From Protein Structure to Function with Bioinformatics

I Generating and Inferring StructuresAb Initio Protein Structure PredictionIntroductionEnergy FunctionsPhysics-Based Energy FunctionsCoupling Physics-Based Potentials With Molecular Dynamics Simulations Application to Atomic-Level Structure RefinementMolecular Mechanics ApproachesKnowledge-Based Energy Function Combined with FragmentsCoupling of Contact Prediction And Ab Initio Structure PredictionConformational Search MethodsMonte Carlo SimulationsMolecular DynamicsGenetic AlgorithmMathematical OptimizationModel SelectionPhysics-Based Energy FunctionKnowledge-Based Energy FunctionSequence-Structure Compatibility FunctionClustering of Decoy StructuresRemarks and DiscussionsReferencesProtein Structures, Interactions and Function from Evolutionary CouplingsIntroductionEvolutionary Couplings from Sequence AlignmentsThe Global ModelPositional constraints from evolutionary couplingsThree-Dimensional Protein Structures from Evolutionary CouplingsTransmembrane ProteinsProtein Interactions and ComplexesConformational Plasticity and Disordered ProteinsPredicting the Effect of MutationsSummary and Future ChallengesReferencesFold RecognitionIntroductionThe Importance of Blind Trials: The CASP CompetitionAb Initio Structure Prediction Versus Homology ModellingThe Limits of Fold SpacePushing Sequence Similarity to the Limits: The Power of Evolutionary InformationThe Rise of Hidden Markov ModelsUsing Predicted Structural FeaturesHarnessing 3D Structure to Enhance RecognitionKnowledge-Based PotentialsSummaryCASP: The Great FilterThe LeadersIndividual AlgorithmsConsensus MethodsPost-processingChoosing and Combining Candidate ModelsClusteringModel Quality Assessment Programs (MQAPs)Combining Models Optimally—Multiple Template ModellingPost-processing in PracticeUse of ContactsFrom Sequence to Profiles to Contact MapsTools for Fold Recognition on the WebThe FutureReferencesComparative Protein Structure ModellingIntroductionStructure Determines FunctionSequences, Structures, Structural GenomicsApproaches to Protein Structure PredictionSteps in Comparative Protein Structure ModellingSearching for Structures Related to the Target SequenceSelecting TemplatesConsiderations in Template SelectionAdvantage of Using Multiple TemplatesSequence to Structure AlignmentTaking Advantage of Structural Information in AlignmentsModel BuildingTemplate Dependent Modelling Modelling by Assembly of Rigid BodiesTemplate Independent Modelling: Modelling Loops, InsertionsRefining ModelsHybrid Modelling of Proteins and Complexes with Experimental RestraintsModel EvaluationPerformance of Comparative ModellingAccuracy of MethodsErrors in Comparative ModelsApplications of Comparative ModellingModelling of Individual ProteinsComparative Modelling and the Protein Structure InitiativeSummaryReferencesAdvances in Computational Methods for Transmembrane Protein Structure PredictionIntroductionMembrane Protein Structural Classesa-Helical BundlesTransmembrane b-BarrelsDatabasesMultiple Sequence AlignmentsTransmembrane Protein Topology PredictionEarly a-Helical Topology Prediction ApproachesMachine Learning Approaches for a-Helical Topology PredictionSignal Peptides and Re-entrant HelicesConsensus Approaches for a-Helical Topology PredictionTransmembrane b-Barrel Topology PredictionEmpirical Approaches for b-Barrel Topology PredictionMachine Learning Approaches for b-Barrel Topology PredictionConsensus Approaches for b-Barrel Topology PredictionD Structure PredictionHomology Modelling of a-Helical Transmembrane ProteinsHomology Modelling of Transmembrane b-Barrel ProteinsDe Novo Modelling of a-Helical Transmembrane ProteinsDe Novo Modelling of Transmembrane b-BarrelsCovariation-Based ApproachesEvolutionary Covariation-Based Methods for De Novo Modelling of a-Helical Membrane ProteinsEvolutionary Covariation-Based Methods for Transmembrane b-Barrel Structure PredictionFuture DirectionsReferencesBioinformatics Approaches to the Structure and Function of Intrinsically Disordered ProteinsThe Concept of Protein DisorderSequence Features of IDPsThe Unusual Amino Acid Composition of IDPsLow Sequence Complexity and DisorderFlavours of DisorderPrediction of DisorderCharge-Hydropathy PlotPropensity-Based PredictorsPrediction Based on Simplified Biophysical ModelsMachine Learning AlgorithmsRelated Approaches for the Prediction of Protein DisorderComparison of Disorder Prediction MethodsDatabases of IDPsStructural Features of IDPsFunctional Classification of IDPsGene Ontology-Based Functional Classification of IDPsClassification of IDPs Based on Their Mechanism, of ActionEntropic ChainsFunction by Transient BindingFunctions by Permanent BindingFunctional Features of IDPsShort Linear motifsDisordered Binding Regions/Molecular Recognition FeaturesIntrinsically Disordered DomainsPrediction of the Function of IDPsPredicting Short Recognition Motifs in IDRsPrediction of Disordered Binding Regions/MoRFsCombination of Information on Sequence and Disorder: