The development of a protein-specifically adapted objective function for docking is described. St... more The development of a protein-specifically adapted objective function for docking is described. Structural and energetic information about known protein-ligand complexes is exploited to tailor knowledge-based potentials using a "reverse", protein-based CoMFA-type (=AFMoC) approach. That way, effects due to protein flexibility and information about multiple solvation schemes can be implicitly incorporated. Compared to the application of AFMoC for binding affinity predictions, a Shannon entropy based column filtering of the descriptor matrix and the capping of adapted repulsive potentials within the binding site have turned out to be crucial for the success of this method. The new developed approach (AFMoC(obj)) was validated on a data set of 66 HIV-1 protease inhibitors, for which experimental structural information was available. Convincingly, for ligands with up to 20 rotatable bonds, in more than 75% of all cases a binding mode below 2 A rmsd has been identified on the first scoring rank when AFMoC(obj)-based potentials were used as the objective function in AutoDock. With respect to nonadapted DrugScore or AutoDock fields, the binding mode prediction accuracy was significantly improved by 14%. Noteworthy, very similar results were obtained for training and test set compounds, demonstrating the strength and robustness of this method. Implications of our findings for binding affinity predictions and its usage in virtual screening are further discussed.
Following the formalism used for the development of the knowledge-based scoring function DrugScor... more Following the formalism used for the development of the knowledge-based scoring function DrugScore, new distance-dependent pair potentials are obtained from nonbonded interactions in small organic molecule crystal packings. Compared to potentials derived from protein-ligand complexes, the better resolved small molecule structures provide relevant contact data in a more balanced distribution of atom types and produce potentials of superior statistical significance and more detailed shape. Applied to recognizing binding geometries of ligands docked into proteins, this new scoring function (DrugScore(CSD)) ranks the crystal structures of 100 protein-ligand complexes best among up to 100 generated decoy geometries in 77% of all cases. Accepting root-mean-square deviations (rmsd) of up to 2 angstroms from the native pose as well-docked solutions, a correct binding mode is found in 87% of the cases. This translates into an improvement of the new scoring function of 57% with respect to the retrieval of the crystal structure and 20% with respect to the identification of a well-docked ligand pose compared to the original Protein Data Bank-based DrugScore. In the analysis of decoy geometries of cross-docking studies, DrugScore(CSD) shows equivalent or increased performance compared to the original PDB-based DrugScore. Furthermore, DrugScore(CSD) predicts binding affinities convincingly. Reducing the set of docking solutions to examples that deviate increasingly from the native pose results in a loss of performance of DrugScore(CSD). This indicates that a necessary prerequisite to successfully resolving the scoring problem with a more discriminative scoring function is the generation of highly accurate ligand poses, which approximate the native pose to below 1 angstroms rmsd, in a docking run.
Proteins-structure Function and Bioinformatics, 2004
Changes in flexibility upon pro- tein-protein complex formation of H-Ras and the Ras-binding doma... more Changes in flexibility upon pro- tein-protein complex formation of H-Ras and the Ras-binding domain of C-Raf1 have been investi- gated using the molecular framework approach FIRST (Floppy Inclusion and Rigid Substructure Topology) and molecular dynamics simulations (MD) of in total 35 ns length. In a computational time of about one second, FIRST identifies flexible and rigid regions in a single,
A new application of DrugScore is reported in which the knowledge-based pair potentials serve as ... more A new application of DrugScore is reported in which the knowledge-based pair potentials serve as objective function in docking optimizations. The Lamarckian genetic algorithm of AutoDock is used to search for favorable ligand binding modes guided by DrugScore grids as representations of the protein binding site. The approach is found to be successful in many cases where DrugScore-based re-ranking of already docked ligand conformations does not yield satisfactory results. Compared to the AutoDock scoring function, DrugScore yields slightly superior results in flexible docking.
The development of a new knowledge-based scoring function (DrugScore) and its power to recognize ... more The development of a new knowledge-based scoring function (DrugScore) and its power to recognize binding modes close to experiment, to predict binding affinities, and to identify ‘hot spots’ in binding pockets is presented. Structural information is extracted from crystallographically determined protein-ligand complexes using ReLiBase and converted into distance-dependent pair-preferences and solvent-accessible surface (SAS) dependent singlet preferences of protein and ligand
In virtual screening, small-molecule ligands are docked into protein binding sites and their bind... more In virtual screening, small-molecule ligands are docked into protein binding sites and their binding affinity is predicted. Knowledge-based, regression-based and first-principle-based methods have been developed to rank computer-generated binding modes. As a result of still existing deficiencies, a best compromise might be the combination of several scoring schemes into a consensus scoring approach.
