles wherein the van der Waals forces are gradually ramped back towards normal values such that the last cycle is performed with the normal values to arrive at the plurality of refined MHC-off-target models for a given coarse-grained MHC off-target model. In some embodiments, multiple refined MHC-off-target models for a given off-target peptide can be generated using an alternative computational peptide docking algorithm or other suitable refinement algorithm as understood by a person skilled in the pertinent art. In some embodiments, the computational peptide docking algorithm (or other suitable algorithm) is repeated multiple times, optionally at least 100, 200, 500, 1,000, 2,000, 5,000, or 10,000 times, to produce the refined MHC-off-target models. In some embodiments, at least 100, 200, 500, 1,000, 2,000, 5,000, or 10,000 refined MHC-off-target models can be generated based at least in part on the coarse-grained MHC-off-target model. At block 123, one or more comparison MHC-off-target models may be selected from the plurality of refined MHC-off-target models for further analysis (e.g., comparison to the MHC- target model). The one or more comparison MHC-off-target models selected may be only a portion
Attorney Docket #: 250298.000961 of the plurality of refined MHC-off-target models selected, for example, a portion having the relatively lower energy calculations than the non-selected portion. For instance, the one or more comparison MHC-off-target models selected may have lower energy than a majority of the plurality of the refined MHC-off-target models. As such, the one or more comparison MHC-off- target models provided by method 120 include relatively lower energy and/or more stable refined MHC-off-target models. In some embodiments, the one or more comparison MHC-off-target models may include models from at least the top 1%, 5%, or 10% lowest-energy and/or most stable refined MHC-off-target models. In some embodiments, the one or more comparison MHC-off- target models may include only models from the top 1%, 5%, or 10% lowest-energy and/or most stable refined MHC-off-target models. In some embodiments, the one or more comparison MHC- off-target models may include only models from at least the top 5, 10, or 100 lowest-energy and/or most stable refined MHC-off-target models. In some embodiments, the one or more comparison MHC-off-target models may include only models from the top 5, 10, or 100 lowest-energy and/or most stable refined MHC-off-target models. In some embodiments, for each of the refined MHC-off-target models, a series of metrics is computed that helps to identify the lowest energy models. For instance, FlexPepDock protocol is an example computational peptide docking algorithm that provides a reweighted score metric that can be used to select lowest energy stable models among the plurality of refined MHC-off- target models. Reweighted score is defined by the FlexPepDock protocol as a linear sum of a total score of the complex, interface score (energy of the pair-wise interactions across the peptide-MHC interface), and peptide score (sum of an energy function over the peptide residues). The lower the reweighted score the more stable is the predicted peptide-MHC model. In some embodiments, at least five (e.g., only five) lowest re-weighted score models of the plurality of refined MHC-off-target models are selected such that method 120 provides at least five (e.g., only five) comparison MHC-off-target models. FIG. 3 is a flow diagram illustrating an exemplary method 130 for quantifying structural similarity between a potential off-target peptide in a groove of an MHC molecule and a target peptide in complex with the MHC molecule for the purposes of antigen-recognition molecule binding. At block 131, a plurality of comparison MHC-off-target models is obtained. The plurality of comparison MHC-off-target models can be obtained by methods described in relation
Attorney Docket #: 250298.000961 to block 120 of FIG. 1, by method 120 in FIG. 2, other methods for obtaining MHC-off-target models disclosed herein, variations thereof, alternatives thereto, or other suitable methods as understood by a person skilled in the pertinent art. The plurality of comparison MHC-off target models represent a single potential off-target peptide in a groove of an MHC molecule. At block 132, for each of the plurality of comparison MHC-off-target models, one or more structural similarity metrics are calculated. Each structural similarity metric has a corresponding value for each of the plurality of comparison MHC-off-target models and represents structural similarity between each of the plurality of comparison MHC-off-target models and an MHC-target model. The one or more structural similarity metrics can include metrics associated with protein structure comparison such as distance-based measures of protein structure similarity, contact-based measures of protein structure similarity, measures of protein structure similarity derived via geometric deep learning , and other suitable meausures as understood by a person skilled in the pertinent art. Distance-based measures provide a difference value which quantifies distances between atoms, or groups of atoms (e.g., a residue), of the MHC-target model and corresponding atoms, or groups of atoms, of a respective comparison MHC-off-target model of the plurality of comparison MHC-off-target models when aligned or superimposed, including by methods or tools well known in the art (e.g., when optimally aligned in a manner that minimizes one or more structural similarity metrics, such as a distance-based measure). Where distances are quantified between groups of atoms, a geometric or weighted center may be used for calculating distances. The difference value itself can be used as a structural similarity metric or a structural similarity metric can be calculated based on the difference value. For distance-based measures, the methodology and the atoms, or groups of atoms, considered can affect the difference value. Root mean square deviation (RMSD) is a distance-based comparison methodology that can be used to generate quantitative metrics to measure the average distance between atoms, or groups of atoms, of superimposed protein structures. Generally, a lower RMSD value indicates higher similarity in the 3D conformations between two structures. RMSD, other suitable distance- based comparison methodology as understood by a person skilled in the pertinent art, or combinations thereof can be utilized at block 132 to generate structural similarity metric(s). In one embodiment, values of up to four similarity metrics (e.g., 1, 2, 3, or 4 similarity metrics) are calculated for each MHC-off-target model as described below.
Attorney Docket #: 250298.000961 A first example similarity metric is an RMSD metric which quantifies similarity of the overall peptide-MHC complex structure that includes the peptide and the peptide binding groove region (e.g., the α1 and α2 domains of MHC class I molecules or the α1 and β1 domains of MHC class II molecules). The first example similarity metric for a given MHC-off-target model is the RMSD between the MHC-off-target model and MHC-target model considering the aforementioned structures of each model. A second example similarity metric is an RMSD metric which quantifies similarity of the peptide conformation in the MHC groove. The second example similarity metric for a given MHC- off-target model is the RMSD between the MHC-off target model and the MHC-target model considering the peptide conformation of each model. A third example similarity metric is an RMSD metric to quantify similarity of the conformation of the peptide residue positions determined or predicted to be important (i.e. available) for TCR/Ab interaction. The value of the third example similarity metric for a given MHC-off-target model is the RMSD between the MHC-off target model and the MHC-target model considering conformation of the peptide residue positions important for TCR/Ab interaction of each model. The third example similarity metric quantifies distances between atoms or residues of the MHC-target model at each amino acid position that has been determined or predicted to be available for binding by an antigen-recognizing molecule to a peptide-MHC (pMHC) complex comprising the target peptide bound to the MHC molecule and all corresponding atoms of the potential off-target peptide backbones of the plurality of comparison MHC-off-target models at each of the corresponding positions. In some embodiments, such residues or positions determined or predicted to be available for binding by an antigen-recognizing molecule to a pMHC complex may be identified according to the methods described in WO2023122621A2, which is herein incorporated by reference in its entirety. Other suitable similarity metrics may include RMSD metrics that quantify similarity of structural portions of pMHC complexes intermediate the first and third examples or encompassing of first, second, third, or all of the examples. For instance, the residues or atoms considered in calculating RMSD may include at least peptide residues available for binding by an antigen- recognizing molecule when in the peptide binding groove of an MHC molecule, at least all the peptide residues, at least all the residues of a peptide binding groove (e.g., at least all the residues of the α1 and α2 domains of an MHC class I molecule or the α1 and β1 domains of an MHC class
Attorney Docket #: 250298.000961 II molecule), or combinations thereof. For example, the RMSD may be calculated for atoms or residues that include all of the peptide and at least a portion of the α1 and α2 domains of an MHC class I molecule or of the α1 and β1 domains of an MHC class II molecule. RMSD metrics may be quantified by computationally aligning/superimposing in three- dimensional space a given MHC-target model with a particular MHC-off-target model (e.g., using Pymol or another suitable molecular visualization tool). The models may be aligned based on sequence similarity (e.g., using the Pymol “align” function”) and/or structural similarity (e.g., using the Pymol “super” function). In some instances, whichever alignment/superimposition function minimizes the RMSD may be used. Preferably, the models may be aligned/superimposed without any further refinement of the molecular structures, such as removal of atoms (e.g., with “cycles” set equal to 0 within Pymol). Upon alignment/superimposition, the RMSD may be calculated between selected atoms. The first three example metrics demonstrate how selection of atoms for calculation of a structural symmetry metric can affect the value of the structural symmetry metric. Other considerations for atom selection can include consideration of heavy atoms only and/or atoms in the peptide backbone only. For each of the first, second, and third metric or any RMSD metric in general, optionally, the similarity metric quantifies distances between heavy atoms only. For each of the first, second, and third metric or any RMSD metric in general, optionally, the similarity metric quantifies distances between heavy atoms of the target peptide backbone of the MHC-target model and all corresponding heavy atoms of each of the potential off-target peptide backbones of the plurality of comparison MHC-off-target models. A fourth example similarity metric is determined based on a quantification of a correlation between a molecular surface interaction fingerprint of the MHC-target model and each of the plurality of comparison MHC-off-target models. The molecular surface interaction fingerprint includes at least one vector that characterizes an interaction probability of a respective model with an antigen-binding molecule based on geometric features and/or chemical features of the molecular surface. Each element of the molecular surface interaction fingerprint vector may comprise an interaction probability (a scalar value, e.g., greater than or equal to 0 and less than or equal to 1) associated with a particular portion of the structure’s surface. The molecular surface may be partitioned into vertices and/or patches, such as geodesic patches centered around each vertex (e.g., of fixed geodesic distances between 5-25, 5-20, 5-15, 5-10, 10-25, 10-20, 10-15, 15-
Attorney Docket #: 250298.000961 25, 15-20, or 20-25 Angstroms, optionally 9-12 Angstroms). Optionally, the correlation quantification includes a correlation coefficient such as a Pearson correlation coefficient. Optionally, the geometric features comprise shape index or distance-dependent curvature and/or the chemical features comprise hydropathy, continuum electrostatics, or location of free electrons and proton donors. In some embodiments, a molecular surface interaction fingerprinting (MaSIF) tool, which is based on a geometric deep learning method, is used to capture fingerprints that are important for specific biomolecular interactions that can be used to produce structural similarity metrics. MaSIF is described in greater detail in Gainza, Pablo, et al. "Deciphering interaction fingerprints from protein molecular surfaces using geometric deep learning." Nature Methods 17.2 (2020): 184-192, which is herein incorporated by reference in its entirety. In some embodiments, to calculate the fourth example similarity metric, first, a geometric deep learning tool such as the MaSIF tool is used to compute a plurality of interaction fingerprints, or interaction scores, for the MHC-target model as well as for each of the plurality of comparison MHC-off-target models (see, e.g., the “MaSIF-site” application described in Gainza et al, supra). The interaction fingerprints are computed for at least a portion of the peptide-MHC molecular surface that includes the peptide and at least a portion of the MHC molecule, including the binding groove region (optionally, including only the binding groove region, such as the α1 and α2 domains of an MHC class I molecule or the α1 and β1 domains of an MHC class II molecule). Each interaction fingerprint may be scalar value representing an interaction score that characterizes an interaction probability for a particular portion of the peptide-MHC molecular surface (a “patch”). An interaction score may represent a probability or likelihood of a particular patch being involved in a protein-protein interaction (e.g., any generic non-covalent interaction) based on one or more surface properties as described elsewhere herein, optionally derived from a machine learning model trained on data sets of known interacting and non-interacting surfaces. In some embodiments, each interaction fingerprint may be a vector that characterizes an interaction probability for patch (e.g., based on different measures). The patches are then optimally aligned in three-dimensional space and corresponding patches are identified based on the spatial alignment (the number of corresponding patches being less than or equal to the total number of patches for whichever model has the smaller number of total patches). The alignment can be performed, for example, using Pyoints, a python package to process and analyze point cloud data, voxels, and raster images, or other suitable data
Attorney Docket #: 250298.000961 fusing/processing package as understood by a person skilled in the pertinent art. The molecular surfaces of each of the models being compared may be represented as point cloud data. In various implementations, only points that can be perfectly aligned are considered to be corresponding vertices/patches. An aggregate molecular surface interaction fingerprint (MSIF), represented as a vector, that characterizes the spatially distributed interaction probabilities across corresponding patches is then computed for each of the models being compared. For instance, each vector may be generated to comprise only interaction fingerprints or interaction scores for corresponding patches/vertices, wherein corresponding patches are arranged within the vectors in the same order. Next, a correlation value (e.g., a correlation coefficient such as a Pearson correlation coefficient) of the interaction probabilities of the patches is computed. The correlation can be performed, for instance using Pyoints. The value of the fourth example similarity metric for a given MHC-off- target model is based on the correlation value. A high correlation value indicates that the off- target-MHC complex surface has a similar molecular surface interaction fingerprint (MSIF) to that of the target-MHC complex surface. In some embodiments, the molecular surface interaction fingerprint (MSIF) is determined by: a) decomposing a surface of the respective model into a plurality of overlapping geodesic patches; b) mapping the one or more surface features of the respective model to each of the geodesic patches using polar geodesic coordinates, wherein the one or more surface features comprise one or more, optionally all, of the following features: shape index, distance-dependent curvature, hydropathy, continuum electrostatics, and location of free electrons and proton donors; c) applying a convolutional neural network (e.g., comprising a plurality of convolutional layers, such as at least 2 or 3 convolutional layers) to each patch to produce an interaction score for each patch, wherein applying the convolutional neural network comprises rotating the geodesic patch to provide rotation invariance, and wherein the convolutional neural network has been trained on i) portions of surfaces (e.g., patches) that form interface regions between pairs of proteins that are known binding partners and ii) portions of surfaces (e.g., patches) that do not form interface regions between pairs of proteins, optionally wherein the interface regions are solvent inaccessible regions in complexes of the known binding partners and optionally wherein the proportions of surfaces that do not form interface regions are derived from one or both of the known binding partners; and d) determining the molecular surface interaction fingerprint based at least in part on the interaction scores of the plurality of overlapping geodesic patches, wherein determining the
Attorney Docket #: 250298.000961 molecular surface interaction fingerprint comprises identifying spatially corresponding patches between the MHC-target model and each respective MHC off-target model and wherein the molecular surface interaction fingerprint comprises the interaction scores for the corresponding patches, optionally wherein determining the molecular surface interaction fingerprint comprises removing interaction scores that are not associated with a corresponding patch. Determining the molecular surface interaction fingerprints (MSIFs) based on the interaction scores for the plurality of patches for comparison may be performed as described elsewhere herein (e.g., using Pyoints or other suitable software to align patches and identify corresponding patches). At block 133, for each of the one or more structural similarity metrics, a single composite structural similarity metric is calculated. The single composite structural similarity metric has a value based at least in part on the corresponding structural similarity metric values for at least a portion of the plurality of comparison MHC-off-target models. In some embodiments, the plurality of comparison MHC-off-target models for a given off-target peptide comprises a predetermined number of models, optionally, at least 5. In some embodiments, the number of comparison MHC-off-target models for the potential off-target peptide can be selected from a plurality of refined MHC-off-target models as described in greater detail in relation to block 123 of FIG. 2. In some embodiments, the one or more comparison MHC-off-target models may include at least 5, 10, 50, 100, 500, 1,000, 5,000, or 10,000 MHC- off-target models. In some embodiments, the portion of the plurality of comparison MHC-off- target models for which a composite structural similarity metric is calculated for a given off-target peptide comprises a predetermined number of models, optionally, at least 5. In some embodiments, the portion may include at least 5, 10, 50, or 100 MHC-off-target models. The comparison MHC-off-target models included in the selected portion may have better metric values (indicative of more structural similarity) than those comparison MHC-off-target models not selected, as described elsewhere herein. In some embodiments, the composite structural similarity metric of each of the one or more structural similarity metrics is based at least in part on an aggregate measure of a respective structural similar metric for each of the plurality of comparison MHC-off-target models. The aggregate measure can include an average, median, minimum (e.g., minimum RMSD), or maximum (e.g., maximum correlation coefficient).