Phosphorylation Sites and CaM Binding MotifsCorrelation of Disorder Pattern and FunctionEvolution of IDPsConclusionsReferencesPrediction of Protein Aggregation and Amyloid FormationIntroductionThe Physico-chemical and Structural Basis of Protein AggregationIntrinsic Determinants of Protein AggregationExtrinsic Determinants of Protein AggregationSpecific Sequence Stretches Drive AggregationStructural Determinants of Amyloid-like AggregationPrediction of Protein Aggregation from the Primary SequencePhenomenological ApproachesStructure-Based ApproachesConsensus MethodsApplications of Sequence-Based PredictorsProteome-Wide AnalysesPrediction of in vivo Protein AggregationPrediction of Aggregation Propensity from the Tertiary StructureConcluding RemarksReferencesPrediction of Biomolecular ComplexesIntroductionDockingStep 1: SearchingStep 2: ScoringData-Driven DockingThe Challenges of Docking: Flexibility and Binding AffinityChanges upon Binding: The Flexible Docking ChallengeThe ‘Perfect’ Scoring Function and the Binding Affinity ProblemProtein-Peptide DockingPost-docking: Interface Prediction from Docking Results and Use of Docking-Derived Contacts for Clustering and RankingWeb Tools for the Post-docking ProcessingConcluding RemarksReferencesII From Structures to Functions Function Diversity Within Folds and SuperfamiliesDefining FunctionFrom Fold to FunctionDefinition of a FoldGeneral UnderstandingPractical DefinitionsParadigm ShiftPrediction of Function Using Fold RelationshipsFolds with a Single FunctionSupersitesSuperfoldsFunction Diversity Between Homologous ProteinsDefinitionsGeneral UnderstandingPractical DefinitionsEvolution of Protein SuperfamiliesFunction Divergence During Protein EvolutionFunction Diversity at the Superfamily LevelFunction Diversity Between Close HomologuesConclusionBibliographyFunction Prediction Using Patches, Pockets and Other Surface PropertiesDefinitions of Protein SurfacesSurface PatchesHydrophobic PatchesElectrostaticsSequence ConservationSurface Atom Triplet PropensitiesMultiple PropertiesPocketsGeometric Descriptions of PocketsChannels and TunnelsDistinguishing Functional PocketsPredicting Ligands for PocketsPocket MatchingDocking for Function PredictionPrediction of Catalytic ResiduesProtein-Protein InterfacesOther Specialised Binding Site PredictorsMedicinal ApplicationsConclusionsReferencesD MotifsBackground: Functional AnnotationWhat Is Function?Genomics and Functional AnnotationThe Need for Structure-Based MethodsD Motif Matching TechniquesWhat Is a 3D Motif?Historical Development of Motif Matching MethodsAlgorithmic Approaches to Motif MatchingMethods Using 3D MotifsEfficiency Considerations for 3D MotifsMethods with Nonstandard Motif InformationInterpretation of ResultsMethods for Deriving MotifsLiterature Search and Manual CurationAnnotated Sites in PDB StructuresMining for Emergent PropertiesUndirected MiningDirected MiningDirected Mining with Positive and Negative ExamplesMolecular Docking for Functional AnnotationDiscussion and ConclusionsReferencesProtein Dynamics: From Structure to FunctionMolecular Dynamics SimulationsPrinciples and ApproximationsApplicationsNuclear Transport ReceptorsLysozymeAquaporinsLimitations—Enhanced Sampling AlgorithmsReplica ExchangePrincipal Component AnalysisCollective Coordinate Sampling AlgorithmsEssential DynamicsTEE-REXApplications: Finding Transition Pathways in Adenylate KinaseMethods for Functional Mode PredictionNormal Mode AnalysisElastic Network ModelsCONCOORDApplicationsSummary and OutlookReferencesIntegrated Servers for Structure-Informed Function PredictionIntroductionThe Problem, of Predicting Function from StructureStructure-Function Prediction MethodsProKnowFold MatchingD MotifsSequence HomologySequence MotifsProtein InteractionsCombining the PredictionsPrediction SuccessProFuncProFunc’s Structure-Based MethodsFold-MatchingSurface CleftsNestsTemplate MethodsPDBsum Structural AnalysesAssessment of the Structural MethodsConclusionReferencesCase Studies: Function Predictions of Structural Genomics ResultsIntroductionFunction Prediction Case StudiesTeichman et al. (2001)Kim et al. (2003)Watson et al. (2007)Lee et al. (2011)Some Specific ExamplesAdams et al. (2007)AF0491 ProteinThe GxGYxYP FamilyCommunity AnnotationConclusionsReferencesPrediction of Protein Function from Theoretical ModelsBackgroundSuitability of Protein 3D Models for Structure-Based PredictionsSurface PropertiesFunctional SitesSpecific Binding PredictionsSmall Molecule BindingProtein-Protein InteractionsProtein Model DatabasesFunction Prediction ExamplesFold Prediction with. Fragment-Based Ab Initio ModelsFold Prediction with. Contact-Based ModelsPlasticity of Catalytic Site ResiduesPrediction of Ligand SpecificityPrediction of Cofactor Specificity Using an Entry from a Database of ModelsMutation MappingProtein ComplexesStructure Modelling of Alternatively Spliced IsoformsFrom Broad Function to Molecular DetailsConclusionsReferences
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