The development and validation of a new knowledge-based scoring function (DrugScore) to describe ... more The development and validation of a new knowledge-based scoring function (DrugScore) to describe the binding geometry of ligands in proteins is presented. It discriminates efficiently between well-docked ligand binding modes (root-mean-square deviation <2.0 A with respect to a crystallographically determined reference complex) and those largely deviating from the native structure, e.g. generated by computer docking programs. Structural information is extracted from crystallographically determined protein-ligand complexes using ReLiBase and converted into distance-dependent pair-preferences and solvent-accessible surface (SAS) dependent singlet preferences for protein and ligand atoms. Definition of an appropriate reference state and accounting for inaccuracies inherently present in experimental data is required to achieve good predictive power. The sum of the pair preferences and the singlet preferences is calculated based on the 3D structure of protein-ligand binding modes generated by docking tools. For two test sets of 91 and 68 protein-ligand complexes, taken from the Protein Data Bank (PDB), the calculated score recognizes poses generated by FlexX deviating <2 A from the crystal structure on rank 1 in three quarters of all possible cases. Compared to FlexX, this is a substantial improvement. For ligand geometries generated by DOCK, DrugScore is superior to the "chemical scoring" implemented into this tool, while comparable results are obtained using the "energy scoring" in DOCK. None of the presently known scoring functions achieves comparable power to extract binding modes in agreement with experiment. It is fast to compute, regards implicitly solvation and entropy contributions and produces correctly the geometry of directional interactions. Small deviations in the 3D structure are tolerated and, since only contacts to non-hydrogen atoms are regarded, it is independent from assumptions of protonation states.
The highly flexible nature of RNA provides a formidable challenge for structure-based drug design... more The highly flexible nature of RNA provides a formidable challenge for structure-based drug design approaches that target RNA. We introduce an approach for modeling target conformational changes in RNA-ligand docking based on potential grids that are represented as elastic bodies using Navier's equation. This representation provides an accurate and efficient description of RNA-ligand interactions even in the case of a moving RNA structure. When applied to a data set of 17 RNA-ligand complexes, filtered out of the largest validation data set used for RNA-ligand docking so far, the approach is twice as successful as docking into an apo structure and still half as successful as redocking to the holo structure. The approach allows considering RNA movements of up to 6 Å rmsd and is based on a uniform and robust parametrization of the properties of the elastic potential grids, so that the approach is applicable to different RNA-ligand complex classes.
Constraint network analysis (CNA) is a graph theory-based rigidity analysis approach for linking ... more Constraint network analysis (CNA) is a graph theory-based rigidity analysis approach for linking a biomolecule's structure, flexibility, (thermo)stability and function. Results from CNA are highly information-rich and require intuitive, synchronized and interactive visualization for a comprehensive analysis. We developed VisualCNA, an easy-to-use PyMOL plug-in that allows setup of CNA runs and analysis of CNA results linking plots with molecular graphics representations. From a practical viewpoint, the most striking feature of VisualCNA is that it facilitates interactive protein engineering aimed at improving thermostability. VisualCNA and its dependencies (CNA and FIRST software) are available free of charge under GPL and academic licenses, respectively. VisualCNA and CNA are available at http://cpclab.uni-duesseldorf.de/software; FIRST is available at http://flexweb.asu.edu. gohlke@uni-duesseldorf.de.
Various coarse graining schemes have been proposed to speed up computer simulations of the motion... more Various coarse graining schemes have been proposed to speed up computer simulations of the motion within large biomolecules, which can contain hundreds of thousands of atoms. We point out here that there is a very natural way of doing this, using the rigid regions identified within a biomolecule as the coarse grain elements. Subsequently, computer resources can be concentrated on the flexible connections between the rigid units. Examples of the use of such techniques are given for the protein barnase and the maltodextrin binding protein, using the geometric simulation technique FRODA and the rigidity enhanced elastic network model RCNMA to compute mobilities and atomic displacements.
