蛋白质相互作用的生物信息学资料教学课件

蛋白质相互作用的生物信息学蛋白质相互作用的生物信息学高友鹤高友鹤中国医学科学院中国医学科学院基础医学研究所基础医学研究所蛋白质相互作用的生物信息学蛋白质相互作用的生物信息学1.实验数据实验数据2.蛋白质相互作用数据库蛋白质相互作用数据库3.高通量实验数据的验证高通量实验数据的验证4.蛋白质相互作用网络蛋白质相互作用网络5.计算预测蛋白质相互作用计算预测蛋白质相互作用实验数据实验数据1.蛋白质相互作用的知识来源于实验。蛋白质相互作用的知识来源于实验。2.高通量地应用传统实验方法获取大量相高通量地应用传统实验方法获取大量相互作用信息。互作用信息。3.高通量的数据需要验证。高通量的数据需要验证。高通量实验方法高通量实验方法CurrOpinStructBiol2019,13:377CurrOpinStructBiol2019,13:377Yeasttwo-hybridassayBenefits:Benefits:invivo.invivo.Dontneedpureproteins.Dontneedpureproteins.DontneedAb.DontneedAb.Drawbacks:Drawbacks:onlyonly twotwo proteinsproteins areare testedtested atat a a timetime(no(nocooperativebinding);cooperativebinding);itittakestakesplaceplaceininthethenucleus,nucleus,sosomanymanyproteinsproteinsarearenotnot inin theirtheir nativenative compartment;compartment;andand itit predictspredictspossiblepossible interactions,interactions,butbut isis unrelatedunrelated toto thethephysiologicalsetting.physiologicalsetting.MassspectrometryofpurifiedcomplexesBenefits:Benefits:severalmembersofacomplexcanbeseveralmembersofacomplexcanbe tagged,givingtagged,givinganinternalcheckforconsistency;aninternalcheckforconsistency;anditdetectsanditdetects realcomplexesinphysiologicalrealcomplexesinphysiologicalsettings.settings.Drawbacks:Drawbacks:itmightitmight misssomecomplexesthatarenotpresentmisssomecomplexesthatarenotpresentunderthegivenconditions;underthegivenconditions;taggingmaydisturbcomplexformation;andlooselytaggingmaydisturbcomplexformation;andlooselyassociatedcomponentsmaybewashedoffduringassociatedcomponentsmaybewashedoffduringpurification.purification.CorrelatedmRNAexpressionBenefits:Benefits:itisaninvivotechnique,albeitanindirectone;itisaninvivotechnique,albeitanindirectone;andithasmuchbroadercoverageofcellularandithasmuchbroadercoverageofcellularconditionsthanothermethods.conditionsthanothermethods.Drawbacks:Drawbacks:itisapowerfulmethodfordiscriminatingcellstatesitisapowerfulmethodfordiscriminatingcellstatesordiseaseoutcomes,butisarelativelyinaccurateordiseaseoutcomes,butisarelativelyinaccuratepredictorofdirectphysicalinteraction;predictorofdirectphysicalinteraction;anditisverysensitivetoparameterchoicesandanditisverysensitivetoparameterchoicesandclusteringmethodsduringanalysis.clusteringmethodsduringanalysis.Geneticinteractions(syntheticlethality).Benefits:itisaninvivotechnique,albeitan indirect one;and it is amenable tounbiasedgenome-widescreens.Drawbacks:notnecessarilyphysicalinteractions蛋白质相互作用的生物信息学蛋白质相互作用的生物信息学1.实验数据实验数据2.蛋白质相互作用数据库蛋白质相互作用数据库3.高通量实验数据的验证高通量实验数据的验证4.蛋白质相互作用网络蛋白质相互作用网络5.