Attorney Docket #: 250298.000961 For instance, in an implementation of the method 130 having the four example structural similarity metrics, respective values for each of the first, second, third, and fourth example structural similarity metrics can be calculated for each of the comparison MHC-off-target models. Then, a composite structural similarity metric can be calculated for each of the four example structural similarity metrics. The same principle can be applied to any of the example structural similarity metrics and/or other structural similarity metrics as understood by a person skilled in the pertinent art. FIG. 4 is a flow diagram illustrating an exemplary method 140 for identifying potential off-target peptide(s) based on sequence similarity and structural similarity for an antigen- recognition molecule that recognizes a target peptide presented in complex with an MHC molecule (MHC-target peptide complex). At block 141, a pool of peptides of suitable length is obtained. Optionally, peptides of the pool of peptides are expressed in normal tissues, optionally, essential, normal tissues. In some embodiments, the pool of peptides can be assembled based on canonical human protein sequences in a medical research database such as, as a non-limiting example, UniprotKB. In some embodiments, the peptide length is 9 mer, 10 mer, 11 mer, or 12 mer. In some embodiments, the peptide length is the same length as that of the target peptide. At block 142, high sequence similarity peptides are identified from within the pool. The identified high sequence similarity peptides may (i) have higher sequence similarity to the target peptide than a majority of peptides within the pool and (ii) have a binding affinity to the MHC molecule greater than a threshold value. High similarity peptides identified at block 142 can be based on one or more of the following: (a) a degree of similarity between a sequence of a peptide within the pool and a target peptide sequence of the target peptide, optionally wherein a threshold degree of similarity for selection requires one or more residue mismatches and/or requires one or more identical residues; (b) a number of identical residues, optionally at least three, at positions within the target peptide sequence that have been determined to be available for binding by an antigen-recognizing molecule to a peptide-MHC (pMHC) complex comprising the target peptide bound to the MHC molecule, optionally wherein the positions available for binding an antigen-recognizing molecule have been predicted by a mutagenesis screen to be those positions that, based on changes in binding affinity, are not
Attorney Docket #: 250298.000961 involved in binding the MHC molecule, optionally wherein the mutagenesis screen is a computational mutagenesis screen and those positions that are not involved in binding the MHC molecule are determined by a machine learning model, such as an artificial neural network, trained on peptide-MHC binding data; (c) binding affinity to the MHC molecule, wherein peptides within the pool that are determined to have a binding affinity below a threshold binding affinity are not selected, optionally wherein binding affinity is determined by a machine learning model, such as an artificial neural network, trained on peptide-MHC binding data; and (d) tissue expression, wherein peptides within the pool that are not expressed in normal tissue (e.g. as determined by a threshold expression level), optionally not in essential normal tissue, are not selected. In some embodiments, the proteome is a human proteome and the normal tissue or essential tissue is human tissue. The high sequence similarity peptides can include at least two amino acids that (i) are located at positions corresponding to positions within the target peptide that are predicted to be available to interact with an antigen-recognition molecule and (ii) are identical to the corresponding amino acids of the target peptide. In some specific embodiments, high sequence similarities may include at least 3, 4, 5 or more amino acids that (i) are located at positions corresponding to positions within the target peptide that are predicted to be available to interact with an antigen-recognition molecule and (ii) are identical to the corresponding amino acids of the target peptide. Sequence similarity may be further evaluated by assessing Degree of Similarity (DoS) or overall peptide sequence similarity across all peptide positions, wherein peptides having more amino acids that are identical to the amino acids at corresponding positions in the target peptide have a higher DoS and are more similar in sequence. In some embodiments the high sequence similarity peptides are identified using the PIGSPRED method described elsewhere herein. At block 143, the potential off-target peptide(s) are identified by selecting, within the high sequence similarity peptides, peptide(s) that are more structurally similar, when positioned in a groove of the MHC molecule, to the MHC-target peptide complex than a majority of the higher sequence similarity peptides. In some embodiments, the selected structurally similar peptides are in the top 50th, 40th, 30th, 20th, 10th, 5th, 2nd, or 1st percentiles of the high sequence similarity peptides. Preferably, the total number of the structurally similar peptides is a manageable number for further in vivo or in vitro testing. In examples presented herein, known off-target peptides were
Attorney Docket #: 250298.000961 likely to be found by considering the top 20th percentile. In some embodiments, the percentile considered is between the 20th and 1st percentiles such that the selected structurally similar peptides are in the top 20th, 19th, 18th, 17th 16th, 15th, 14th, 13th, 12th, 11th, 10th, 9th, 8th, 7th, 6th, 5th, 4th, 3rd, 2nd, or 1st percentiles of the high sequence similarity peptides. In some embodiments, the method 140 can include quantifying structural similarity between each of the high sequence similarity peptides in a groove of the MHC molecule and the target peptide in the groove of the MHC molecule for the purposes of antigen-recognition molecule binding according to method 130 illustrated in FIG. 3 and/or as described in relation to block 130 of FIG. 1. In some embodiments, the potential off-target peptides are ranked based on structural similarity and a portion of the highest ranking (i.e., most structurally similar) potential off-target peptide(s) are selected. FIG. 5 is a flow diagram illustrating an exemplary method for ranking potential target peptides to mitigate off-target toxicity. At block 210, two or more potential target peptides, among disease-associated peptides, that are predicted to bind to an MHC molecule are obtained. Disease-specific MHC-target peptide complexes can be identified by ascertaining genes that are specifically expressed in diseased tissue. In some embodiments, the disease-specific MHC-target peptide complex can be identified based on a medical database such as The Cancer Genome Atlas (TCGA) and Genome Tissue Expression Database (GTEx), which include human gene sequences. In some embodiments, a gene that is expressed in a cancer type at 75-percentile TPM value > 2 and is negligibly expressed in all essential, normal tissues or essential cell types in the GTEx is considered a cancer-specific gene. The canonical protein sequence corresponding to the cancer-specific gene can be derived from a medical research database including canonical human protein sequences such as the UniProtKB database. In some embodiments, the two or more potential target peptides are selected from a database such as database 600 illustrated in FIG. 10. In some embodiments, the two or more off- target peptides are selected based on having a low risk metric. In some embodiments, the risk metric is based on a predicted off-target toxicity as determined by the PIGSPRED method, described elsewhere herein, or other computational method. At block 220, a respective list of potential off-target peptides is obtained for each of the potential target peptides. The respective list of potential off-target peptides can be obtained via
Attorney Docket #: 250298.000961 the PIGSPRED method, from a database, such as database 600, as disclosed elsewhere herein, and/or by other suitable method as understood by a person skilled in the pertinent art. At block 230, potential off-target peptides within the respective list of off-target peptides are ranked, for each of the potential target peptides, based at least in part on structural similarity of each potential off-target peptide to the potential target peptide. The potential off-target peptides can be ranked as disclosed elsewhere herein. For instance, one or more structural similarity metrics can be calculated for each potential off-target peptide, and the potential off-target peptides can be ranked from most structurally similar to least structurally similar based at least in part on the structural similarity metrics. At block 240, the two or more potential target peptides can be ranked based at least in part on the ranking of potential off-target peptides within the respective list of potential off-target peptides. In some embodiments, the potential target peptides can be ranked according to the number of potential off-target peptides having at least one structural similarity metric above a predetermined threshold for that target peptide. For example, a first target peptide may be ranked higher than a second target peptide, where the first target peptide is predicted, based at least in part on the methods described herein, to have fewer potential off-target peptides satisfying a structural similarity criterion (e.g., an RMSD between potential off-target peptide backbones and the respective target peptide backbone lower than a threshold RMSD value) than the second target peptide and where the ranking predicts a likelihood of off-target toxicity or related consideration. A ranking of potential target peptides may be used to select one or more target peptides for therapeutic development (e.g., development of antigen-recognition molecules against the target peptide). FIG. 6 is a block diagram of an exemplary system 300 for development of an antigen recognition molecule. An in silico (i.e., computational) system 310 ranks potential target peptides and/or potential off-target peptides for the purpose of providing recommended target and/or off-target peptides for further testing and development. In vivo system 330 synthesizes target and off-target peptides and/or MHC-peptide complexes for development and testing of antigen recognition molecules. As illustrated, the in silico system 310 receives target peptide sequence(s) 311 of potential target peptides as an input. A sequence-based target ranking engine 312 ranks the potential target
Attorney Docket #: 250298.000961 peptides. In some embodiments, the sequence-based target ranking engine 312 utilizes aspects of the PIGSPRED method, described elsewhere herein, to rank the target peptides. In some embodiments, the sequence-based target ranking engine 312 compares the received target peptide sequences 311 to a database, such as database 600, and selects potential target peptides based on having low risk metric. The sequence-based target ranking engine 312 outputs one or more low risk target sequence(s) 313 and associate high sequence similarity off-target sequences 314. The in silico system 310 includes a structure-based off-target ranking engine 400 configured to rank the high sequence similarity off-target sequences 314 based on structural similarity to the target peptide. In some embodiments, the structure-based off-target ranking engine 400 is configured similar to the structure-based off-target ranking engine 400 is illustrated in FIG. 7. In some embodiments, the structure-based off-target ranking engine 400 is configured to computationally execute steps of method 100 illustrated in FIG. 1. In some embodiments, the structure-based off-target ranking engine 400 is configured to rank the low risk target sequence(s) 313 according to method 200 illustrated in FIG. 6. The structure-based off-target ranking engine 400 is configured to output recommended target sequence(s) 316, which may (or may not) be ranked, and may (or may not) include a filtered list of prioritized potential target sequences from the target sequence(s) 313 input to the structure-based off-target ranking engine 400. The structure-based off-target ranking engine 400 is configured to output recommended off-target sequences 317. Although not illustrated, the structure-based off-target ranking engine 400 may optionally output MHC-target models for each of the recommended target sequence(s) 316 and/or MHC-off-target models for each of the recommended off-target sequences 317. The recommended target sequence(s) 316 the recommended off-target sequences 317 can be used to identify peptides for an in vivo synthesis process 331 which synthesizes antigen recognition molecules 332 and MHC-off-target peptide complexes 333, which in turn, can be fed into an in vivo testing process 334, which can provide one or more recommended antigen recognition molecule(s) 335 as an output. FIG.7 is a block diagram of an exemplary structure-based off-target ranking engine 400. The structure-based off-target ranking engine 400 receives as a first input 313, an MHC and target peptide sequence or an MHC-target model. The structure-based off-target ranking engine 400 receives, as a second input 314, potential off-target sequences associated with the target peptide of the first input 313. The structure-based off-target ranking engine 400 may be configured to receive
Attorney Docket #: 250298.000961 an optional third input 401 including residue positions of the target peptide available for binding to an antigen recognition molecule when in an MHC-target peptide complex. The available positions may be determined by the PIGSPRED method, described elsewhere herein, by identifying amino acid positions of the target peptide unbound to the MHC molecule in the MHC- target model, by experimental methods, and/or by other suitable method as understood by a person skilled in the pertinent art. The structure-based off-target ranking engine 400 can include an off-target model engine 410 configured to generate one or more comparison MHC-off-target models for each off-target peptide. In some embodiments, the off-target model engine 410 is configured to execute method 120 in FIG.2 for each off-target peptide sequence in the second input 314. In some embodiments, the off-target model engine 410 can also generate an MHC-target model if such a model is not provided in the first input 313. Note that the outputs of the off-target model engine 410 may be useful for other purposes beyond the structure-based off-target ranking engine 400 as illustrated, such as in silico evaluation of potential antigen-recognition molecules, comparison to experimentally determined models, and other applications as understood to a person skilled in the pertinent art. Additional use cases are likely to become apparent as computational processing continues to become more available and the field of bioinformatics continues to advance. The off-target model engine 410 can include a coarse-grain model builder 411 configured to generate a coarse-grained MHC-off-target model for each off-target peptide sequence provided in the second input 314. In some embodiments, the coarse-grain model builder 411 is further configured to generate a coarse-grained MHC-target model if an MHC-target model is not provided in the first input 313. In some embodiments, the coarse-grain model builder 411 is configured to execute computational methods as described in relation to block 121 of FIG. 2. The off-target model engine 410 can include a model refinement module 412. In some embodiments, the model refinement module is configured to generate one or more refined MHC- off-target models for each coarse-grained MHC-off-target model. The model refinement module 412 may also be configured to generate one or more refined MHC target model if a coarse-grained MHC-off-target model is provided by the coarse-grain model builder 411. In some embodiments, the model refinement module 412 is configured to computationally execute steps associated with block 122 of FIG. 2.
Attorney Docket #: 250298.000961 The off-target model engine 410 can include a model selection module 413. The model selection module 413 is configured to select at least one of the refined MHC-off-target models for each off-target peptide. The model selection module 413 may also select a refined MHC-target model if more than one refined MHC-target model is provided from the model refinement module 412. The selected refined MHC-off-target model(s) are provided as comparison MHC-off-target models for comparison to the MHC-target model. In some embodiments, the model selection module 413 is configured to computationally execute steps associated with block 123 of FIG. 2. The structure-based off-target ranking engine 400 can include a similarity engine 420 configured to calculate one or more structural similarity metrics for each off-target peptide of the second input 314. In some embodiments, the similarity engine 420 is configured to calculate each of the structural similarity metric(s) based at least in part on a comparison of the 3D structure of the MHC-target model to each of the MHC-off-target models, or at least a portion of the MHC- off-target models per off-target peptide. In some embodiments, the similarity engine 420 is configured to computationally execute steps of method 130 illustrated in FIG. 3. The similarity engine 420 can include a geometric similarity metric calculator 421 and/or an RMSD structural similarity metric calculator 422. The similarity engine 420 can include additional and/or alternative structural similarity metric calculator(s) that are able to calculate a structural similarity metric as understood by a person skilled in the pertinent art. In some embodiments, the geometric structural similarity metric calculator 421 and the RMSD structural similarity metric calculator 422 are configured to execute steps associated with block 132 of FIG. 3. The geometric similarity calculator can utilize geometric deep learning algorithms, such as MaSIF, to calculate a structural similarity metric (e.g., based on geometric and/or chemical features of the molecular surface, such as shape index, distance-dependent curvature, hydropathy, continuum electrostatics, location of free electrons and proton donors, etc.) as disclosed elsewhere herein, alternatives thereto, and variations thereof as understood by a person skilled in the pertinent art. In embodiments in which the third input 401 including important residue positions are provided as an input to the structure-based off-target ranking engine 400, the RMSD structural similarity metric calculator 422 can calculate a structural similarity metric based only on positions of the target peptide important and/or available for antigen recognition molecule binding (e.g., positions of the target peptide unbound to the MHC molecule in the MHC-target peptide complex).