Changes in flexibility upon protein-protein complex formation of H-Ras and the Ras-binding domain... more Changes in flexibility upon protein-protein complex formation of H-Ras and the Ras-binding domain of C-Raf1 have been investigated using the molecular framework approach FIRST (Floppy Inclusion and Rigid Substructure Topology) and molecular dynamics simulations (MD) of in total approximately 35 ns length. In a computational time of about one second, FIRST identifies flexible and rigid regions in a single, static three-dimensional molecular framework, whose vertices represent protein atoms and whose edges represent covalent and non-covalent (hydrogen bond and hydrophobic) constraints and fixed bond angles within the protein. The two methods show a very good agreement with respect to the identification of changes in flexibility in both binding partners on a local scale. This implies that flexibility can be successfully predicted by identifying which bonds limit motion within a molecule and how they are coupled. In particular, as identified by MD, the beta-sheet in Raf shows considerab...
Estimating protein-protein interaction energies is a very challenging task for current simulation... more Estimating protein-protein interaction energies is a very challenging task for current simulation protocols. Here, absolute binding free energies are reported for the complex H-Ras/C-Raf1 using the MM-PB(GB)SA approach, testing the internal consistency and model dependence of the results. Averaging gas-phase energies (MM), solvation free energies as determined by Generalized Born models (GB/SA), and entropic contributions calculated by normal mode analysis for snapshots obtained from 10 ns explicit-solvent molecular dynamics in general results in an overestimation of the binding affinity when a solvent-accessible surface area-dependent model is used to estimate the nonpolar solvation contribution. Applying the sum of a cavity solvation free energy and explicitly modeled solute-solvent van der Waals interaction energies instead provides less negative estimates for the nonpolar solvation contribution. When the polar contribution to the solvation free energy is determined by solving th...
Absolute binding free energy calculations and free energy decompositions are presented for the pr... more Absolute binding free energy calculations and free energy decompositions are presented for the protein-protein complexes H-Ras/C-Raf1 and H-Ras/RalGDS. Ras is a central switch in the regulation of cell proliferation and differentiation. In our study, we investigate the capability of the molecular mechanics (MM)-generalized Born surface area (GBSA) approach to estimate absolute binding free energies for the protein-protein complexes. Averaging gas-phase energies, solvation free energies, and entropic contributions over snapshots extracted from trajectories of the unbound proteins and the complexes, calculated binding free energies (Ras-Raf: -15.0(+/-6.3)kcal mol(-1); Ras-RalGDS: -19.5(+/-5.9)kcal mol(-1)) are in fair agreement with experimentally determined values (-9.6 kcal mol(-1); -8.4 kcal mol(-1)), if appropriate ionic strength is taken into account. Structural determinants of the binding affinity of Ras-Raf and Ras-RalGDS are identified by means of free energy decomposition. Fo...
The development of a new tailor-made scoring function to predict binding affinities of protein-li... more The development of a new tailor-made scoring function to predict binding affinities of protein-ligand complexes is described. Knowledge-based pair-potentials are specifically adapted to a particular protein by considering additional ligand-based information. The formalism applied to derive the new function is similar to the well-known CoMFA approach, however, the fields used in the approach originate from the protein environment (and not from the aligned ligands as in CoMFA, thus, a "reverse" CoMFA (= AFMoC) named Adaptation of Fields for Molecular Comparison is performed). A regular-spaced grid is placed into the binding site and knowledge-based pair-potentials between protein atoms and ligand atom probes are mapped onto the grid intersections resulting in "potential fields". By multiplying distance-dependent atom-type properties of actual ligands docked into the binding site with the neighboring grid values, "interaction fields" are produced from the ...
Purpose. Peptidomimetic thrombin inhibitors derived from Na-(2-naphthylsulfonyl)-3-amidino-phenyl... more Purpose. Peptidomimetic thrombin inhibitors derived from Na-(2-naphthylsulfonyl)-3-amidino-phenylalanine with different basic and acidic substituents were investigated with respect to their intestinal transport behavior.
A promising way to interfere with biological processes is through the control of protein-protein ... more A promising way to interfere with biological processes is through the control of protein-protein interactions by means of small molecules that modulate the formation of protein-protein complexes. Although the feasibility of this approach has been demonstrated in ...
