FINDSITE: a combined evolution/structure-based approach to protein function prediction
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bSiteFinder, an improved protein-binding sites prediction server based on structural alignment: more accurate and less time-consumingImproving protein-ligand binding site prediction accuracy by classification of inner pocket points using local featuresPredicting Ligand Binding Sites on Protein Surfaces by 3-Dimensional Probability Density Distributions of Interacting AtomsExploring the "dark matter" of a mammalian proteome by protein structure and function modeling.Developing eThread pipeline using SAGA-pilot abstraction for large-scale structural bioinformatics.Q-Dock(LHM): Low-resolution refinement for ligand comparative modeling.The utility of geometrical and chemical restraint information extracted from predicted ligand-binding sites in protein structure refinementComprehensive structural and functional characterization of the human kinome by protein structure modeling and ligand virtual screening.Free energies for coarse-grained proteins by integrating multibody statistical contact potentials with entropies from elastic network models.How noise in force fields can affect the structural refinement of protein models?The distribution of ligand-binding pockets around protein-protein interfaces suggests a general mechanism for pocket formation.Elastic network normal modes provide a basis for protein structure refinementeThread: a highly optimized machine learning-based approach to meta-threading and the modeling of protein tertiary structures.Rampant exchange of the structure and function of extramembrane domains between membrane and water soluble proteins.Are protein-protein interfaces special regions on a protein's surface?Correlation between protein function and ligand binding profiles.PoPMuSiC 2.1: a web server for the estimation of protein stability changes upon mutation and sequence optimalityKnowledge-based annotation of small molecule binding sites in proteins.Evolution: a guide to perturb protein function and networksHigh-performance prediction of functional residues in proteins with machine learning and computed input features.Functional and structural divergence of an unusual LTR retrotransposon family in plants.FINDSITE-metal: integrating evolutionary information and machine learning for structure-based metal-binding site prediction at the proteome level.Rapid catalytic template searching as an enzyme function prediction procedure.Binding ligand prediction for proteins using partial matching of local surface patches.Biochemical functional predictions for protein structures of unknown or uncertain functionDetecting local ligand-binding site similarity in nonhomologous proteins by surface patch comparison.Normal Modes Expose Active Sites in Enzymes.Are predicted protein structures of any value for binding site prediction and virtual ligand screening?Beyond structural genomics: computational approaches for the identification of ligand binding sites in protein structures.Exploiting structural information for drug-target assessment.Computational analyses of the catalytic and heparin-binding sites and their interactions with glycosaminoglycans in glycoside hydrolase family 79 endo-β-D-glucuronidase (heparanase).Can We Rely on Computational Predictions To Correctly Identify Ligand Binding Sites on Novel Protein Drug Targets? Assessment of Binding Site Prediction Methods and a Protocol for Validation of Predicted Binding Sites.Annotating Protein Functional Residues by Coupling High-Throughput Fitness Profile and Homologous-Structure Analysis.Computational prediction of protein function based on weighted mapping of domains and GO terms.PDID: database of molecular-level putative protein-drug interactions in the structural human proteome.Computational approaches for classification and prediction of P-type ATPase substrate specificity in Arabidopsis.GeauxDock: A novel approach for mixed-resolution ligand docking using a descriptor-based force field.fpocket: online tools for protein ensemble pocket detection and tracking.Multibody coarse-grained potentials for native structure recognition and quality assessment of protein models.Toward prediction of functional protein pockets using blind docking and pocket search algorithms.
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FINDSITE: a combined evolution/structure-based approach to protein function prediction
description
article científic
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article scientifique
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articolo scientifico
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artigo científico
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bilimsel makale
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scientific article published on 26 March 2009
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vedecký článok
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vetenskaplig artikel
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videnskabelig artikel
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vědecký článek
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name
FINDSITE: a combined evolution/structure-based approach to protein function prediction
@en
FINDSITE: a combined evolution/structure-based approach to protein function prediction.
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type
label
FINDSITE: a combined evolution/structure-based approach to protein function prediction
@en
FINDSITE: a combined evolution/structure-based approach to protein function prediction.
@nl
prefLabel
FINDSITE: a combined evolution/structure-based approach to protein function prediction
@en
FINDSITE: a combined evolution/structure-based approach to protein function prediction.
@nl
P2860
P921
P356
P1476
FINDSITE: a combined evolution/structure-based approach to protein function prediction
@en
P2860
P304
P356
10.1093/BIB/BBP017
P577
2009-03-26T00:00:00Z