Attorney Docket #: 250298.000961 The similarity engine 420 can include a similarity metric calculator 423. In some embodiments, the similarity metric calculator 423 is configured to calculate a single composite structural similarity metric having a value based at least in part on the corresponding structural similarity metric values for at least a portion of the plurality of comparison MHC-off-target models. In some embodiments, the similarity metric calculator 423 is configured to computationally execute steps associated with block 133 of FIG. 3. The structure-based off-target ranking engine 400 can include a ranking engine 430. The ranking engine is configured to rank, and optionally filter, the off-target peptides based at least in part on the structural similarity metrics calculated by the similarity engine 420. The ranking engine 430 includes a ranking module 431 configured to provide an off- target similarity ranking output 411. Examples 1 and 2 provided herein are illustrative examples of how potential peptides can be ranked according to various similarity metrics by the ranking module 431. In some embodiments, the ranking engine 430 is configured to computationally execute steps associated with block 140 of FIG.1. In some embodiments, the off-target similarity ranking 441 can be stored in a database such as database 600 illustrated in FIG. 10. Optionally, the ranking engine 430 can include a selection module 432 configured to select prioritized potential off-target sequences from the second input 314 for further analysis and/or testing. In some embodiments, the selection module 432 is configured to provide the selected potential off-target sequences as a reduced off-target peptide list 442. The reduced off- target peptide list can be used as recommended off-target sequences 317 and provided to an in vivo system 330 for further analysis and testing as described in relation to FIG. 6. FIG. 8 is a block diagram of an embodiment of the structure-based off-target prediction engine 400. FIG. 8 may also be considered as an example method flow chart illustrating aspects of computational methods disclosed herein. The first input (input-1), corresponds to the first input 313 of FIG. 7. In the illustrated embodiment, the first input includes off-target peptides predicted by the PIGSPRED method. The second input (input-2) corresponds to the second input 314 of FIG. 7. In the illustrated embodiment, the second input can include an experimentally solved target-peptide-MHC complex or a computationally modeled target-peptide-MHC complex. The optional third input (input-3) corresponds to the optional third input 401 of FIG. 7. In the illustrated embodiment, the third input includes peptide residue positions important TCR/Ab interaction as determined by any experimental approach.
Attorney Docket #: 250298.000961 The structure-based off-target prediction engine includes two coarse-grained model creation approaches, which correspond to the coarse-grain model builder 411 of FIG. 7. In a first approach, a coarse-grained model is created using Rosetta in two steps. First, a threading approach is utilized in which a template peptide sequence is substituted with the target peptide sequence to build a coarse-grained model. In some examples, the MHC-target model can be used as the template for off-target peptides such that the amino acids of the MHC-target model are replaced by amino acids at corresponding positions of an off-target peptide. Second, packing of the side- chains is performed in each monomers to remove internal clashes that are not related to inter- molecular interactions. In the second approach, a coarse-grained model is created using AlphaFold. In the second approach, peptide-MHC model prediction using AlphaFold with default parameters is used to generate each MHC-off-target model. The coarse MHC-off-target models (and optionally a coarse MHC-target model) are then refined using Rosetta. Peptide backbone and side chains are optimized for each model. In the illustrated embodiment, from 10,000 models generated per off-target peptide (and optionally target peptide), 5 lowest Rosetta energy models are selected. These steps correspond to the model refinement module 412 and the model selection module 413 of FIG. 7. Finally, the selected 5 lowest Rosetta energy models are utilized as comparison MHC- off-target peptides and are compared for structural similarity to the MCH-target model. In the illustrated embodiment, the comparison includes computation of RMSD between the MHC-target model and MHC-off-target models; computation of median RMSD between; computation of median RMSD of the TCR/Ab interaction residues between the MHC-target model and MHC-off- target models; computation of correlation of the molecular surface interaction fingerprints (MSIFs) between the MHC-target model and MHC-off-target models complex surfaces; and rank the predicted off-target peptides by RMSD and MSIF metrics. The comparison block of the illustrated embodiment corresponds to the similarity engine 420 and the ranking module 431 of FIG. 7. FIG. 9 is a block diagram of a workflow executable by a structure-based off-target ranking engine. Inputs to the computational workflow include two required inputs and one optional input. The first required input is a list of predicted off-target peptide sequences of equal length to the target peptide sequence. In some embodiments, the peptide length is 9 mer, 10 mer, or 11 mer. The source of these predicted off-targets can be the PIGSPRED or any other available methods which select a workable number of potential off-target peptides. The second required
Attorney Docket #: 250298.000961 input is 3D-structure of the target-MHC complex in a suitable format (e.g., pdb format). The structure can be either experimentally determined using methods such as X-ray crystallography, Cryo-EM or computational predicted using tools including Rosetta, and AlphaFold. The optional input is a list of residue positions in the target peptide that are important for TCR/Ab interaction. These residues can be determined using structure analysis of target peptide-MHC-TCR/Ab complex or mutation scanning-based approaches such as X-scan. The computational workflow can be divided into the following three components: (i) structural modeling of the off-target peptide-MHC complex and identifying lowest energy models, (ii) computing Root Mean Square Deviation (RMSD) and Molecular Surface Interaction Fingerprint (MSIF) metrices to compare 3D conformations of the off targets in MHC groove, and (iii) output metrics for ranking the potential off-target peptides. For the first component, the modeling of an off-target peptide-MHC complex is executed in two steps: 1. Creating a coarse-grained model of the peptide-MHC complex, and 2. Refining the docking of peptide in the MHC groove of the coarse-grained model to generate multiple energy minimized stable models. The coarse-grained model is generated using Rosetta if an experimental structure of the target peptide-MHC complex is available that can be used as a template. In this Rosetta approach, peptide sequence of the template structure is substituted by the off-target sequence (a.k.a. threading technique) followed by packing of the sidechains of off-target peptide and MHC monomers to remove internal clashing. Alternatively, without experimental target peptide-MHC template structure, AlphaFold2 (AF2) with default parameters generates the coarse-grained model. The coarse-grained model is then refined using Rosetta-FlexPepDock protocol to optimize peptide-MHC backbone and side chains. In this protocol, Rosetta performs Monte-Carlo sampling with minimization approach of the backbone and on-the-fly side-chain optimization there by generating multiple models of an off-target peptide-MHC complex. Currently, the protocol has been parameterized to generate 10000 models per off-target peptide-MHC. For each of these models, the protocol computes a series of metrics that helps to identify the lowest energy models. As recommended in the FlexPepDock protocol, the reweighted score metric is used in the workflow to select lowest energy stable models for downstream analyses. Reweighted score is defined as a linear sum of total score (rosetta energy score of the complex), interface score (energy of the pair-wise interactions across the peptide-MHC interface), and peptide score (sum of the
Attorney Docket #: 250298.000961 rosetta energy function over the peptide residues). The lower the reweighted score the more stable is the predicted peptide-MHC model. We selected five lowest re-weighted score models per off- target peptide-MHC complex for the 3D-comformation comparison purpose (described below). For the second component, RMSD and MSIF metrices are computed to compare 3D conformations of the off targets in MHC groove. RMSD is a quantitative metric to measure the average distance between atoms of superimposed protein structures. A lower RMSD value indicates high similarity in the 3D-conformations between two structures. In this workflow, RMSD metric is used to quantify three types of conformational similarities as described below: 1. Similarity of the overall peptide-MHC complex structure that includes only the peptide and the binding groove region (e.g., the α1 and α2 domains of an MHC class I molecule or the α1 and β1 domains of an MHC class II molecule): For this computation, align and super functions from Pymol library, with cycles=0 parameter, is used to measure the RMSD between the target-MHC structure and off-target-MHC model. 2. Similarity of the peptide conformation in the MHC groove: For this metric, the align and super functions from Pymol library, with cycles=0 parameter, is used to superimpose the target and off-target peptide in the model and measure RMSD. 3. Similarity of the conformation of the peptide residue positions important for TCR/Ab interaction: To compute this metric, first, the target and off-target peptide in the model are superimpose using the align and super functions from Pymol library, with cycles=0 parameter, and then rms_cur function from Pymol library is used to measure the RMSD of the residue positions important for TCR/Ab interaction. Additionally, the fourth metric for conformation comparison is a molecular surface interaction fingerprint (MSIF). A recent publication described a method to compute interaction fingerprints, or interaction scores (indicative of protein-protein interaction probabilities), that decipher patterns in protein surfaces important for biomolecular interaction. Within this workflow, the method is utilized to compute MSIFs on the peptide-MHC molecular surface, concentrating specifically on the peptide and the binding groove region (e.g., the α1 and α2 domains of an MHC class I molecule). These MSIFs are represented as vectors indicating interaction scores across different patches on the peptide-MHC molecular surface. The MSIFs in this workflow are generated through a two-step process: firstly, individual fingerprint vectors are calculated for both target-MHC and off-target-MHC complexes; secondly, a correlation is established between the
Attorney Docket #: 250298.000961 interaction probabilities of 3D aligned surface regions between target-MHC and off-target- peptide-MHC complexes. The Pyoints package is used to perform the 3D alignment of surfaces between target-MHC and off-target-MHC complexes. A high correlation value implies that the surface of the off-target-MHC complex exhibits a similar MSIFs to that of the target-MHC complex surface. All the above described metrices are computed for the selected five lowest re-weighted score models per off-target peptide-MHC complex and finally the median value for the metrics is reported for an off-target peptide-MHC complex. For the third component, output of the computational workflow, the method outputs four metrics per off-target peptide. These include median RMSD of the entire MHC and off-target peptide complex, median RMSD of the off-target peptide only, median RMSD of the residues positions important for TCR/Ab interaction, and median molecular surface interaction fingerprint (MSIF) correlation between the target and off-target peptide-MHC molecular surfaces. The computation workflow is validated using publicly available data as described in greater detail in the Examples section herein. Two public data sources have been used to validate the accuracy of the computational workflow. In the first Example, potential off-target peptides for a TCR that targets MAGEA-3168-176 (EVDPIGHLY (SEQ ID NO: 29)) peptide – HLA-A01 complex are ranked. A set of 231 PIGSPRED predicted off targets were analyzed using the workflow. A computationally predicted model of EVDPDTILK (SEQ ID NO: 205) target peptide – HLA-A01 complex was used as the second input to the workflow. The optional input was not supplied in the first example. The aim here was to check if the workflow can prioritize the known Titan off-target peptide as a top ranked candidate. In the second Example, potential off-targets for a bi-specific antibody that targets WT1126- 134 RMFPNAPYL (SEQ ID NO: 241) HLA-A02:01 complex. A set of 142 PIGSPRED and known off-targets were analyzed using the workflow. An experimental X-ray structure of targets WT1126-134 RMFPNAPYL (SEQ ID NO: 241) HLA-A02:01 complex (PDBID: 3HPJ) was used as the second input. Two runs of the workflow were done, one without the optional input and other with the optional input of peptide residues position 1,2,3, and 4 that were known to be important for the antibody interaction.
Attorney Docket #: 250298.000961 FIG. 10 illustrates a block diagram of an exemplary embodiment of a target toxicity database 600 including MHC-target peptide complexes, a list of their respective associated off- target peptides, and an associated risk metric. In some embodiments, the MHC-target peptide complexes included in the database 600 are ranked according to method 200 in FIG. 5. In some embodiments, the off-target list can be determined according to method 140 illustrated in FIG. 4. In some embodiments, the risk metrics can include one or more structural similarity metrics for each off-target peptide calculated according to method 130 in FIG. 3. In some embodiments, a risk metric can be calculated for each off-target peptide based on a weighted sum or other suitable function of the structural similarity metrics for the given off-target peptide. FIG. 11 illustrates a block diagram of an embodiment of a computing device 700. As shown, computing device 700 may include one or more processor(s) 710, an I/O device 720, a memory 730 containing an operating system (“OS”) 740, a database 750, and a program 760. In some embodiments, instructions are stored in the memory 730 that are executable by the processor 710 to perform steps of the computational methods disclosed herein. The computing device can include one or more modules or engines for carrying out computational methods disclosed herein. In some embodiments, the I/O device 720 is configured to communicate with the respective ancillary features such as databased or software computational tools to carry out the functions and computational steps disclosed herein. Computing device 700 may be a single server or may be configured as a distributed computer system including multiple servers or computers that interoperate to perform one or more of the processes and functionalities associated with the disclosed embodiments. In some embodiments, computing device 700 may further include a peripheral interface, a transceiver, a mobile network interface in communication with processor 710, a bus configured to facilitate communication between the various components of computing device 700, and a power source configured to power one or more components of computing device 700. A peripheral interface may include the hardware, firmware and/or software that enables communication with various peripheral devices, such as media drives (e.g., magnetic disk, solid state, or optical disk drives), other processing devices, or any other input source used in connection with the instant techniques. In some embodiments, a peripheral interface may include a serial port, a parallel port, a general- purpose input and output (GPIO) port, a game port, a universal serial bus (USB), a micro-USB
Attorney Docket #: 250298.000961 port, a high definition multimedia (HDMI) port, a video port, an audio port, a BluetoothTM port, an NFC port, another like communication interface, or any combination thereof. In some embodiments, a transceiver may be configured to communicate with compatible devices and ID tags when they are within a predetermined range. A transceiver may be compatible with one or more of: RFID, NFC, BluetoothTM, low-energy Bluetooth™ (BLE), WiFi™, ZigBee™, ABC protocols or similar technologies. A mobile network interface may provide access to a cellular network, the Internet, or another wide-area network. In some embodiments, a mobile network interface may include hardware, firmware, and/or software that allows processor 710 to communicate with other devices via wired or wireless networks, whether local or wide area, private or public, as known in the art. A power source may be configured to provide an appropriate alternating current (AC) or direct current (DC) to power components. Processor 710 may include one or more of a microprocessor, microcontroller, digital signal processor, co-processor or the like or combinations thereof capable of executing stored instructions and operating upon stored data. Memory 730 may include, in some implementations, one or more suitable types of memory (e.g., volatile or non-volatile memory, random access memory (RAM), read only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), magnetic disks, optical disks, floppy disks, hard disks, removable cartridges, flash memory, a redundant array of independent disks (RAID), and the like) for storing files, including an operating system, application programs (including, e.g., a web browser application, a widget or gadget engine, or other applications, as necessary), executable instructions, and data. In one embodiment, the processing techniques described herein are implemented as a combination of executable instructions and data within memory 730. Processor 710 may be one or more known processing devices, such as a microprocessor from the PentiumTM family manufactured by IntelTM or the TurionTM family manufactured by AMDTM. Processor 710 may constitute a single core or multiple core processor that executes parallel processes simultaneously. For example, processor 710 may be a single core processor that is configured with virtual processing technologies. In certain embodiments, processor 710 may use logical processors to simultaneously execute and control multiple processes. Processor 710 may implement virtual machine technologies, or other similar known technologies to provide the ability
Attorney Docket #: 250298.000961 to execute, control, run, manipulate, store, etc. multiple software processes, applications, programs, etc. Other types of processor arrangements could be implemented that provide for the capabilities disclosed herein as understood by a person skilled in the pertinent art. Computing device 700 may include one or more storage devices configured to store information used by processor 710 (or other components) to perform certain functions related to the disclosed embodiments. In one example, computing device 700 may include memory 730 that includes instructions to enable processor 710 to execute one or more applications, such as server applications, network communication processes, and any other type of application or software known to be available on computer systems. Alternatively, the instructions, application programs, etc., may be stored in an external storage or available from a memory over a network. The one or more storage devices may be a volatile or non-volatile, magnetic, semiconductor, tape, optical, removable, non-removable, or other type of storage device or tangible computer-readable medium. In one embodiment, computing device 700 may include memory 730 that includes instructions that, when executed by processor 710, perform one or more processes consistent with the functionalities disclosed herein. Methods, systems, and articles of manufacture consistent with disclosed embodiments are not limited to separate programs or computers configured to perform dedicated tasks. For example, computing device 700 may include memory 730 that may include one or more programs 760 to perform one or more functions of the disclosed embodiments. Moreover, processor 710 may execute one or more programs 760 located remotely from computing device 700. For example, computing device 700 may access one or more remote programs 760, that, when executed, perform functions related to disclosed embodiments. Memory 730 may include one or more memory devices that store data and instructions used to perform one or more features of the disclosed embodiments. Memory 730 may also include any combination of one or more databases controlled by memory controller devices (e.g., server(s), etc.) or software, such as document management systems, MicrosoftTM SQL databases, SharePointTM databases, OracleTM databases, SybaseTM databases, or other relational databases. Memory 730 may include software components that, when executed by processor 710, perform one or more processes consistent with the disclosed embodiments. In some embodiments, memory 730 may include database 750 for storing related data to enable computing device 700 to perform one or more of the processes and functionalities associated with the disclosed embodiments.