An assessment of nine scoring functions commonly applied in docking using a set of 189 protein-li... more An assessment of nine scoring functions commonly applied in docking using a set of 189 protein-ligand complexes is presented. The scoring functions include the CHARMm potential, the scoring function DrugScore, the scoring function used in AutoDock, the three scoring functions implemented in DOCK, as well as three scoring functions implemented in the CScore module in SYBYL (PMF, Gold, ChemScore). We evaluated the abilities of these scoring functions to recognize near-native configurations among a set of decoys and to rank binding affinities. Binding site decoys were generated by molecular dynamics with restraints. To investigate whether the scoring functions can also be applied for binding site detection, decoys on the protein surface were generated. The influence of the assignment of protonation states was probed by either assigning "standard" protonation states to binding site residues or adjusting protonation states according to experimental evidence. The role of solvation models in conjunction with CHARMm was explored in detail. These include a distance-dependent dielectric function, a generalized Born model, and the Poisson equation. We evaluated the effect of using a rigid receptor on the outcome of docking by generating all-pairs decoys ("cross-decoys") for six trypsin and seven HIV-1 protease complexes. The scoring functions perform well to discriminate near-native from misdocked conformations, with CHARMm, DOCK-energy, DrugScore, ChemScore, and AutoDock yielding recognition rates of around 80%. Significant degradation in performance is observed in going from decoy to cross-decoy recognition for CHARMm in the case of HIV-1 protease, whereas DrugScore and ChemScore, as well as CHARMm in the case of trypsin, show only small deterioration. In contrast, the prediction of binding affinities remains problematic for all of the scoring functions. ChemScore gives the highest correlation value with R(2) = 0.51 for the set of 189 complexes and R(2) = 0.43 for the set of 116 complexes that does not contain any of the complexes used to calibrate this scoring function. Neither a more accurate treatment of solvation nor a more sophisticated charge model for zinc improves the quality of the results. Improved modeling of the protonation states, however, leads to a better prediction of binding affinities in the case of the generalized Born and the Poisson continuum models used in conjunction with the CHARMm force field.
The development of a protein-specifically adapted objective function for docking is described. St... more The development of a protein-specifically adapted objective function for docking is described. Structural and energetic information about known protein-ligand complexes is exploited to tailor knowledge-based potentials using a "reverse", protein-based CoMFA-type (=AFMoC) approach. That way, effects due to protein flexibility and information about multiple solvation schemes can be implicitly incorporated. Compared to the application of AFMoC for binding affinity predictions, a Shannon entropy based column filtering of the descriptor matrix and the capping of adapted repulsive potentials within the binding site have turned out to be crucial for the success of this method. The new developed approach (AFMoC(obj)) was validated on a data set of 66 HIV-1 protease inhibitors, for which experimental structural information was available. Convincingly, for ligands with up to 20 rotatable bonds, in more than 75% of all cases a binding mode below 2 A rmsd has been identified on the first scoring rank when AFMoC(obj)-based potentials were used as the objective function in AutoDock. With respect to nonadapted DrugScore or AutoDock fields, the binding mode prediction accuracy was significantly improved by 14%. Noteworthy, very similar results were obtained for training and test set compounds, demonstrating the strength and robustness of this method. Implications of our findings for binding affinity predictions and its usage in virtual screening are further discussed.
Following the formalism used for the development of the knowledge-based scoring function DrugScor... more Following the formalism used for the development of the knowledge-based scoring function DrugScore, new distance-dependent pair potentials are obtained from nonbonded interactions in small organic molecule crystal packings. Compared to potentials derived from protein-ligand complexes, the better resolved small molecule structures provide relevant contact data in a more balanced distribution of atom types and produce potentials of superior statistical significance and more detailed shape. Applied to recognizing binding geometries of ligands docked into proteins, this new scoring function (DrugScore(CSD)) ranks the crystal structures of 100 protein-ligand complexes best among up to 100 generated decoy geometries in 77% of all cases. Accepting root-mean-square deviations (rmsd) of up to 2 angstroms from the native pose as well-docked solutions, a correct binding mode is found in 87% of the cases. This translates into an improvement of the new scoring function of 57% with respect to the retrieval of the crystal structure and 20% with respect to the identification of a well-docked ligand pose compared to the original Protein Data Bank-based DrugScore. In the analysis of decoy geometries of cross-docking studies, DrugScore(CSD) shows equivalent or increased performance compared to the original PDB-based DrugScore. Furthermore, DrugScore(CSD) predicts binding affinities convincingly. Reducing the set of docking solutions to examples that deviate increasingly from the native pose results in a loss of performance of DrugScore(CSD). This indicates that a necessary prerequisite to successfully resolving the scoring problem with a more discriminative scoring function is the generation of highly accurate ligand poses, which approximate the native pose to below 1 angstroms rmsd, in a docking run.
