Pillardy et al., 2001 - Google Patents
Development of physics-based energy functions that predict medium-resolution structures for proteins of the α, β, and α/β structural classesPillardy et al., 2001
View PDF- Document ID
- 11734656863113505732
- Author
- Pillardy J
- Czaplewski C
- Liwo A
- Wedemeyer W
- Lee J
- Ripoll D
- Arłukowicz P
- Ołdziej S
- Arnautova Y
- Scheraga H
- Publication year
- Publication venue
- The Journal of Physical Chemistry B
External Links
Snippet
The development of three physics-based energy functions (force fields), designed to simulate the restricted free energy of proteins of the α, β, and α/β structural classes, is described. Each force field corresponds to a particular weighting of the united-residue …
- 102000004169 proteins and genes 0 title abstract description 285
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
- G06F19/10—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology
- G06F19/16—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology for molecular structure, e.g. structure alignment, structural or functional relations, protein folding, domain topologies, drug targeting using structure data, involving two-dimensional or three-dimensional structures
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
- G06F19/10—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology
- G06F19/28—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology for programming tools or database systems, e.g. ontologies, heterogeneous data integration, data warehousing or computing architectures
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
- G06F19/10—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology
- G06F19/22—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology for sequence comparison involving nucleotides or amino acids, e.g. homology search, motif or SNP [Single-Nucleotide Polymorphism] discovery or sequence alignment
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
- G06F19/10—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology
- G06F19/18—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology for functional genomics or proteomics, e.g. genotype-phenotype associations, linkage disequilibrium, population genetics, binding site identification, mutagenesis, genotyping or genome annotation, protein-protein interactions or protein-nucleic acid interactions
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
- G06F19/70—Chemoinformatics, i.e. data processing methods or systems for the retrieval, analysis, visualisation, or storage of physicochemical or structural data of chemical compounds
- G06F19/708—Chemoinformatics, i.e. data processing methods or systems for the retrieval, analysis, visualisation, or storage of physicochemical or structural data of chemical compounds for data visualisation, e.g. molecular structure representations, graphics generation, display of maps or networks or other visual representations
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
- G06F19/70—Chemoinformatics, i.e. data processing methods or systems for the retrieval, analysis, visualisation, or storage of physicochemical or structural data of chemical compounds
- G06F19/701—Chemoinformatics, i.e. data processing methods or systems for the retrieval, analysis, visualisation, or storage of physicochemical or structural data of chemical compounds for molecular modelling, e.g. calculation and theoretical details of quantum mechanics, molecular mechanics, molecular dynamics, Monte Carlo methods, conformational analysis or the like
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
- G06F19/10—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology
- G06F19/24—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology for machine learning, data mining or biostatistics, e.g. pattern finding, knowledge discovery, rule extraction, correlation, clustering or classification
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
- G06F19/70—Chemoinformatics, i.e. data processing methods or systems for the retrieval, analysis, visualisation, or storage of physicochemical or structural data of chemical compounds
- G06F19/706—Chemoinformatics, i.e. data processing methods or systems for the retrieval, analysis, visualisation, or storage of physicochemical or structural data of chemical compounds for drug design with the emphasis on a therapeutic agent, e.g. ligand-biological target interactions, pharmacophore generation
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by the preceding groups
- G01N33/48—Investigating or analysing materials by specific methods not covered by the preceding groups biological material, e.g. blood, urine; Haemocytometers
- G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
- G01N33/68—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving proteins, peptides or amino acids
- G01N33/6803—General methods of protein analysis not limited to specific proteins or families of proteins
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F9/00—Arrangements for programme control, e.g. control unit
- G06F9/06—Arrangements for programme control, e.g. control unit using stored programme, i.e. using internal store of processing equipment to receive and retain programme
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Pillardy et al. | Development of physics-based energy functions that predict medium-resolution structures for proteins of the α, β, and α/β structural classes | |
| Siebenmorgen et al. | Evaluation of predicted protein–protein complexes by binding free energy simulations | |
| Liwo et al. | Modification and optimization of the united-residue (UNRES) potential energy function for canonical simulations. I. Temperature dependence of the effective energy function and tests of the optimization method with single training proteins | |
| Vendruscolo et al. | Recovery of protein structure from contact maps | |
| Pillardy et al. | Recent improvements in prediction of protein structure by global optimization of a potential energy function | |
| Irbäck et al. | Folding thermodynamics of peptides | |
| Wang et al. | Protein–protein interaction-Gaussian accelerated molecular dynamics (PPI-GaMD): Characterization of protein binding thermodynamics and kinetics | |
| Krupa et al. | Maximum likelihood calibration of the UNRES force field for simulation of protein structure and dynamics | |
| Shehu et al. | Modeling protein conformational ensembles: from missing loops to equilibrium fluctuations | |
| Sieradzan et al. | Physics-based potentials for the coupling between backbone-and side-chain-local conformational states in the united residue (UNRES) force field for protein simulations | |
| Bolia et al. | BP-Dock: a flexible docking scheme for exploring protein–ligand interactions based on unbound structures | |
| Chys et al. | Random coordinate descent with spinor-matrices and geometric filters for efficient loop closure | |
| Zaborowski et al. | A maximum-likelihood approach to force-field calibration | |
| Lee et al. | Optimization of parameters in macromolecular potential energy functions by conformational space annealing | |
| Bhattacharya et al. | UniCon3D: de novo protein structure prediction using united-residue conformational search via stepwise, probabilistic sampling | |
| Gromiha | Importance of native-state topology for determining the folding rate of two-state proteins | |
| Wang et al. | Higher accuracy achieved in the simulations of protein structure refinement, protein folding, and intrinsically disordered proteins using polarizable force fields | |
| Suruzhon et al. | ProtoCaller: robust automation of binding free energy calculations | |
| Bæk et al. | Assessment of AlphaFold2 for human proteins via residue solvent exposure | |
| Desta et al. | Mapping of antibody epitopes based on docking and homology modeling | |
| Májek et al. | A coarse‐grained potential for fold recognition and molecular dynamics simulations of proteins | |
| Xiao et al. | Prediction enhancement of residue real-value relative accessible surface area in transmembrane helical proteins by solving the output preference problem of machine learning-based predictors | |
| Xu et al. | OPUS-Rota3: improving protein side-chain modeling by deep neural networks and ensemble methods | |
| Ji et al. | Personal precise force field for intrinsically disordered and ordered proteins based on deep learning | |
| Yang et al. | Construction of a deep neural network energy function for protein physics |