Attorney Docket #: 250298.000961 Computing device 700 may also be communicatively connected to one or more memory devices (e.g., databases (not shown)) locally or through a network. The remote memory devices may be configured to store information and may be accessed and/or managed by computing device 700. By way of example, the remote memory devices may be document management systems, MicrosoftTM SQL database, SharePointTM databases, OracleTM databases, SybaseTM databases, or other relational databases. Systems and methods consistent with disclosed embodiments, however, are not limited to separate databases or even to the use of a database. Computing device 700 may also include one or more I/O devices 720 that may include one or more interfaces for receiving signals or input from devices and providing signals or output to one or more devices that allow data to be received and/or transmitted by computing device 700. For example, computing device 700 may include interface components, which may provide interfaces to one or more input devices, such as one or more keyboards, mouse devices, touch screens, track pads, trackballs, scroll wheels, digital cameras, microphones, sensors, and the like, that enable computing device 700 to receive data from one or more users (such as via user device 130). In example embodiments of the disclosed technology, computing device 700 may include any number of hardware and/or software applications that are executed to facilitate any of the operations. The one or more I/O interfaces may be utilized to receive or collect data and/or user instructions from a wide variety of input devices. Received data may be processed by one or more computer processors as desired in various implementations of the disclosed technology and/or stored in one or more memory devices. While computing device 700 has been described as one form for implementing the techniques described herein, other, functionally equivalent techniques may be employed as understood by a person skilled in the pertinent art. For example, as known in the art, some or all of the functionality implemented via executable instructions may also be implemented using firmware and/or hardware devices such as application specific integrated circuits (ASICs), programmable logic arrays, state machines, etc. Furthermore, other implementations may include a greater or lesser number of components than those illustrated. FIG. 12 illustrates a block diagram of an embodiment of a computing network 800 including computing device(s) 810, server(s) 820, memory store(s) 840, and a network 830 facilitating communication between each. In some embodiments, the network 800 is configured
Attorney Docket #: 250298.000961 to execute steps of computational methods as disclosed herein. In some embodiments, one or more computing device(s) 810 include various engines and/or modules as disclosed elsewhere herein, which may be distributed across the computing device(s). The computing device(s) are configured to communicate with ancillary databases and/or software services to carry out steps of computational methods disclosed herein. Network 830 may be of any suitable type, including individual connections via the internet such as cellular or WiFiTM networks. In some embodiments, network 830 may connect terminals, services, and mobile devices using direct connections such as radio-frequency identification (RFID), near-field communication (NFC), BluetoothTM, low-energy BluetoothTM (BLE), WiFiTM, ZigBeeTM, ambient backscatter communications (ABC) protocols, USB, WAN, or LAN. Because the information transmitted may be personal or confidential, security concerns may dictate one or more of these types of connections be encrypted or otherwise secured. In some embodiments, however, the information being transmitted may be less personal, and therefore the network connections may be selected for convenience over security. FIG. 13 illustrates cellular functions related to the example embodiments presented herein. A cell 910 includes MHC-peptide complexes 920, 930 presented on a surface 912 of the cell 910. The cell 910 includes a class I MHC-peptide complex 920 and a class II MHC-peptide complex 930. Each MHC-peptide complex 920, 930 includes a respective MHC molecule 922, 932 and a respective peptide 924, 934. Each peptide 924, 934 includes amino acids that are bound to the respective MHC molecule 922, 932 (illustrated as shaded shapes) and amino acids that are unbound to the respective MHC molecule 922, 932 (illustrated as white shapes). An antigen- recognition molecule 940 includes a receptor 942 that can bind to amino acids of a peptide in an MHC-target peptide complex that are unbound to the MHC molecule. The antigen-recognition molecule 940 may also bind to off-target peptides which have a similar configuration of amino acids (compared to the MHC-target peptide complex) that are unbound to the respective MHC molecule in an MHC-peptide complex. EXAMPLES Example 1: Off-target prediction for the MAGEA3168-176 (EVDPIGHLY (SEQ ID NO: 29)) – HLA-A*01:01 complex.
Attorney Docket #: 250298.000961 Peptide ESDPIVAQY (SEQ ID NO: 47) is a known (experimentally validated) off-target peptide for the MAGEA3168-176 (EVDPIGHLY (SEQ ID NO: 29)) – HLA-A*01:01 complex. This example demonstrates the efficacy of methods and systems disclosed herein for: (i) generating a 3D computational model of the MAGEA3168-176 (EVDPIGHLY (SEQ ID NO: 29)) – HLA-A*01:01 complex as an MHC-target model, (ii) generating 3D computational models of potential off-target peptides, including the ESDPIVAQY (SEQ ID NO: 47) peptide, in the groove of the HLA- A*01:01 molecule for comparison the MHC-target model, and (iii) prioritizing the ESDPIVAQY (SEQ ID NO: 47) peptide among the potential off-target peptides (identified by the PIGSPRED method) based on structural similarity metrics. FIG.14 is a block diagram of an embodiment of a system 900 using an example structure- based off-target prediction engine applied as a proof of concept to a MAGEA3168-176 (EVDPIGHLY (SEQ ID NO: 29)) – HLA-A*01:01 complex. The structure-based off-target ranking engine receives two inputs: an MHC-target model and a list of peptide sequences for potential off-target peptides. The MHC-target model can be obtained through methods disclosed elsewhere herein, for instance as described in relation to block 110 of FIG.1. In the illustrated example, the MAGEA3168- 176 (EVDPIGHLY (SEQ ID NO: 29)) – HLA-A*01:01 complex was not experimentally available. An MHC-target model of the MAGEA3168-176 (EVDPIGHLY (SEQ ID NO: 29)) – HLA-A*01:01 complex was computationally generated using Rosetta and provided as an input (input_2) to the structure-based off-target ranking engine. As described in relation to methods generally herein, the list of off-target peptide amino acid sequences can include a large pool of peptides or can be pre-filtered to arrive at a practical number of potential off-target peptides which can be evaluated by the structure-based off-target ranking engine in a reasonable time frame. In the illustrated example, the list of peptide sequences for potential off-target peptides (input_1) is determined by implementing the PIGSPRED method on the MAGEA3168-176 (EVDPIGHLY (SEQ ID NO: 29)) – HLA-A*01:01 complex and selecting the peptides of the PIGSPRED method having a DoS score of three or more which were present in an internal immunopeptidomics database of MHC-bound peptides identified by mass spectrometry in tissue samples. As a result, the potential off-target peptides evaluated included a list of 231 peptides having the same length as the target peptide, MAGEA3168-176 (EVDPIGHLY
Attorney Docket #: 250298.000961 (SEQ ID NO: 29)). PIGSPRED is disclosed in greater detail elsewhere herein and in WO2023122621A2, which is herein incorporated herein by reference in its entirety. The filtered list of potential off-target peptides includes peptides with various degrees of similarity (DoS) to the target peptide: 2 of these off-targets were DoS of 7, 2 were DoS of 6, 36 were DoS of 5, 169 were DoS of 4 and 22 were DoS of 3. This predicted set includes the known off-target peptide ESDPIVAQY (SEQ ID NO: 47) from Titan protein (“TTN peptide”) with DoS=5 which was ranked 18, based on DoS and gene expression values, by the PIGSPRED method. Improvement in the Titan peptide ranking based on the computed molecular mimicry mechanism provides a validation to the structural modeling based computational workflow. Table 2 lists the top 20 off-target peptides from the list of 231 potential off-target peptides as determined by the PIGSPRED method (using a threshold of at least three identical residues to the target peptide at positions predicted to be available for binding to an antigen-recognition molecule). The DoS score is an indication of a degree of similarity in overall amino acid sequence of the potential off-target peptides with respect to the target peptide. The predicted binding affinity of each off-target is represented by the half maximal inhibitory concentration (IC50) value and a binding affinity percentile rank value (%Rank_BA in NetMHCpan). The mRNA level (TMP from GTEx) in the normal tissue with the highest expression for each off-target gene is provided, as well as the top three highly expressing normal tissues. Peptides found in mice may be useful for conducting antigen-recognition molecule binding studies (e.g., screenings) in mice. Table 2 also indicates which peptides were present in the internal immunopeptidomics database of MHC-bound peptides identified by mass spectrometry in tissue samples. The potential off-target peptides are listed in ranked order as determined primarily by the DoS score, from highest DoS to lowest DoS, and secondarily by gene expression values (TPM) in normal tissue. Table 2: Top 20 off-targets for MAGEA3168-176 (EVDPIGHLY (SEQ ID NO: 29)) A01 complex as predicted by PIGSPRED No Off.target DoS ic50 rank Off target TPM Normal tissue In Mass c
Attorney Docket #: 250298.000961 EVVRIGHLY 7 4098.4 1.3 MAGEA12 0.6 Brain No Yes (SEQ ID NO:
Attorney Docket #: 250298.000961 14 DVNGIRHLY 5 2593.9 0.9 MMP9 1187.8 Blood, Spleen, No Yes (SEQ ID NO: Lung
The structure-based off-target engine generates one or more MHC-off-target models per potential off-target peptide. The MHC-off-target models can be generated as disclosed elsewhere herein, for instance as described in relation to block 120 of FIG. 1 and/or method 120 in FIG. 2. In the illustrated example, the MHC-off-target models were generated by first generating a coarse- grained MHC-off-target model (coarse-grained model) using AlphaFold2 (AF2) with default parameters, and then generating five refined MHC-off-target models using Rosetta-FlexPepDock protocol to optimize peptide-MHC backbone and side chains. In this protocol, Rosetta performs Monte-Carlo sampling with minimization approach of the backbone and on-the-fly side-chain optimization thereby generating multiple models of an off-target peptide-MHC complex. In the illustrated example, the protocol has been parameterized to generate 10,000 refined MHC-off- target models per off-target peptide-MHC. For each of these refined MHC-off-target models, the protocol computes a series of metrics that helps to identify the lowest energy models for a given
Attorney Docket #: 250298.000961 off-target peptide. The reweighted score metric of the FlexPepDock protocol is used in the workflow to select lowest energy, stable models for downstream analyses. Reweighted score is defined as a linear sum of total score (Rosetta energy score of the complex), interface score (energy of the pair-wise interactions across the peptide-MHC interface), and peptide score (sum of the Rosetta energy function over the peptide residues). The lower the reweighted score the more stable is the refined MHC-off-target model. The five lowest re-weighted score models per off-target peptide were then selected for 3D-conformation comparison. The structure-based off-target engine computes one or more structural similarity metrics for each off-target peptide. The structural similarity metrics can be calculated as described elsewhere herein, for instance, according to method 130 illustrated in FIG. 3. In the illustrated example, RMSD between the MHC-target model and MHC-off-target models were computed. The structure-based off-target engine ranks the off-targets according to the RMSD values. The potential off-target peptides can be ranked as described elsewhere herein, for instance, according to method 140 illustrated in FIG. 4. In the illustrated example, the potential off-target peptides are ranked according to the RMSD between MHC-off-target models and the MHC-target model. FIG. 15 is a block diagram of another embodiment of the structure-based off-target prediction engine applied as a proof of concept to the MAGEA3168-176 (EVDPIGHLY (SEQ ID NO: 29)) – HLA-A*01:01 complex. The embodiment illustrated in FIG. 15 is similar to FIG. 14, with one difference being that a molecular surface interaction fingerprint (MSIF) is calculated as a structural similarity metric for each off-target peptide. FIG. 15 also depicts a superposition of the MHC-target model of the MAGEA3168-176 (EVDPIGHLY (SEQ ID NO: 29)) – HLA-A*01:01 complex (computationally generated) in comparison to an available crystal structure of the MAGEA3168-176 (EVDPIGHLY (SEQ ID NO: 29)) HLA-A01 in complex with TCR (PDBID: 5BRZ). The illustrated superposition illustrates that the predicted conformation of the target peptide in the groove (MHC_target model) is close to an experimental structure as indicated by a low peptide RMSD of 0.63 Angstroms. The low target peptide RMSD of 0.63 indicates that the modeled structure is of a reasonable quality. As the crystal structure is trimeric complex of the target MAGEA3168-176 (EVDPIGHLY (SEQ ID NO: 29)) peptide, HLA-A01 allele and TCR, it also reflects that TCR binding did not induce much conformational change to the target peptide.
Attorney Docket #: 250298.000961 FIGs. 16A through 16E include a chart in which the selected 231 potential off-target peptides of the MAGEA3168-176 (EVDPIGHLY (SEQ ID NO: 29)) – HLA-A*01:01 complex ranked according to RMSD of the respective off-target peptide backbone conformation relative to the target peptide backbone conformation as determined by the exemplary methods illustrated in FIGs. 14 and 15. The off-target peptides are sorted by median RMSD value for the five refined lowest energy MHC-off-target models (y-axis). A lower RMSD value indicates a higher degree of structural similarity. The off-target peptides listed along the x-axis are color coded by DoS to the target peptide. FIG.16A includes the top 50 off-target peptides, i.e., highest priority, highest ranked off- target peptides. FIG. 16B includes the 51st through 100th ranked off-target peptides. FIG. 16C includes the 101st through 150th ranked off-target peptides. FIG. 16D includes the 151st through 200th ranked off-target peptides. FIG. 16E includes the 201st through 231st ranked off-target peptides. Table 3 Data from top 30 ranked potential off-target peptides ranked according to RMSD of conformation of the respective off-target peptide to the target peptide. N Off target Do ic50 ran Off target TPM Normal In Mas *RMS o S k ene tissue mous s D d
Attorney Docket #: 250298.000961 AVDPAILLI 4 1429. 0.6 TSPAN33 165.1 Kidney, Yes Yes 0.19 (SEQ ID 4 Pituitary,
Attorney Docket #: 250298.000961 Blood Vessel
Attorney Docket #: 250298.000961 25 FVDSITELL 4 593.5 0.3 EXOC6B 46.8 Skin,Heart, Yes Yes 0.44 (SEQ ID Brain
Of particular importance, is that the TTN peptide ESDPIVAQY (SEQ ID NO: 47), which is a known off-target peptide of the MAGEA3168-176 (EVDPIGHLY (SEQ ID NO: 29)) – HLA- A*01:01 complex, is highly ranked, at #7 (FIG. 16A). The median value of the peptide RMSD metric for the TTN peptide measures 0.20 Angstroms. Notably, the TTN peptide has a DoS of 5, there are four potential off-target peptides which have a greater DoS of 6 or 7, and there are 36 peptides having the same DoS of 5. The data in FIGs. 16-16E therefore shows that the structure- based off-target prediction engine is able to prioritize the known off-target peptide TTN peptide ESDPIVAQY (SEQ ID NO: 47) of the MAGEA3168-176 (EVDPIGHLY (SEQ ID NO: 29)) – HLA- A*01:01 complex by ranking potential off-target peptides based on a structural similarity metric based on a RMSD of conformation of off-target peptides in computationally generated MHC-off- target models relative to an MHC-target model.