Proteins-structure Function and Bioinformatics, 2004
Changes in flexibility upon pro- tein-protein complex formation of H-Ras and the Ras-binding doma... more Changes in flexibility upon pro- tein-protein complex formation of H-Ras and the Ras-binding domain of C-Raf1 have been investi- gated using the molecular framework approach FIRST (Floppy Inclusion and Rigid Substructure Topology) and molecular dynamics simulations (MD) of in total 35 ns length. In a computational time of about one second, FIRST identifies flexible and rigid regions in a single,
A new application of DrugScore is reported in which the knowledge-based pair potentials serve as ... more A new application of DrugScore is reported in which the knowledge-based pair potentials serve as objective function in docking optimizations. The Lamarckian genetic algorithm of AutoDock is used to search for favorable ligand binding modes guided by DrugScore grids as representations of the protein binding site. The approach is found to be successful in many cases where DrugScore-based re-ranking of already docked ligand conformations does not yield satisfactory results. Compared to the AutoDock scoring function, DrugScore yields slightly superior results in flexible docking.
The development of a new knowledge-based scoring function (DrugScore) and its power to recognize ... more The development of a new knowledge-based scoring function (DrugScore) and its power to recognize binding modes close to experiment, to predict binding affinities, and to identify ‘hot spots’ in binding pockets is presented. Structural information is extracted from crystallographically determined protein-ligand complexes using ReLiBase and converted into distance-dependent pair-preferences and solvent-accessible surface (SAS) dependent singlet preferences of protein and ligand
In virtual screening, small-molecule ligands are docked into protein binding sites and their bind... more In virtual screening, small-molecule ligands are docked into protein binding sites and their binding affinity is predicted. Knowledge-based, regression-based and first-principle-based methods have been developed to rank computer-generated binding modes. As a result of still existing deficiencies, a best compromise might be the combination of several scoring schemes into a consensus scoring approach.
The development and validation of a new knowledge-based scoring function (DrugScore) to describe ... more The development and validation of a new knowledge-based scoring function (DrugScore) to describe the binding geometry of ligands in proteins is presented. It discriminates efficiently between well-docked ligand binding modes (root-mean-square deviation <2.0 A with respect to a crystallographically determined reference complex) and those largely deviating from the native structure, e.g. generated by computer docking programs. Structural information is extracted from crystallographically determined protein-ligand complexes using ReLiBase and converted into distance-dependent pair-preferences and solvent-accessible surface (SAS) dependent singlet preferences for protein and ligand atoms. Definition of an appropriate reference state and accounting for inaccuracies inherently present in experimental data is required to achieve good predictive power. The sum of the pair preferences and the singlet preferences is calculated based on the 3D structure of protein-ligand binding modes generated by docking tools. For two test sets of 91 and 68 protein-ligand complexes, taken from the Protein Data Bank (PDB), the calculated score recognizes poses generated by FlexX deviating <2 A from the crystal structure on rank 1 in three quarters of all possible cases. Compared to FlexX, this is a substantial improvement. For ligand geometries generated by DOCK, DrugScore is superior to the "chemical scoring" implemented into this tool, while comparable results are obtained using the "energy scoring" in DOCK. None of the presently known scoring functions achieves comparable power to extract binding modes in agreement with experiment. It is fast to compute, regards implicitly solvation and entropy contributions and produces correctly the geometry of directional interactions. Small deviations in the 3D structure are tolerated and, since only contacts to non-hydrogen atoms are regarded, it is independent from assumptions of protonation states.
