蛋白质相互作用的生物信息学省公开课金奖全国赛课一等奖微课获奖课件

蛋白质相互作用生物信息学高友鹤中国医学科学院基础医学研究所1/35蛋白质相互作用生物信息学试验数据蛋白质相互作用数据库高通量试验数据验证蛋白质相互作用网络计算预测蛋白质相互作用2/35试验数据

蛋白质相互作用知识起源于试验。高通量地应用传统试验方法获取大量相互作用信息。高通量数据需要验证。3/35高通量试验方法CurrOpinStructBiol,13:3774/35Yeasttwo-hybridassay

Benefits:invivo.Don’tneedpureproteins.Don’tneedAb.Drawbacks:only

twoproteinsaretestedatatime(nocooperativebinding);ittakesplaceinthenucleus,somanyproteinsarenotintheirnativecompartment;anditpredictspossibleinteractions,butisunrelatedtothephysiologicalsetting.

5/35Massspectrometryofpurifiedcomplexes

Benefits:severalmembersofacomplexcanbe

tagged,givinganinternalcheckforconsistency;anditdetects

realcomplexesinphysiologicalsettings.Drawbacks:itmight

misssomecomplexesthatarenotpresentunderthegivenconditions;taggingmaydisturbcomplexformation;andlooselyassociatedcomponentsmaybewashedoffduringpurification.

6/35CorrelatedmRNAexpressionBenefits:itisaninvivotechnique,albeitanindirectone;andithasmuchbroadercoverageofcellularconditionsthanothermethods.Drawbacks:itisapowerfulmethodfordiscriminatingcellstatesordiseaseoutcomes,butisarelativelyinaccuratepredictorofdirectphysicalinteraction;anditisverysensitivetoparameterchoicesandclusteringmethodsduringanalysis.7/35Geneticinteractions(syntheticlethality).Benefits:itisaninvivotechnique,albeitanindirectone;anditisamenableto

unbiasedgenome-widescreens.Drawbacks:notnecessarilyphysicalinteractions8/35蛋白质相互作用生物信息学试验数据蛋白质相互作用数据库高通量试验数据验证蛋白质相互作用网络计算预测蛋白质相互作用9/35蛋白质相互作用数据库

CurrOpinStructBiol,13:37710/35THEDIPDATABASEDatabaseofInteractingProteinsTheDIPdatabasecatalogsexperimentallydeterminedinteractionsbetweenproteins.11/35DIP相互作用表示

NucleicAcidsResearch,,28,289－29112/35DIP数据库结构

NucleicAcidsResearch,,28,289－29113/35BIND：theBiomolecularInteractionNetworkDatabaseNucleicAcidsResearch,,29,242-24514/35蛋白质相互作用生物信息学试验数据蛋白质相互作用数据库高通量试验数据验证蛋白质相互作用网络计算预测蛋白质相互作用15/35高通量试验数据需要验证

CurrOpinStructBiol,13:37716/35与可信数据相比CurrOpinStructBiol,13:37717/35ExpressionProfileReliabilityEPRIndexExpressionProfileReliabilityIndex(EPRIndex)evaluatesthequalityofalarge-scaleprotein-proteininteractiondatasetsbycomparingtheexpressionprofileoftheinteractingdatasetwiththatofthehigh-qualitysubsetoftheDIPdatabase.18/35高通量数据相互比CurrOpinStructBiol,13:37719/35ParalogousVerificationMethodPVMScoreTheParalogousVerification(PVM)methodjudgesaninteractionprobableiftheputativelyinteractingpairhasparalogsthatalsointeract.20/35DomainPairVerificationDPVScoreTheDomainPairVerification(DPV)methodjudgesaninteractionprobableifpotentialdomain-domaininteractionsbetweenthepairaredeemedprobable.21/35CorrelationdistanceNatureBiotechnology

,22,7822/35蛋白质相互作用网络Nature,411,41-4223/35相互作用网络用途Themosthighlyconnectedproteinsinthecellarethemostimportantforitssurvival.

Nature,411,41-4224/35蛋白质相互作用生物信息学试验数据蛋白质相互作用数据库高通量试验数据验证蛋白质相互作用网络计算预测蛋白质相互作用25/35计算预测蛋白质相互作用CurrOpinStructBiol,13:37726/35DockingNeed3DStructuresCAPRI:CriticalAssessmentofPredictedInteractions,acommunity-wideexperimentforassessingthepredictivepoweroftheseprocedures.27/35ProteinFusionBasedon:Somepairsofinteractingproteinsencodedinseparategenesinoneorganismarefusedtoproducesinglehomologousproteinsinotherorganism.CompareE.Coliwithothergenomes:6,809putativeprotein-proteininteractionsMarcotteEMScience285,751(1999)Compareyeastwithothers:45,502putativeinteractionsEnrightAJNature402,86(1999)28/35GeneClusteringBasedon:Functionalcouplinggenesareinconservedgeneclustersindifferentgenomes.29/35GeneClusteringOverbeekRPNAS96,2896(1999)30/35OverbeekRPNAS96,2896(1999)31/35PhylogeneticprofilePNAS(1999)96,4285-428832/35ACombinedExperimentalandComputationalStrategy1)Screenrandompeptidelibrariesbyphagedisplaytodefinetheconsensussequencesforpreferredligandsthatbindtoeach

peptiderecognitionmodule.2)Onthebasisoftheseconsensussequences,computationallyderiveaprotein-proteininteractionnetworkthatlinkseach

peptiderecognitionmoduletoproteinscontainingapreferred

peptideligand.Science295,32133/353)Experimentallyderiveaprotein-proteininteractionnetworkbytestingeachpeptiderecognitionmoduleforassociation

toeachproteinoftheinferredproteomeintheyeasttwo-hybrid

system.4