Attorney Docket #: 250298.000961 FIGs. 17A through 17E include a chart in which the selected 231 potential off-target peptides of the MAGEA3168-176 (EVDPIGHLY (SEQ ID NO: 29)) – HLA-A*01:01 complex are ranked according to median RMSD value calculated for the overall peptide-MHC complex structure that includes only the peptide backbone and the binding groove region (e.g., the α1 and α2 domains of an MHC class I molecule or the α1 and β1 domains of an MHC class II molecule) as determined by the exemplary methods illustrated in FIGs. 14 and 15. The off-target peptides are sorted by median RMSD value for the five refined lowest energy MHC-off-target models. A lower RMSD value indicates a higher degree of structural similarity. Notably a difference with respect to the data generated in FIGs.16A-16E is that the data represented in FIGs.17A-17E takes into account a portion of the HLA groove in the RMSD calculations, whereas FIGs. 16A-16E do not, and only consider peptide backbone conformation comparison. FIG.17A includes the top 50 off-target peptides, i.e., highest priority, highest ranked off- target peptides. FIG. 17B includes the 51st through 100th ranked off-target peptides. FIG. 17C includes the 101st through 150th ranked off-target peptides. FIG. 17D includes the 151st through 200th ranked off-target peptides. FIG. 17E includes the 201st through 231st ranked off-target peptides. Y-axis shows the median peptide RMSD value. The off-target peptides are color coded by DoS to the target peptide. Table 4 Data from top 5 ranked potential off-target peptides according to median RMSD value of the overall peptide-MHC complex structure that includes only the peptide backbone and the binding groove region (the α1 and α2 domain of the MHC class I molecule). No Off.target Do ic50 ra Off.target. TP Normal.tissue.sam In.mo MassS Medi S 3
Attorney Docket #: 250298.000961 3 EAQPIV 4 574 1.7 HNRNPUL 109 Brain, Blood Yes Yes 0.57 TKY 1.2 2 .4 Vessel, Bladder 7 7
par cu ar mpor ance, s a e pep e Q ( Q : ), w ich is a known off-target peptide of the MAGEA3168-176 (EVDPIGHLY (SEQ ID NO: 29)) – HLA- A*01:01 complex, is the highest ranked among all of the selected 231 potential off-target peptides (FIG.17A). The median RMSD measured for the Titan peptide is 0.50. The data in FIGs.17-17E therefore shows that the structure-based off-target prediction engine is able to prioritize the known off-target peptide TTN peptide ESDPIVAQY (SEQ ID NO: 47) of the MAGEA3168-176 (EVDPIGHLY (SEQ ID NO: 29)) – HLA-A*01:01 complex by ranking potential off-target peptides based on a structural similarity metric based on a RMSD of conformation of off-target peptides and HLA groove in computationally generated MHC-off-target models relative to an MHC-target model. FIGs. 18A through 18E include a chart in which the selected 231 potential off-target peptides of the MAGEA3168-176 (EVDPIGHLY (SEQ ID NO: 29)) – HLA-A*01:01 complex were ranked according to similarity of molecular surface interaction fingerprints (MSIFs) of the respective off-target peptide and HLA groove to the target HLA-peptide complex as determined by the exemplary method illustrated in FIG.15. To arrive at the data illustrated in FIGs.18A-18E, first MaSIF was used to compute an interaction fingerprint for the MHC-target model and each of the plurality of comparison MHC-off-target models (i.e., each of the five refined MHC-off-target models for each of the 231 potential off-target peptides). The molecular surface interaction fingerprint (MSIF) was computed based on the peptide-MHC molecular surface that includes only the peptide and the binding groove region (the α1 and α2 domain of MHC class I molecule). Each
Attorney Docket #: 250298.000961 fingerprint is represented as a vector of interaction probabilities of the patches on the peptide- MHC molecular surface. In the illustrated example, the MaSIF tool is used to compute interaction fingerprints for the MHC-target model as well as for each of the plurality of comparison MHC- off-target models (see, e.g., the “MaSIF-site” application described in Gainza et al, supra). Next, the molecular surfaces of a target peptide-MHC model is computationally aligned with each off- target peptide-MHC model using Pyoints so that a molecular surface interaction fingerprint (MSIF) may be generated for each and a correlation value of the interaction probabilities of the patches is computed. The plotted values are ranked based on the median correlation value (Pearson Correlation Coefficient, PCC) calculated for the five refined MHC-off-target models for each off- target. The TTN peptide ESDPIVAQY (SEQ ID NO: 47) has a value of approximately 0.967. A high correlation value indicates that the off-target-MHC complex surface has a similar molecular surface interaction fingerprint (MSIF) to that of the target-MHC complex surface. FIG.18A includes the top 50 off-target peptides, i.e., highest priority, highest ranked off- target peptides. FIG. 18B includes the 51st through 100th ranked off-target peptides. FIG. 18C includes the 101st through 150th ranked off-target peptides. FIG. 18D includes the 151st through 200th ranked off-target peptides. FIG. 18E includes the 201st through 231st ranked off-target peptides. FIG. 18F includes a table showing data from the top 9 off-target peptides ranked according to similarity of molecular surface interaction fingerprints (MSIFs) of the respective off- target peptide and HLA groove to the target HLA-peptide complex. Of particular importance, is that the TTN peptide ESDPIVAQY (SEQ ID NO: 47), which is a known off-target peptide of the MAGEA3168-176 (EVDPIGHLY (SEQ ID NO: 29)) – HLA- A*01:01 complex, is highly ranked (#9) among the 231 potential off-target peptides (FIG. 18A). The data in FIGs. 18-18E therefore shows that the structure-based off-target prediction engine is able to prioritize the known off-target peptide TTN peptide ESDPIVAQY (SEQ ID NO: 47) of the MAGEA3168-176 (EVDPIGHLY (SEQ ID NO: 29)) – HLA-A*01:01 complex by ranking potential off-target peptides based on a structural similarity metric based on a degree of correlation of molecular surface interaction fingerprints (MSIFs) between MHC-off-target models and the MHC- target model. FIG. 19A illustrates a superposition of a 3D computational model of the MAGEA3168-176 (EVDPIGHLY (SEQ ID NO: 29)) in a complex with the HLA-A*01:01 MHC molecule and a 3D
Attorney Docket #: 250298.000961 computational model of the TTN off-target peptide ESDPIVAQY (SEQ ID NO: 47) in the groove of the HLA-A*01:01 molecule as generated according to the exemplary methods illustrated in FIGs. 14 and 15. The 3D computational models are generated as disclosed in relation to FIG. 14. The structural similarity of the ESDPIVAQY (SEQ ID NO: 47) MHC-off-target model to the EVDPIGHLY (SEQ ID NO: 29) MHC-target model is visibly apparent. In this example, the similarity between the target complex (MAGEA3168-176 (EVDPIGHLY (SEQ ID NO: 29)) – HLA-A*01:01 complex) and the TTN off-target peptide is quantified by multiple similarity metrics as follows. The DoS indicates similarity in amino acid sequence and the PIGSPRED method identifies potential off-target peptides having high sequence similarity (sequence similarity at least two key positions, in particular positions predicted to be available for antigen-recognition molecule binding). A first structural similarity metric based on peptide conformation has an aggregate value of 0.20 Angstroms. This structural similarity metric was determined by: calculating RMSD of the MHC-target model compared to each of the MHC- off-target models considering only peptide backbone conformation, and selecting the median value of the calculated RMSD values (FIGs. 16A-16E). A second structural similarity metric based on peptide backbone and partial HLA molecule structure has an aggregate value of 0.50 Angstroms. This structural similarity metric was determined by: calculating RMSD of the MHC-target model compared to each of the MHC-off-target models considering the peptide backbone and the binding groove region (the α1 and α2 domain of the MHC class I molecule) and selecting the median value of the calculated RMSD values (FIGs. 17A-17E). Yet another structural similarity metric determined based on similarity of molecular surface interaction fingerprints (MSIFs) has an aggregate value of 0.967. This structural similarity metric was determined by: computing a molecular surface interaction fingerprint (MSIF) for the MHC-target model and each of the MHC- off-target models, calculating a correlation value of the molecular surface interaction fingerprints (MSIFs) for each MHC-off-target model compared to the MHC-target model, and selecting the median value of the correlation values. FIG. 19B illustrates a superposition of a 3D computational model of the TTN off-target peptide ESDPIVAQY (SEQ ID NO: 47) in the groove of the HLA-A*01:01 molecule as generated according to the exemplary methods illustrated in FIGs. 14 and 15 and an experimentally determined 3D computational model of the TTN off-target peptide ESDPIVAQY (SEQ ID NO:
Attorney Docket #: 250298.000961 47) in the groove of the HLA-A*01:01 molecule as in complex with a TCR for the sake of verifying the exemplary methods illustrated in FIGs. 14 and 15. FIG. 20A illustrates a superposition of a 3D computational model of the MAGEA3168-176 (EVDPIGHLY (SEQ ID NO: 29)) in a complex with the HLA-A*01:01 MHC molecule and a 3D computational model of the MRPL43 potential off-target peptide TVDPISSSL (SEQ ID NO: 202) in the groove of the HLA-A*01:01 molecule as generated according to the exemplary methods illustrated in FIGs. 14 and 15. TVDPISSSL (SEQ ID NO: 202) is the top-ranked potential off- target peptide according to structural similarity metric based on RMSD of conformation of the respective off-target peptide backbone to the target peptide backbone as shown in FIG. 16A. FIG. 20B illustrates a superposition of a 3D computational model of the MAGEA3168-176 (EVDPIGHLY (SEQ ID NO: 29)) in a complex with the HLA-A*01:01 MHC molecule and a 3D computational model of the IGHM off-target peptide ESATITCLV (SEQ ID NO: 204) in the groove of the HLA-A*01:01 molecule as generated according to the exemplary methods illustrated in FIGs. 14 and 15. ESATITCLV (SEQ ID NO: 204) is the third top-ranked potential off-target peptide according to structural similarity metric based on RMSD of conformation of the respective off-target peptide to the target peptide as shown in FIG. 16A. Example 2: Off-target prediction for the WT1126- 134 (RMFPNAPYL (SEQ ID NO: 241)) – HLA-A*02:01 complex. Eight known off-target peptides of the target WT1126-134 (RMFPNAPYL (SEQ ID NO: 241)) HLA-A02:01 complex for the ESK1 bispecific antibody were compiled from a recent publication that used liver immunopeptidomics data (Marrer-Berger E, Nicastri A, Augustin A, Kramar V, Liao H, Hanisch LJ, Carpy A, Weinzierl T, Durr E, Schaub N, Nudischer R, Ortiz- Franyuti D, Breous-Nystrom E, Stucki J, Hobi N, Raggi G, Cabon L, Lezan E, Umaña P, Woodhouse I, Bujotzek A, Klein C, Ternette N. The physiological interactome of TCR-like antibody therapeutics in human tissues. Nat Commun. 2024 Apr 16;15(1):3271). This example demonstrates the efficacy of methods and systems disclosed herein for: (i) generating a 3D computational model of the WT1126-134 (RMFPNAPYL (SEQ ID NO: 241)) HLA-A02:01 complex as an MHC-target model (based on comparison to an experimentally available model), (ii) generating 3D computational models of potential off-target peptides in the groove of the HLA- A*01:01 molecule for comparison the MHC-target model, and (iii) prioritizing the known off- target peptides among the potential off-target peptides based on structural similarity metrics.
Attorney Docket #: 250298.000961 FIG. 21 is a block diagram of an embodiment of the structure-based off-target prediction engine applied as a proof of concept to a WT1126- 134 (RMFPNAPYL (SEQ ID NO: 241)) – HLA- A*02:01 complex. The structure-based off-target ranking engine receives two inputs: an MHC-target model and a list of peptide sequences for potential off-target peptides. The structure-based off-target engine is configured similarity to the structure-based off-target engine illustrated in FIGs. 14 and 15. The MHC-target model can be obtained through methods disclosed elsewhere herein, for instance as described in relation to block 110 of FIG. 1. In the illustrated example, the MHC- target model (input_2) was obtained from the Protein Data Bank (PDB), a publicly available protein structure database, which includes a previously solved X-ray crystal structure of the WT1126-134 (RMFPNAPYL (SEQ ID NO: 241)) HLA-A02:01 complex (PDBID: 3HPJ) and a previously solved X-ray crystal structure of the same in complex with the ESK1 antibody (PDBID: 4WUU). Peptide residue positions important for the antibody interaction including N-terminal positions 1, 2, 3, and 4 were identified based on the obtained structure from the PDB database. Availability of these interaction residue positions enabled computing a structural similarity metric based on an RMSD metric type that measures the conformational similarity for these important positions only. In the illustrated example, a list of potential off-target peptides was compiled including all the potential off-target peptides generated via the PIGSPRED method that were found to be expressed in normal liver tissue (using a threshold of at least three identical residues to the target peptide at positions predicted to be available for binding to an antigen recognition molecule) and peptides experimentally evaluated by Marrer-Berger et al., supra, including the eight known off- target peptides of the WT1126-134 (RMFPNAPYL (SEQ ID NO: 241)) HLA-A02:01 complex. A total set of 142 off-target peptides were therefore considered for comparison analysis of the structural similarity with the WT1126-134 RMFPNAPYL (SEQ ID NO: 241) HLA-A02:01 complex. Five lowest energy refined MHC-off-target models were selected for each off-target peptide similar to as described in relation to FIG. 14, resulting in five comparison MHC-off-target models per off-target peptide. FIGs. 22A through 22D include a chart in which the 142 potential off-target peptides of the WT1126- 134 (RMFPNAPYL (SEQ ID NO: 241)) – HLA-A*02:01 complex are ranked according