The highly flexible nature of RNA provides a formidable challenge for structure-based drug design... more The highly flexible nature of RNA provides a formidable challenge for structure-based drug design approaches that target RNA. We introduce an approach for modeling target conformational changes in RNA-ligand docking based on potential grids that are represented as elastic bodies using Navier's equation. This representation provides an accurate and efficient description of RNA-ligand interactions even in the case of a moving RNA structure. When applied to a data set of 17 RNA-ligand complexes, filtered out of the largest validation data set used for RNA-ligand docking so far, the approach is twice as successful as docking into an apo structure and still half as successful as redocking to the holo structure. The approach allows considering RNA movements of up to 6 Å rmsd and is based on a uniform and robust parametrization of the properties of the elastic potential grids, so that the approach is applicable to different RNA-ligand complex classes.
Constraint network analysis (CNA) is a graph theory-based rigidity analysis approach for linking ... more Constraint network analysis (CNA) is a graph theory-based rigidity analysis approach for linking a biomolecule's structure, flexibility, (thermo)stability and function. Results from CNA are highly information-rich and require intuitive, synchronized and interactive visualization for a comprehensive analysis. We developed VisualCNA, an easy-to-use PyMOL plug-in that allows setup of CNA runs and analysis of CNA results linking plots with molecular graphics representations. From a practical viewpoint, the most striking feature of VisualCNA is that it facilitates interactive protein engineering aimed at improving thermostability. VisualCNA and its dependencies (CNA and FIRST software) are available free of charge under GPL and academic licenses, respectively. VisualCNA and CNA are available at http://cpclab.uni-duesseldorf.de/software; FIRST is available at http://flexweb.asu.edu. gohlke@uni-duesseldorf.de.
Various coarse graining schemes have been proposed to speed up computer simulations of the motion... more Various coarse graining schemes have been proposed to speed up computer simulations of the motion within large biomolecules, which can contain hundreds of thousands of atoms. We point out here that there is a very natural way of doing this, using the rigid regions identified within a biomolecule as the coarse grain elements. Subsequently, computer resources can be concentrated on the flexible connections between the rigid units. Examples of the use of such techniques are given for the protein barnase and the maltodextrin binding protein, using the geometric simulation technique FRODA and the rigidity enhanced elastic network model RCNMA to compute mobilities and atomic displacements.
Changes in flexibility upon protein-protein complex formation of H-Ras and the Ras-binding domain... more Changes in flexibility upon protein-protein complex formation of H-Ras and the Ras-binding domain of C-Raf1 have been investigated using the molecular framework approach FIRST (Floppy Inclusion and Rigid Substructure Topology) and molecular dynamics simulations (MD) of in total approximately 35 ns length. In a computational time of about one second, FIRST identifies flexible and rigid regions in a single, static three-dimensional molecular framework, whose vertices represent protein atoms and whose edges represent covalent and non-covalent (hydrogen bond and hydrophobic) constraints and fixed bond angles within the protein. The two methods show a very good agreement with respect to the identification of changes in flexibility in both binding partners on a local scale. This implies that flexibility can be successfully predicted by identifying which bonds limit motion within a molecule and how they are coupled. In particular, as identified by MD, the beta-sheet in Raf shows considerab...
Estimating protein-protein interaction energies is a very challenging task for current simulation... more Estimating protein-protein interaction energies is a very challenging task for current simulation protocols. Here, absolute binding free energies are reported for the complex H-Ras/C-Raf1 using the MM-PB(GB)SA approach, testing the internal consistency and model dependence of the results. Averaging gas-phase energies (MM), solvation free energies as determined by Generalized Born models (GB/SA), and entropic contributions calculated by normal mode analysis for snapshots obtained from 10 ns explicit-solvent molecular dynamics in general results in an overestimation of the binding affinity when a solvent-accessible surface area-dependent model is used to estimate the nonpolar solvation contribution. Applying the sum of a cavity solvation free energy and explicitly modeled solute-solvent van der Waals interaction energies instead provides less negative estimates for the nonpolar solvation contribution. When the polar contribution to the solvation free energy is determined by solving th...
Absolute binding free energy calculations and free energy decompositions are presented for the pr... more Absolute binding free energy calculations and free energy decompositions are presented for the protein-protein complexes H-Ras/C-Raf1 and H-Ras/RalGDS. Ras is a central switch in the regulation of cell proliferation and differentiation. In our study, we investigate the capability of the molecular mechanics (MM)-generalized Born surface area (GBSA) approach to estimate absolute binding free energies for the protein-protein complexes. Averaging gas-phase energies, solvation free energies, and entropic contributions over snapshots extracted from trajectories of the unbound proteins and the complexes, calculated binding free energies (Ras-Raf: -15.0(+/-6.3)kcal mol(-1); Ras-RalGDS: -19.5(+/-5.9)kcal mol(-1)) are in fair agreement with experimentally determined values (-9.6 kcal mol(-1); -8.4 kcal mol(-1)), if appropriate ionic strength is taken into account. Structural determinants of the binding affinity of Ras-Raf and Ras-RalGDS are identified by means of free energy decomposition. Fo...
The development of a new tailor-made scoring function to predict binding affinities of protein-li... more The development of a new tailor-made scoring function to predict binding affinities of protein-ligand complexes is described. Knowledge-based pair-potentials are specifically adapted to a particular protein by considering additional ligand-based information. The formalism applied to derive the new function is similar to the well-known CoMFA approach, however, the fields used in the approach originate from the protein environment (and not from the aligned ligands as in CoMFA, thus, a "reverse" CoMFA (= AFMoC) named Adaptation of Fields for Molecular Comparison is performed). A regular-spaced grid is placed into the binding site and knowledge-based pair-potentials between protein atoms and ligand atom probes are mapped onto the grid intersections resulting in "potential fields". By multiplying distance-dependent atom-type properties of actual ligands docked into the binding site with the neighboring grid values, "interaction fields" are produced from the ...
Purpose. Peptidomimetic thrombin inhibitors derived from Na-(2-naphthylsulfonyl)-3-amidino-phenyl... more Purpose. Peptidomimetic thrombin inhibitors derived from Na-(2-naphthylsulfonyl)-3-amidino-phenylalanine with different basic and acidic substituents were investigated with respect to their intestinal transport behavior.
A promising way to interfere with biological processes is through the control of protein-protein ... more A promising way to interfere with biological processes is through the control of protein-protein interactions by means of small molecules that modulate the formation of protein-protein complexes. Although the feasibility of this approach has been demonstrated in ...
An assessment of nine scoring functions commonly applied in docking using a set of 189 protein-li... more An assessment of nine scoring functions commonly applied in docking using a set of 189 protein-ligand complexes is presented. The scoring functions include the CHARMm potential, the scoring function DrugScore, the scoring function used in AutoDock, the three scoring functions implemented in DOCK, as well as three scoring functions implemented in the CScore module in SYBYL (PMF, Gold, ChemScore). We evaluated the abilities of these scoring functions to recognize near-native configurations among a set of decoys and to rank binding affinities. Binding site decoys were generated by molecular dynamics with restraints. To investigate whether the scoring functions can also be applied for binding site detection, decoys on the protein surface were generated. The influence of the assignment of protonation states was probed by either assigning "standard" protonation states to binding site residues or adjusting protonation states according to experimental evidence. The role of solvation models in conjunction with CHARMm was explored in detail. These include a distance-dependent dielectric function, a generalized Born model, and the Poisson equation. We evaluated the effect of using a rigid receptor on the outcome of docking by generating all-pairs decoys ("cross-decoys") for six trypsin and seven HIV-1 protease complexes. The scoring functions perform well to discriminate near-native from misdocked conformations, with CHARMm, DOCK-energy, DrugScore, ChemScore, and AutoDock yielding recognition rates of around 80%. Significant degradation in performance is observed in going from decoy to cross-decoy recognition for CHARMm in the case of HIV-1 protease, whereas DrugScore and ChemScore, as well as CHARMm in the case of trypsin, show only small deterioration. In contrast, the prediction of binding affinities remains problematic for all of the scoring functions. ChemScore gives the highest correlation value with R(2) = 0.51 for the set of 189 complexes and R(2) = 0.43 for the set of 116 complexes that does not contain any of the complexes used to calibrate this scoring function. Neither a more accurate treatment of solvation nor a more sophisticated charge model for zinc improves the quality of the results. Improved modeling of the protonation states, however, leads to a better prediction of binding affinities in the case of the generalized Born and the Poisson continuum models used in conjunction with the CHARMm force field.
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