Attorney Docket #: 250298.000961 to RMSD of conformation of the peptide backbone. The RMSD value for each comparison MHC- off-target model is calculated by comparing to the MHC-target model. The 142 potential off-target peptides are ranked by the median value of the RMSD value for the corresponding MHC-off-target models based on a comparison of the conformation of the entire peptide. X-axis lists the 142 off targets sorted by low to high median RMSD values. Y-axis shows the median peptide RMSD value. The off-target peptides are color coded by DoS to the target peptide. The known off-targets from the publication (Marrer-Berger E, et. al.) are prefixed by the gene name followed by dotted line. FIG.22A includes the top 40 off-target peptides, i.e., highest priority, highest ranked off- target peptides. FIG. 22B includes the 41st through 80th ranked off-target peptides. FIG. 22C includes the 81st through 120th ranked off-target peptides. FIG. 22D includes the 121st through 142nd ranked off-target peptides. Based on the peptide RMSD metric, 5 out of 8 known off-target peptides were ranked in the top 20 set (FIG. 22A). Most interestingly, an off-target peptide with DoS=0 was ranked the highest among the eight known off-targets. Three of the 8 known off-targets that were not presented in the top 20 were distributed evenly rank-wise in the entire 142 off-target set. The data in FIGs. 22A-22D therefore shows that the structure-based off-target prediction engine is able to prioritize known off-target peptides of the WT1126-134 (RMFPNAPYL (SEQ ID NO: 241)) HLA- A02:01 complex having a low DoS by ranking potential off-target peptides based on a structural similarity metric based on RMSD of conformation of the peptide. FIGs. 23A through 23D include a chart in which the 142 potential off-target peptides of the WT1126- 134 (RMFPNAPYL (SEQ ID NO: 241)) – HLA-A*02:01 complex are ranked according to RMSD of conformation of the peptide at positions 1, 2, 3, and 4 only. The RMSD value for each comparison MHC-off-target model is calculated by comparing to the MHC-target model at peptide positions 1, 2, 3, and 4. The 142 potential off-target peptides are ranked by the median value of the RMSD value for the corresponding MHC-off-target models. Y-axis indicates the median RMSD values of the peptide residue positions 1, 2, 3 and 4 that are important for interaction with ESK1 antibody computed for the selected 142 potential off-target peptides of WT1126-134 (RMFPNAPYL (SEQ ID NO: 241)) HLA-A02:01 complex. X-axis lists the 142 potential off-target peptides sorted by low to high median RMSD values. The off-target peptides
Attorney Docket #: 250298.000961 are color coded by degree of similarity to the target peptide. The known off-targets from the publication are prefixed by the gene name followed by dotted line. In this example, the similarity between the target complex (WT1126- 134 (RMFPNAPYL (SEQ ID NO: 241)) – HLA-A*02:01 complex) and eight known off-target peptides quantified by multiple similarity metrics and ranked according to two structural similarity metrics as shown in FIGS.22A-22D and FIGS.23A-23D. The DoS score is derived from the PIGSPRED method and is an indication of similarity in amino acid sequence. A structural similarity metric based on peptide conformation is shown in FIGS. 22A-22D. This structural similarity metric is determined by: calculating RMSD of the MHC-target model compared to each of the MHC-off-target models considering only peptide backbone conformation, and selecting the median value of the calculated RMSD values. Another structural similarity metric is based on peptide conformation only at important residue positions. This structural similarity metric is determined by: determining positions important for antigen-recognition molecule binding, calculating RMSD of the MHC- target model compared to each of the MHC-off-target models considering important residue positions, and selecting the median value of the calculated RMSD values. Table 5 lists the name, sequence, DoS, ranking by the peptide RMSD structural similarity metric value, and ranking by the important residue RMSD structural similarity metric of the eight known off-target peptides. Interestingly, on considering the median RMSD of the residue position important for antibody interaction, 7 out of 8 known off-target peptides were ranked in the top 20 set. This provides evidence that the prediction of the computational workflow may be more accurate if the TCR/Ab interacting residue positions are known. Table 5 Similarity metrics for eight known off-target peptides of the WT1126-134 RMFPNAPYL (SEQ ID NO: 241) HLA-A02:01 complex. Name Sequence DoS Ranking by Ranking by Important
Attorney Docket #: 250298.000961 SF3B478-86 KLYGKPIRV (SEQ 0 4 13 ID NO: 235)
FIG. 24A illustrates a superposition of a 3D computational model of the WT1126-134 (RMFPNAPYL (SEQ ID NO: 241)) target peptide in a complex with the HLA-A*02:01 MHC molecule and a 3D computational model of the SF3B4 off-target peptide KLYGKPIRV (SEQ ID NO: 235) in the groove of the HLA-A*02:01 molecule as generated according to the exemplary method illustrated in FIG. 21. The structural similarity metric based on peptide RMSD is 0.52. FIG. 24B illustrates a superposition of a 3D computational model of the WT1126-134 (RMFPNAPYL (SEQ ID NO: 241)) target peptide in a complex with the HLA-A*02:01 MHC molecule and a 3D computational model of the ILF2 off-target peptide KILPTLEAV (SEQ ID NO: 233) in the groove of the HLA-A*02:01 molecule as generated according to the exemplary method illustrated in FIG. 21. The structural similarity metric based on peptide RMSD is 0.77. FIG. 24C illustrates a superposition of a 3D computational model of the WT1126-134 (RMFPNAPYL (SEQ ID NO: 241)) target peptide in a complex with the HLA-A*02:01 MHC molecule and a 3D computational model of the USP9Y off-target peptide RLWGEPVNL (SEQ ID NO: 240) in the groove of the HLA-A*02:01 molecule as generated according to the exemplary method illustrated in FIG. 21. The structural similarity metric based on peptide RMSD is 0.70. FIG. 24D illustrates a superposition of a 3D computational model of the WT1126-134 (RMFPNAPYL (SEQ ID NO: 241)) target peptide in a complex with the HLA-A*02:01 MHC molecule and a 3D computational model of the SHC1 off-target peptide RVPPPPQSV (SEQ ID NO: 236) in the groove of the HLA-A*02:01 molecule as generated according to the exemplary
Attorney Docket #: 250298.000961 method illustrated in FIG. 21. The structural similarity metric based on peptide RMSD is 1.54; however, when considering only important residue positions, the RMSD is significantly lower. FIG.25A illustrates an X-Ray structure of ESK1 in complex with HLA-A*02:01/WT1126- 134. FIG. 25B illustrates the X-Ray structure of ESK1 in complex with HLA- A*02:01/WT1126-134 of FIG. 25A with a 3D computational model of off-target peptide KLYGKPIRV (SEQ ID NO: 235) in the groove of the HLA -A*02:01 molecule as generated according to the exemplary method illustrated in FIG. 21. The following clauses list non-limiting embodiments of the disclosure: Clause 1: A method for providing one or more off-target 3D computational models of a potential off-target peptide in a groove of an MHC molecule (“comparison MHC-off-target models”) for comparison to a 3D computational model of a target peptide in a complex with the MHC molecule (“MHC-target model”), the method comprising: generating a coarse-grained model of the potential off-target peptide in the groove of the MHC molecule (“coarse-grained MHC-off-target model”) by substituting, in the MHC-target model, an amino acid sequence of the potential off-target peptide in place of an amino acid sequence of the target peptide; generating a plurality of refined computational models of the potential off-target peptide in the groove of the MHC molecule (“refined MHC-off-target models”) by computationally optimizing the coarse- grained MHC-off-target model multiple times such that each optimization of the coarse-grained MHC-off-target model results in a respective refined MHC-off-target model of the plurality of refined MHC-off-target models; and selecting the one or more comparison MHC-off-target models from the plurality of refined MHC-off-target models such that the one or more comparison MHC- off-target models have lower energy than a majority of the plurality of the refined MHC-off-target models. Clause 2: The method of Clause 1, further comprising: selecting a 3D computational model of a template peptide bound to the MHC molecule (“MHC-template model”) from a database of known protein structures based at least in part on amino acid sequence similarity of the template peptide to the target peptide; and generating the MHC-target model based at least in part on the MHC-template model.
Attorney Docket #: 250298.000961 Clause 3: The method of Clause 1, further comprising: generating the MHC-target model based at least in part on an experimentally determined 3D structure of the target peptide in a complex with the MHC molecule. Clause 4: The method of any one of Clauses 1-3, wherein generating the coarse-grained MHC-off-target model comprises optimizing a selection of rotamer combinations for the off-target peptide and MHC molecule to eliminate steric clashes. Clause 5: The method of any one of Clauses 1-4, wherein generating the plurality of refined MHC-off-target models comprises executing computational peptide docking algorithm on the coarse-grained MHC-off-target model. Clause 6: The method of Clause 5, wherein the computational peptide docking algorithm comprises a Monte Carlo search with minimization algorithm. Clause 7: The method of Clause 6, wherein the minimization algorithm is applied to a backbone of the off-target peptide, and wherein peptide docking algorithm comprises on-the-fly side-chain optimization of the off-target peptide. Clause 8: The method of any one of Clauses 5-7, wherein the computational peptide docking algorithm comprises the following steps: a) modifying an energy function used to evaluate an MHC-peptide model by reducing the weight of van der Waals repulsive forces and/or increasing the weight of van der Waals attractive forces, optionally both, to an extent that will permit sampling of alternative conformations of the MHC-peptide model while preventing a peptide of the MHC- peptide model from separating from a binding pocket within the groove of the MHC molecule during a subsequent energy minimization reconfiguration of the MHC-peptide model; b) optimizing a rigid body orientation of the peptide of the MHC-peptide model by applying a random rigid body perturbation, optionally a Gaussian rigid body perturbation, comprising a rotation and/or translation to affect the orientation of the peptide within the MHC-peptide model with respect to the groove, repacking the side chains of rotamers within a peptide-MHC interface of the MHC-peptide model following the random rigid body perturbation, and applying an energy minimization step following the repacking to arrive at a reconfigured rigid body orientation, wherein the repacking comprises optimizing the selection of rotamer combinations for the peptide- MHC interface to eliminate steric clashes, wherein the energy minimization step comprises using a deterministic algorithm to find a local energy minimum, wherein the reconfigured rigid body orientation is accepted only if an energy function criterion, optionally the Metropolis criterion, is
Attorney Docket #: 250298.000961 met, and wherein the applying of the random rigid body perturbation, the repacking, and the energy minimization step are sequentially repeated for a plurality of cycles, optionally a predefined number of cycles and/or until an energy criterion is satisfied; c) optimizing the peptide backbone conformation, following optimization of the rigid body orientation, by applying a random torsion angle perturbation to the peptide backbone, optionally comprising a Rosetta small move or a Rosetta shear move, repacking the side chains of rotamers within the peptide-MHC interface following the random torsion angle perturbation, and applying an energy minimization step following the repacking to arrive at a reconfigured peptide backbone conformation, wherein the repacking comprises optimizing the selection of rotamer combinations for the peptide-MHC interface to eliminate steric clashes, wherein the energy minimization step comprises using a deterministic algorithm to find a local energy minimum, wherein the reconfigured peptide backbone conformation is accepted only if an energy function criterion, optionally the Metropolis criterion, is met, and wherein the applying of the random torsion angle perturbation, the repacking, and the energy minimization step are sequentially repeated for a plurality of cycles, optionally a predefined number of cycles and/or until an energy criterion is satisfied, optionally wherein the random torsion angle perturbation alternates between Rosetta small moves and Rosetta shear moves each cycle; and d) repeating steps (b) and (c) for a plurality of cycles wherein the van der Waals forces are gradually ramped back towards normal values such that the last cycle is performed with the normal values to arrive at a refined MHC-off-target model of the plurality of refined MHC-off-target models. Clause 9: The method of any one of Clauses 5-8, further comprising: repeating the computational peptide docking algorithm a plurality of times, optionally at least 100, 200, 500, 1,000, 2,000, 5,000, or 10,000 times, to produce the plurality of refined MHC-off-target models. Clause 10: The method of any one of Clauses 1-9, wherein generating a plurality of refined computational models comprises generating at least 100, 200, 500, 1,000, 2,000, 5,000, or 10,000 refined MHC-off-target models based at least in part on the coarse-grained MHC-off-target model. Clause 11: The method of any one of Clauses 1-10, wherein the one or more comparison MHC-off-target peptide models consist of a predetermined number, optionally at least 5, of the refined MHC-off-target models having the lowest energy scores as evaluated by an energy function.
Attorney Docket #: 250298.000961 Clause 12: The method of Clause 11, further comprising: calculating, for each of the plurality of refined three-dimensional models, a total energy score of an MHC-peptide complex, an interface score that measures an energy of pairwise interactions across a peptide-MHC interface, a peptide score that measures an energy function over the residues of the peptide, or a combination thereof, optionally a weighted sum. Clause 13: The method of any one of Clauses 1-12, wherein the one or more comparison MHC-off-target peptide models consist of a predetermined number, optionally at least 5, of the refined MHC-off-target models having a higher stability than a majority of the refined MHC-off- target models. Clause 14: A method for quantifying structural similarity between a potential off-target peptide in a groove of an MHC molecule and a target peptide in complex with the MHC molecule for the purposes of antigen-recognition molecule binding, the method comprising: obtaining a plurality of 3D computational models of the potential off-target peptide in the groove of the MHC molecule (“comparison MHC-off-target models”); calculating, for each of the plurality of comparison MHC-off-target models, one or more structural similarity metrics such that each of the one or more structural similarity metrics comprises a corresponding value for each of the plurality of comparison MHC-off-target models and represents a measure of structural similarity between the comparison MHC-off-target model and a 3D computational model of the target peptide in complex with the MHC molecule (“MHC-target model”); and calculating, for each of the one or more structural similarity metrics, a single composite structural similarity metric comprising a value based at least in part on the corresponding structural similarity metric values for at least a portion of the plurality of comparison MHC-off-target models. Clause 15: The method of Clause 14, wherein obtaining the one or more comparison MHC-off-target models comprises providing one or more, optionally all, of the plurality of comparison MHC-off-target models according to any one of Clauses 1-12. Clause 16: The method of Clause 14 or 15, further comprising: obtaining the MHC-target model and/or one or more of the plurality of comparison MHC-off-target models, at least in part, from a predicted structure determined using one or more of the following: a) template-based modeling; b) sequence-based machine learning; and/or c) experimental measurements, optionally wherein the experimental measurements are made using X-ray crystallography or Cryo-electron microscopy (Cryo-EM).
Attorney Docket #: 250298.000961 Clause 17: The method of Clause 16, further comprising: obtaining the MHC-target model and/or at least a portion of the plurality of comparison MHC-off-target models by a template-based modeling process comprising: a) searching a database of known protein structures comprising a template peptide bound to the MHC molecule to identify a plurality of template structures based, at least in part, on amino acid sequence similarity of the template peptide with the peptide sequence of the target peptide or potential off-target peptide; b) aligning the peptide sequence to each of the three-dimensional structures of the template structures based on a comparison of the sequences; c) calculating an energy score for each of the aligned peptide sequences; d) selecting a template structure based on the calculated energy scores; and e) assigning a predicted structure based on the selected template structure. Clause 18: The method of Clause 16 or 17, further comprising: determining the predicted structure(s) using a sequence-based machine learning algorithm, optionally wherein the sequence- based machine learning algorithm uses a neural network-based model. Clause 19: The method of any one of Clauses 14-18, further comprising: determining the MHC-target model and/or at least a portion of the plurality of comparison MHC-off-target models, at least in part, from a prepacked predicted structure, wherein the prepacked predicted structure is optimized based at least in part on the selection of rotamer combinations for the peptide and MHC molecule to eliminate steric clashes, optionally wherein the predicted structure is based at least in part on a templated-based modeling or experimental measurements followed by prepacking. Clause 20: The method of any one of Clauses 14-19, further comprising: determining the MHC-target model and the plurality of comparison MHC-off-target models, at least in part, from predicted structures determined using the same approach, optionally template-based modeling or sequence-based machine learning. Clause 21: The method of any one of Clauses 14-20, further comprising: generating the MHC-target model by refining an initial coarse-grained MHC-target model by a computational peptide docking algorithm, optionally wherein the initial coarse-grained MHC-target model is a predicted structure of any one of Clauses 23-27, optionally wherein the predicted structure is a prepacked predicted structure. Clause 22: The method of any one of Clauses 14-21, generating at least a portion of the plurality of comparison MHC-off-target models by refining a coarse-grained MHC-off-target model by a computational peptide docking algorithm, optionally wherein the coarse-grained
Attorney Docket #: 250298.000961 MHC-off-target model is a predicted structure of any one of Clauses 23-27, optionally wherein the predicted structure is a prepacked predicted structure. Clause 23: The method of Clause 21 or 22, wherein the computational peptide docking algorithm comprises a Monte Carlo search with minimization algorithm. Clause 24: The method of any one of Clauses 21-23, wherein the computational peptide docking algorithm comprises the following steps: a) modifying an energy function used to evaluate an MHC-peptide model by reducing the weight of van der Waals repulsive forces and/or increasing the weight of van der Waals attractive forces, optionally both, to an extent that will permit sampling of alternative conformations of the MHC-peptide model while preventing a peptide of the MHC- peptide model from separating from a binding pocket within the groove of the MHC molecule during a subsequent energy minimization reconfiguration of the MHC-peptide model; b) optimizing a rigid body orientation of the peptide of the MHC-peptide model by applying a random rigid body perturbation, optionally a Gaussian rigid body perturbation, comprising a rotation and/or translation to affect the orientation of the peptide within the MHC-peptide model with respect to the groove, repacking the side chains of rotamers within a peptide-MHC interface of the MHC-peptide model following the random rigid body perturbation, and applying an energy minimization step following the repacking to arrive at a reconfigured rigid body orientation, wherein the repacking comprises optimizing the selection of rotamer combinations for the peptide- MHC interface to eliminate steric clashes, wherein the energy minimization step comprises using a deterministic algorithm to find a local energy minimum, wherein the reconfigured rigid body orientation is accepted only if an energy function criterion, optionally the Metropolis criterion, is met, and wherein the applying of the random rigid body perturbation, the repacking, and the energy minimization step are sequentially repeated for a plurality of cycles, optionally a predefined number of cycles and/or until an energy criterion is satisfied; c) optimizing the peptide backbone conformation, following optimization of the rigid body orientation, by applying a random torsion angle perturbation to the peptide backbone, optionally comprising a Rosetta small move or a Rosetta shear move, repacking the side chains of rotamers within the peptide-MHC interface following the random torsion angle perturbation, and applying an energy minimization step following the repacking to arrive at a reconfigured peptide backbone conformation, wherein the repacking comprises optimizing the selection of rotamer combinations for the peptide-MHC interface to eliminate steric clashes, wherein the energy minimization step comprises using a
Attorney Docket #: 250298.000961 deterministic algorithm to find a local energy minimum, wherein the reconfigured peptide backbone conformation is accepted only if an energy function criterion, optionally the Metropolis criterion, is met, and wherein the applying of the random torsion angle perturbation, the repacking, and the energy minimization step are sequentially repeated for a plurality of cycles, optionally a predefined number of cycles and/or until an energy criterion is satisfied, optionally wherein the random torsion angle perturbation alternates between Rosetta small moves and Rosetta shear moves each cycle; and d) repeating steps (b) and (c) for a plurality of cycles wherein the van der Waals forces are gradually ramped back towards normal values such that the last cycle is performed with the normal values to arrive at a refined MHC-off-target model of the plurality of refined MHC-off-target models. Clause 25: The method of any one of Clauses 21-24, further comprising: repeating the computational peptide docking algorithm a plurality of times, optionally at least 100, 200, 500, 1,000, 2,000, 5,000, or 10,000 times, to produce a plurality of refined MHC-off-target models, wherein the plurality of comparison MHC-off-target models comprises a portion of the refined MHC-off-target models having lower energy than a majority of the plurality of refined MHC-off- target models. Clause 26: The method of any one of Clauses 14-25, wherein the one or more structural similarity metrics comprises a difference value which quantifies distances between atoms of the MHC-target model and corresponding atoms of a respective comparison MHC-off-target model of the plurality of comparison MHC-off-target models, optionally wherein the difference value is based on an average distance such as root mean square deviations (RMSDs). Clause 27: The method of Clause 26, wherein the difference value quantifies distances between heavy atoms only. Clause 28: The method of Clause 26 or 27, wherein the distances comprise the distances between all heavy atoms of the target peptide backbone and all corresponding heavy atoms of each of the potential off-target peptide backbones. Clause 29: The method of Clause 27 or 28, wherein the distances consist of the distances between atoms, optionally all heavy atoms, of the target peptide backbone of the MHC-target model at each position that has been determined to be available for binding by an antigen- recognizing molecule to a peptide-MHC (pMHC) complex comprising the target peptide bound to the MHC molecule and all corresponding atoms of the potential off-target peptide backbones of
Attorney Docket #: 250298.000961 the plurality of comparison MHC-off-target models at each of the corresponding positions, optionally wherein the positions available for binding an antigen-recognizing molecule have been predicted by a mutagenesis screen to be those positions that, based on changes in binding affinity, are not involved in binding the MHC molecule, and optionally wherein the mutagenesis screen is a computational mutagenesis screen and those positions that are not involved in binding the MHC molecule are determined by a machine learning model, such as an artificial neural network, trained on peptide-MHC binding data. Clause 30: The method of Clause 28, wherein the distances consist of the distances between all heavy atoms of the target peptide backbone of the MHC-target model and all corresponding heavy atoms of each of the potential off-target peptide backbones of the plurality of comparison MHC-off-target models. Clause 31: The method of Clause 28, wherein the distances comprise the distances between heavy atoms of at least a portion of the groove of the MHC molecule in the MHC-target model and a corresponding portion of the groove of the MHC molecule in each of the plurality of comparison MHC-off target models, optionally wherein the portion comprises the bed and rails of the groove. Clause 32: The method of Clause 31, wherein the portion consists of the bed and the rails of the groove. Clause 33: The method of any one of Clauses 14-32, wherein the one or more structural similarity metrics comprises a quantification of a correlation (“correlation quantification”) between a molecular surface interaction fingerprint of the MHC-target model and each of the plurality of comparison MHC-off-target models, wherein the molecular surface interaction fingerprint characterizes an interaction probability of a respective model with an antigen-binding molecule based on one or more surface features, wherein the one or more surface features comprise geometric features and/or chemical features of the molecular surface, optionally wherein the molecular surface interaction fingerprint is a vector, optionally wherein the correlation quantification comprises a correlation coefficient such as a Pearson correlation coefficient, and optionally wherein the geometric features comprise shape index or distance-dependent curvature and/or the chemical features comprise hydropathy, continuum electrostatics, or location of free electrons and proton donors.
Attorney Docket #: 250298.000961 Clause 34: The method of Clause 33, wherein the molecular surface interaction fingerprint is a vector of interaction scores, wherein each interaction score is associated with a spatially localized portion of the molecular surface of the respective model and is a scalar value representative of a probability of the respective spatially localized portion being involved in a protein-protein interaction, optionally wherein each spatially localized portion is one of a plurality of overlapping patches that encompass the entire molecular surface of the respective model, further optionally wherein the vector comprises an interaction score for each patch of the plurality of patches. Clause 35: The method of Clause 34, wherein each patch of the plurality of overlapping patches is a geodesic patch having a fixed geodesic distance from a patch center, optionally wherein the patch center is a vertex of the molecular surface of the respective model and optionally wherein each patch has the same fixed geodesic distance. Clause 36: The method of any one of Clauses 33-35, wherein the molecular surface interaction fingerprint is determined at least in part by geometric deep learning, optionally using a convolutional neural network. Clause 37: The method of any one of Clauses 33-36, wherein the molecular surface interaction fingerprint is determined by: a) decomposing a surface of the respective model into a plurality of overlapping geodesic patches; b) mapping the one or more surface features of the respective model to each of the geodesic patches using polar geodesic coordinates, wherein the one or more surface features comprise one or more, optionally all, of the following features: shape index, distance-dependent curvature, hydropathy, continuum electrostatics, and location of free electrons and proton donors; c) applying a convolutional neural network to each patch to produce an interaction score for each patch, wherein applying the convolutional neural network comprises rotating the geodesic patch to provide rotation invariance, and wherein the convolutional neural network has been trained on i) portions of surfaces that form interface regions between pairs of proteins that are known binding partners and ii) portions of surfaces that do not form interface regions between pairs of proteins, optionally wherein the interface regions are solvent inaccessible regions in complexes of the known binding partners and optionally wherein the proportions of surfaces that do not form interface regions are derived from one or both of the known binding partners; and d) determining the molecular surface interaction fingerprint based at least in part on the interaction scores of the plurality of overlapping geodesic patches, wherein determining the
Attorney Docket #: 250298.000961 molecular surface interaction fingerprint comprises identifying spatially corresponding patches between the MHC-target model and each respective MHC off-target model and wherein the molecular surface interaction fingerprint comprises the interaction scores for the corresponding patches, optionally wherein determining the molecular surface interaction fingerprint comprises removing interaction scores that are not associated with a corresponding patch. Clause 38: The method of Clause 37, wherein mapping the surface features to the geodesic patches comprises locally averaging the one or more surface features to learnable Gaussian kernels. Clause 39: The method of any one of Clauses 14-38, wherein the plurality of comparison MHC-off-target models comprises a predetermined number of models, optionally, at least 5. Clause 40: The method of any one of Clauses 14-39, wherein the composite structural similarity metric of each of the one or more structural similarity metrics is based at least in part on an aggregate measure of a respective structural similar metric for each of the plurality of comparison MHC-off-target models. Clause 41: The method of Clause 40, wherein the aggregate measure comprises an average, median, or minimum. Clause 42: The method of Clause 39, wherein the aggregate measure comprises a median of the respective structural similar metric for each of the plurality of comparison MHC-off-target models. Clause 43: The method of any one of Clauses 14-42, wherein the one or more structural similarity metrics comprises at least one of: a first RMSD metric based on similarity of an overall peptide-MHC complex structure that includes only the peptide and the binding groove region of the MHC molecule; a second RMSD metric based on similarity of peptide conformation in the MHC groove; a third RMSD metric based on similarity of conformation of peptide residue positions available for antigen-recognition molecule binding; or a fourth metric based on correlation between computed interaction scores associated with portions of a peptide-MHC molecular surface that comprises protein surfaces important for biomolecular interaction. Clause 44: A method for identifying potential off-target peptide(s) based on sequence similarity and structural similarity for an antigen-recognition molecule that recognizes a target peptide presented in complex with an MHC molecule (MHC-target peptide complex), the method comprising: obtaining a pool of peptides of suitable length, optionally wherein the pool of peptides
Attorney Docket #: 250298.000961 are expressed in normal tissues, optionally essential, normal tissues; identifying, within the pool, high sequence similarity peptides that have (i) more amino acid sequence similarity to the target peptide than a majority of peptides within the pool and (ii) have a binding affinity to the MHC molecule greater than a threshold value; and identifying the potential off-target peptides by selecting within the high sequence similarity peptides, the peptide(s) that are more structurally similar, when positioned in a groove of the MHC molecule, to the MHC-target peptide complex than a majority of the high sequence similarity peptides. Clause 45: The method of Clause 44, wherein identifying, within the high sequence similarity peptides, the high sequence similarity and structurally similar off-target peptide(s) comprises: quantifying structural similarity between each of the high sequence similarity peptides in a groove of the MHC molecule and the target peptide in the groove of the MHC molecule for the purposes of antigen-recognition molecule binding according to the method of any one of Clauses 21-43, wherein each of the high sequence similarity peptides is the potential off-target peptide of Clauses 21-43. Clause 46: The method of Clause 44 or 45, wherein identifying, within the pool, high sequence similarity peptides comprises identifying peptides within the pool based on one or more of the following: a) a degree of similarity between a sequence of a peptide within the pool and a target peptide sequence of the target peptide, optionally wherein a threshold degree of similarity for selection requires one or more residue mismatches and/or requires one or more identical residues; b) a number of identical residues, optionally at least three, at positions within the target peptide sequence that have been determined to be available for binding by an antigen-recognizing molecule to a peptide-MHC (pMHC) complex comprising the target peptide bound to the MHC molecule, optionally wherein the positions available for binding an antigen-recognizing molecule have been predicted by a mutagenesis screen to be those positions that, based on changes in binding affinity, are not involved in binding the MHC molecule, optionally wherein the mutagenesis screen is a computational mutagenesis screen and those positions that are not involved in binding the MHC molecule are determined by a machine learning model, such as an artificial neural network, trained on peptide-MHC binding data; c) binding affinity to the MHC molecule, wherein peptides within the pool that are determined to have a binding affinity below a threshold binding affinity are not selected, optionally wherein binding affinity is determined by a machine learning model, such as an artificial neural network, trained on peptide-MHC binding data; and d) tissue
Attorney Docket #: 250298.000961 expression, wherein peptides within the pool that are not expressed in normal tissue, optionally not in essential normal tissue, are not selected. Clause 47: The method of Clause 46, wherein the proteome is a human proteome and the normal tissue or essential tissue is human tissue. Clause 48: The method of any one of Clauses 44-47, wherein the high sequence similarity peptides include at least two amino acids that (i) are located at positions corresponding to positions within the target peptide that are available to interact with an antigen-recognition molecule and (ii) are identical to the corresponding amino acids of the target peptide. Clause 49: A method of ranking a plurality of potential off-target peptides of a target peptide, the method comprising: obtaining a 3D computational model of a target peptide in complex with the MHC molecule (“MHC-target model”); obtaining a plurality of off target 3D computational models of a potential off-target peptide in a groove of an MHC molecule (“comparison MHC-off-target models”) such that each of the plurality of off-target peptides is respectively represented in one or more comparison MHC-off-target models of the plurality of comparison MHC-off-target models; computing a structural similarly metric for each of the plurality of off-target peptides such that the structural similarity metric indicates a degree of similarity between the MHC-target model and at least a portion of the one or more comparison MHC-off-target models associated with the respective potential off-target peptide; and ranking the plurality of potential off-target peptides based at least in part on the structural similarity metric. Clause 50: The method of Clause 49, wherein obtaining the plurality of comparison MHC-off-target models comprises providing one or more comparison MHC-off-target models of the plurality of comparison MHC-off-target models according to the method of any one of Clauses 1-13. Clause 51: The method of Clause 49 or 50, wherein computing the structural similarity metric for each of the plurality of off-target peptides comprises quantifying structural similarity between a potential off-target peptide of the plurality of off-target peptides in a groove of an MHC molecule and a target peptide in complex with the MHC molecule for the purposes of antigen- recognition molecule binding according to the method of any one of Clauses 21-43. Clause 52: The method of any one of Clauses 49-51, further comprising: identifying high sequence similarity and structurally similar off-target peptide(s), from the plurality of potential off-target peptides, for an antigen-recognition molecule that recognizes an MHC-target peptide
Attorney Docket #: 250298.000961 complex, according to the method of any one of Clauses 44-48; and ranking the high sequence similarity and structurally similar off-target peptide(s) based at least in part on the structural similarity metric. Clause 53: The method of any one of Clauses 49-52, further comprising: providing a portion of the plurality of potential off-target peptides for in vitro assessment of off-target affects of an antigen-recognition model based at least in part on the ranking. Clause 54: The method of any one of Clauses 49-52, further comprising: providing the ranking of the plurality of potential off-target peptides in a database. Clause 55: The method of any one of Clauses 49-53, further comprising: selecting off- target peptides from the plurality of potential off-target peptides based on the ranking; and providing a list of the selected off-target peptides in a database with an association to the target peptide. Clause 56: A method for ranking potential target peptides to mitigate off-target toxicity, the method comprising: obtaining two or more potential target peptides among disease-associated peptides that are predicted to bind to an MHC molecule; obtaining, for each of the potential target peptides, a respective list of potential off-target peptides; ranking, for each of the potential target peptides, potential off-target peptides within the respective list of off-target peptides based at least in part on structural similarity of each potential off-target peptide to the potential target peptide; and ranking the two or more potential target peptides based at least in part on the ranking of potential off-target peptides within the respective list of potential off-target peptides. Clause 57: The method of Clause 56, wherein obtaining, for each of the potential target peptides, a respective list of potential off-target peptides comprises identifying the high sequence similarity and structurally similar off-target peptide(s) according to the method of any one of Clauses 44-48. Clause 58: The method of Clause 56 or 57, wherein ranking, for each of the potential target peptides, potential off-target peptides within the respective list of off-target peptides comprises ranking the off-target peptide(s) according to the method of any one of Clauses 49-55. Clause 59: The method of Clause 58, further comprising: determining, for each of the two or more potential target peptides, a number structurally similar off-target peptide(s) based at least in part on a comparison of the highest ranked potential off-target peptide(s) associated with
Attorney Docket #: 250298.000961 each of the potential target peptides; and ranking the two or more potential target peptides based at least in part on the number of structurally similar off-target peptide(s). Clause 60: A method of ranking a plurality of potential off-target peptides for a target peptide, the method comprising: providing a target conformation, wherein the target conformation is a computational representation of a three-dimensional structure comprising the target peptide and a groove of a major histocompatibility complex (MHC) molecule, wherein the target peptide is positioned within the groove for binding to the MHC molecule; providing a plurality of off- target conformations, wherein each off-target conformation is a computational representation of a three-dimensional structure comprising a potential off-target peptide and the groove of the MHC molecule and wherein each of the off-target conformations corresponds to one of the plurality of potential off-target peptides positioned within the groove for binding to the MHC molecule; aligning the target conformation with each of the off-target conformations in three-dimensional space; computing a difference value for each of the off-target conformations, wherein each difference value quantifies a difference between the target conformation and one of the off-target conformations; and ranking the plurality of off-target conformations based on their respective difference values. Clause 61: The method of Clause 60, wherein the difference values quantify distances between atoms of the target conformation and corresponding atoms of each of the off-target conformations, optionally wherein the difference values are average distances such as root mean square deviations (RMSDs). Clause 62: The method of Clause 61, wherein the difference values quantify distances between heavy atoms only. Clause 63: The method of Clause 61 or 62, wherein the distances comprise the distances between all heavy atoms of the target peptide backbone and all corresponding heavy atoms of each of the potential off-target peptide backbones. Clause 64: The method of Clause 61 or 62, wherein the distances consist of the distances between atoms, optionally all heavy atoms, of the target peptide backbone at each position that has been determined to be available for binding by an antigen-recognizing molecule to a peptide-MHC (pMHC) complex comprising the target peptide bound to the HLA molecule and all corresponding atoms of the potential off-target peptide backbones at each of the corresponding positions, optionally wherein the positions available for binding an antigen-recognizing molecule have been
Attorney Docket #: 250298.000961 predicted by a mutagenesis screen to be those positions that, based on changes in binding affinity, are not involved in binding the MHC molecule, optionally wherein the mutagenesis screen is a computational mutagenesis screen and those positions that are not involved in binding the MHC molecule are determined by a machine learning model, such as an artificial neural network, trained on peptide-MHC binding data. Clause 65: The method of Clause 63, wherein the distances consist of the distances between all heavy atoms of the target peptide backbone and all corresponding heavy atoms of each of the potential off-target peptide backbones. Clause 66: The method of Clause 63, wherein the distances comprise the distances between heavy atoms of at least a portion of the groove of the MHC molecule in the target conformation and a corresponding portion of the groove of the MHC molecule in each of the off- target conformations, optionally wherein the portion comprises the bed and rails of the groove. Clause 67: The method of Clause 66, wherein the portion consists of the bed and the rails of the groove. Clause 68: The method of Clause 60, wherein the difference value quantifies a correlation between a molecular surface interaction fingerprint of the target conformation and each of the off- target conformations, wherein the molecular surface interaction fingerprint characterizes an interaction probability of the three-dimensional structure with an antigen-binding molecule based on one or more surface features, wherein the one or more surface features comprise geometric features and/or chemical features of the surface, optionally wherein the molecular surface interaction fingerprint is a vector, optionally wherein the difference value is a correlation coefficient such as a Pearson correlation coefficient, optionally wherein the geometric features comprise shape index or distance-dependent curvature and/or the chemical features comprise hydropathy, continuum electrostatics, or location of free electrons and proton donors. Clause 69: The method of Clause 68, wherein the molecular surface interaction fingerprint is a vector of interaction scores, wherein each interaction score is associated with a spatially localized portion of the molecular surface of the respective model and is a scalar value representative of a probability of the respective spatially localized portion being involved in a protein-protein interaction, optionally wherein each spatially localized portion is one of a plurality of overlapping patches that encompass the entire molecular surface of the respective model, further
Attorney Docket #: 250298.000961 optionally wherein the vector comprises an interaction score for each patch of the plurality of patches. Clause 70: The method of Clause 69, wherein each patch of the plurality of overlapping patches is a geodesic patch having a fixed geodesic distance from a patch center, optionally wherein the patch center is a vertex of the molecular surface of the respective model and optionally wherein each patch has the same fixed geodesic distance. Clause 71: The method of any one of Clauses 68-70, wherein the molecular surface interaction fingerprints are determined, at least in part, by geometric deep learning, optionally using convolutional neural network. Clause 72: The method of any one of Clauses 68-71, wherein the molecular surface interaction fingerprints for each three-dimensional structure are determined by: a) decomposing a surface of the three-dimensional structure into a plurality of overlapping geodesic patches; b) mapping the one or more surface features of the three-dimensional structure to each of the geodesic patches using polar geodesic coordinates, wherein the one or more surface features comprise one or more, optionally all, of the following features: shape index, distance-dependent curvature, hydropathy, continuum electrostatics, and location of free electrons and proton donors; c) applying a convolutional neural network to each patch to produce an interaction score for each patch, wherein applying the convolutional neural network comprises rotating the geodesic patch to provide rotation invariance, and wherein the convolutional neural network has been trained on i) portions of surfaces that form interface regions between pairs of proteins that are known binding partners and ii) portions of surfaces that do not form interface regions between pairs of proteins, optionally wherein the interface regions are solvent inaccessible regions in complexes of the known binding partners and optionally wherein the proportions of surfaces that do not form interface regions are derived from one or both of the known binding partners; and d) determining the molecular surface interaction fingerprints based on the interaction scores of the plurality of overlapping geodesic patches, wherein determining the molecular surface interaction fingerprint comprises identifying spatially corresponding patches between the MHC-target model and each respective MHC off-target model and wherein the molecular surface interaction fingerprint comprises the interaction scores for the corresponding patches, optionally wherein determining the molecular surface interaction fingerprint comprises removing interaction scores that are not associated with a corresponding patch.
Attorney Docket #: 250298.000961 Clause 73: The method of any one of Clauses 72, wherein mapping the surface features to the geodesic patches comprises locally averaging the one or more surface features to learnable Gaussian kernels. Clause 74: The method of any one of Clauses 60-73, wherein the plurality of potential off-target peptides, are selected from a list of peptides derivable from a proteome based on one or more of the following: a) a degree of similarity with the target peptide sequence, optionally wherein a threshold degree of similarity for selection requires one or more residue mismatches and/or requires one or more identical residues; b) a number of identical residues, optionally at least three, at positions within the target peptide sequence that have been determined to be available for binding by an antigen-recognizing molecule to a peptide-MHC (pMHC) complex comprising the target peptide bound to the HLA molecule, optionally wherein the positions available for binding an antigen-recognizing molecule have been predicted by a mutagenesis screen to be those positions that, based on changes in binding affinity, are not involved in binding the MHC molecule, optionally wherein the mutagenesis screen is a computational mutagenesis screen and those positions that are not involved in binding the MHC molecule are determined by a machine learning model, such as an artificial neural network, trained on peptide-MHC binding data; c) binding affinity to the MHC molecule, wherein peptides that are determined to have a binding affinity below a threshold binding affinity are not selected, optionally wherein binding affinity is determined by a machine learning model, such as an artificial neural network, trained on peptide- MHC binding data; and d) tissue expression, wherein peptides that are not expressed in normal tissue, optionally not in essential normal tissue, are not selected. Clause 75: The method of Clause 74, wherein the proteome is a human proteome and the normal tissue or essential normal tissue is human tissue. Clause 76: The method of any one of Clauses 60-75, wherein the three-dimensional structure of the target conformation and/or the three-dimensional structures of one or more, optionally all, of the plurality of off-target conformations have been determined, at least in part, from a predicted structure determined using one or more of the following: a) template-based modeling; b) sequence-based machine learning; and/or c) experimental measurements, optionally wherein the experimental measurements are made using X-ray crystallography or Cryo-electron microscopy (Cryo-EM).
Attorney Docket #: 250298.000961 Clause 77: The method of Clause 76, wherein the predicted structure(s) has been determined by a template-based modeling process comprising: a) searching a database of known protein structures comprising a template peptide bound to the MHC molecule to identify a plurality of template structures based, at least in part, on sequence similarity of the template peptide with the peptide sequence of the target peptide or potential off-target peptide; b) aligning the peptide sequence to each of the three-dimensional structures of the template structures based on a comparison of the sequences; c) calculating an energy score for each of the aligned peptide sequences; d) selecting a template structure based on the calculated energy scores; and e) assigning a predicted structure based on the selected template structure. Clause 78: The method of Clause 75 or 76, wherein the predicted structure(s) has been determined by sequence-based machine learning, optionally where the sequence-based machine learning uses a neural network-based model. Clause 79: The method of any one Clauses 60-78, wherein the three-dimensional structure of the target conformation and/or the three-dimensional structures of one or more, optionally all, of the plurality of off-target conformations have been determined, at least in part, from a prepacked predicted structure, wherein the prepacked predicted structure is optimized based at least in part on the selection of rotamer combinations for the peptide and MHC molecule to eliminate steric clashes, optionally wherein the predicted structure is determined using templated- based modeling or experimental measurements followed by prepacking. Clause 80: The method of any one of Clauses 60-79, wherein the three-dimensional structure of the target conformation and the three-dimensional structures of all of the plurality of off-target conformations have been determined, at least in part, from predicted structures determined using the same approach, optionally template-based modeling or sequence-based machine learning. Clause 81: The method of any one of Clauses 60-80, wherein the three-dimensional structure of the target conformation has been refined from an initial three-dimensional structure by a computational peptide docking algorithm, optionally wherein the initial three-dimensional structure is a predicted structure of any one of Clauses 14-18, optionally wherein the predicted structure is a prepacked predicted structure. Clause 82: The method of any one Clauses 60-81, wherein the three-dimensional structure of one or more, optionally all, of the plurality of off-target conformations have been
Attorney Docket #: 250298.000961 refined from initial three-dimensional structures by a computational peptide docking algorithm, optionally wherein the initial three-dimensional structure is a predicted structure of any one of Clauses 14-18, optionally wherein the predicted structure is a prepacked predicted structure. Clause 83: The method of Clause 81 or 82, wherein the computational peptide docking algorithm comprises a Monte Carlo search with minimization algorithm. Clause 84: The method of any one of Clauses 81-83, wherein the computational peptide docking algorithm comprises the following steps: a) modifying an energy function used to evaluate the three-dimensional structure by reducing the weight of van der Waals repulsive forces and/or increasing the weight of van der Waals attractive forces, optionally both, to an extent that will permit sampling of alternative conformations of the peptide within the groove while preventing the peptide from separating from a binding pocket within the groove of the MHC molecule during a subsequent energy minimization reconfiguration of the three-dimensional structure; b) optimizing the rigid body orientation by applying a random rigid body perturbation, optionally a Gaussian rigid body perturbation, comprising a rotation and/or translation to affect the orientation of the peptide within the three-dimensional structure with respect to the groove, repacking the side chains of rotamers within the peptide-MHC interface following the random rigid body perturbation, and applying an energy minimization step following the repacking to arrive at a reconfigured rigid body orientation, wherein the repacking comprises optimizing the selection of rotamer combinations for the peptide-MHC interface to eliminate steric clashes, wherein the energy minimization step comprises using a deterministic algorithm to find a local energy minimum, wherein the reconfigured rigid body orientation is accepted only if an energy function criterion, optionally the Metropolis criterion, is met, and wherein the applying of the random rigid body perturbation, the repacking, and the energy minimization step are sequentially repeated for a plurality of cycles, optionally a predefined number of cycles and/or until an energy criterion is satisfied; c) optimizing the peptide backbone conformation, following optimization of the rigid body orientation, by applying a random torsion angle perturbation to the peptide backbone, optionally comprising a Rosetta small move or a Rosetta shear move, repacking the side chains of rotamers within the peptide-MHC interface following the random torsion angle perturbation, and applying an energy minimization step following the repacking to arrive at a reconfigured peptide backbone conformation, wherein the repacking comprises optimizing the selection of rotamer combinations for the peptide-MHC interface to eliminate steric clashes, wherein the energy
Attorney Docket #: 250298.000961 minimization step comprises using a deterministic algorithm to find a local energy minimum, wherein the reconfigured peptide backbone conformation is accepted only if an energy function criterion, optionally the Metropolis criterion, is met, and wherein the applying of the random torsion angle perturbation, the repacking, and the energy minimization step are sequentially repeated for a plurality of cycles, optionally a predefined number of cycles and/or until an energy criterion is satisfied, optionally wherein the random torsion angle perturbation alternates between Rosetta small moves and Rosetta shear moves each cycle; and e) repeating steps (b) and (c) for a plurality of cycles wherein the van der Waals forces are gradually ramped back towards normal values such that the last cycle is performed with the normal values to arrive at a refined three- dimensional structure. Clause 85: The method of any one of Clauses 81-84, wherein the computational peptide docking algorithm is repeated a plurality of times, optionally at least 100, 200, 500, 1,000, 2,000, 5,000, or 10,000 times, to produce a plurality of refined three-dimensional structures and wherein ranking the plurality of off-target conformations based on their respective difference values comprises ranking the plurality of off-target conformations based on an aggregate measure of the computed difference values for at least several of the plurality of refined three-dimensional structures. Clause 86: The method of Clause 85, wherein the at least several of the plurality of refined three-dimensional structures consist of a predetermined number, optionally at least 5, of the refined three-dimensional structures having the lowest energy scores as evaluated by an energy function. Clause 87: The method of Clause 84 or 85, wherein the aggregate measure comprises an average, median, or minimum. Clause 88: The method of Clause 86 or 87, wherein each energy score comprises a total energy score for the refined three-dimensional structure, an interface score that measures an energy of pairwise interactions across a peptide-MHC interface, a peptide score that measures an energy function over the residues of the peptide, or a combination thereof, optionally a weighted sum. Clause 89: A non-transitory computer-readable medium configured to communicate with one or more processor(s) of a computational device, the non-transitory computer-readable medium including instructions thereon, that when executed by the processor(s), cause the computational device to perform the method of any one of Clauses 1-88.
Attorney Docket #: 250298.000961 Having shown and described exemplary embodiments of the subject matter contained herein, further adaptations of the methods and systems described herein may be accomplished by appropriate modifications without departing from the scope of the claims. In addition, where methods and steps described above indicate certain events occurring in certain order, it is intended that certain steps do not have to be performed in the order described but in any order as long as the steps allow the embodiments to function for their intended purposes. Therefore, to the extent there are variations of the invention, which are within the spirit of the disclosure or equivalent to the inventions found in the claims, it is the intent that this patent will cover those variations as well. Some such modifications should be apparent to those skilled in the art. For instance, the examples, embodiments, geometrics, materials, dimensions, ratios, steps, and the like discussed above are illustrative. Accordingly, the claims should not be limited to the specific details of structure and operation set forth in the written description and drawings. Further References The below references provide further detail on the theory of molecular mimicry (and alternative theories for T cell cross-reactivity) as well as various computational tools that may be used or adapted according ot the disclosure herein for protein folding and/or docking predictions and other molecular modeling and analysis tasks. Each is herein incorporated by reference in its entirety. • Gouttefangeas, et al. “The good and the bad of T cell cross-reactivity: challenges and opportunities for novel therapeutics in autoimmunity and cancer,” Front Immunol. 2023 Jun 19:14:1212546. • Rohl, et al. “Protein structure prediction using Rosetta,” Methods Enzymol. 2004:383:66- 93 • Das, et al. “Macromolecular modeling with rosetta,” Annu Rev Biochem. 2008:77:363- 82. • Ravel, et al., “Sub-angstrom modeling of complexes between flexible peptides and globular proteins,” Proteins. 2010 Jul;78(9):2029-40. • London, et al. “Rosetta FlexPepDock web server--high resolution modeling of peptide- protein interactions,” Nucleic Acids Res. 2011 Jul;39(Web Server issue):W249-53.
Attorney Docket #: 250298.000961 • Jumper, et al. “Highly accurate protein structure prediction with AlphaFold,” Nature. 2021 Aug;596(7873):583-589. • Evans, et al. “Protein complex prediction with AlphaFold-Multimer,” bioRxiv 2021.10.04.463034; doi: https://doi.org/10.1101/2021.10.04.463034. • Cipriano, et al. “GRAPE: GRaphical Abstracted Protein Explorer,” Nucleic Acids Res. 2010 Jul;38(Web Server issue):W595-601. • Lamprecht “Pyoints: A Python package for point cloud, voxel and raster processing,” Journal of Open Source Software, 20194(36), 990, https://doi.org/10.21105/joss.00990.