计算预测蛋白质相互作用计算预测蛋白质相互作用蛋白质相互作用数据库蛋白质相互作用数据库CurrOpinStructBiol2019,13:377CurrOpinStructBiol2019,13:377THEDIPDATABASEDatabaseofInteractingProteinsTheDIPdatabasecatalogsexperimentallydeterminedinteractionsbetweenproteins.DIP相互作用的表达相互作用的表达 NucleicAcidsResearch,2000,28,289NucleicAcidsResearch,2000,28,289291291DIP数据库结构数据库结构 NucleicAcidsResearch,2000,28,289NucleicAcidsResearch,2000,28,289291291BIND：theBiomolecularInteractionNetworkDatabaseNucleicAcidsResearch,2019,29,242-245NucleicAcidsResearch,2019,29,242-245蛋白质相互作用的生物信息学蛋白质相互作用的生物信息学1.实验数据实验数据2.蛋白质相互作用数据库蛋白质相互作用数据库3.高通量实验数据的验证高通量实验数据的验证4.蛋白质相互作用网络蛋白质相互作用网络5.计算预测蛋白质相互作用计算预测蛋白质相互作用高通量实验数据需要验证高通量实验数据需要验证CurrOpinStructBiol2019,13:377CurrOpinStructBiol2019,13:377与可信的数据相比与可信的数据相比CurrOpinStructBiol2019,13:377CurrOpinStructBiol2019,13:377ExpressionProfileReliabilityEPRIndexExpressionProfileReliabilityIndex(EPRIndex)evaluatesthequalityofalarge-scaleprotein-proteininteractiondatasetsbycomparingtheexpressionprofileoftheinteractingdatasetwiththatofthehigh-qualitysubsetoftheDIPdatabase.高通量数据互相比高通量数据互相比CurrOpinStructBiol2019,13:377CurrOpinStructBiol2019,13:377ParalogousVerificationMethodPVMScoreTheParalogousVerification(PVM)methodjudgesaninteractionprobableiftheputativelyinteractingpairhasparalogsthatalsointeract.DomainPairVerificationDPVScoreTheDomainPairVerification(DPV)methodjudgesaninteractionprobableifpotentialdomain-domaininteractionsbetweenthepairaredeemedprobable.CorrelationdistanceNature BiotechnologyNature Biotechnology2019,22,782019,22,78蛋白质相互作用网络蛋白质相互作用网络NatureNature2019,411,41-422019,411,41-42相互作用网络的用途相互作用网络的用途Themosthighlyconnectedproteinsinthecellarethemostimportantforitssurvival.NatureNature2019,411,41-422019,411,41-42蛋白质相互作用的生物信息学蛋白质相互作用的生物信息学1.实验数据实验数据2.蛋白质相互作用数据库蛋白质相互作用数据库3.高通量实验数据的验证高通量实验数据的验证4.蛋白质相互作用网络蛋白质相互作用网络5.计算预测蛋白质相互作用计算预测蛋白质相互作用计算预测蛋白质相互作用计算预测蛋白质相互作用CurrOpinStructBiol2019,13:377CurrOpinStructBiol2019,13:377DockingNeed3DStructuresCAPRI:CriticalAssessmentofPredictedInteractions,acommunity-wideexperimentforassessingthepredictivepoweroftheseprocedures.ProteinFusionBasedon:SomepairsofinteractingproteinsBasedon:Somepairsofinteractingproteinsencodedinseparategenesinoneorganismareencodedinseparategenesinoneorganismarefusedtoproducesinglehomologousproteinsinfusedtoproducesinglehomologousproteinsinotherorganism.otherorganism.CompareE.Coliwithothergenomes:6,809CompareE.Coliwithothergenomes:6,809putativeprotein-proteininteractionsputativeprotein-proteininteractionsMarcotteEMMarcotteEMScience285,751(2019)Science285,751(2019)Compareyeastwithothers:45,502putativeCompareyeastwithothers:45,502putativeinteractionsinteractionsEnrightAJNature402,86(2019)EnrightAJNature402,86(2019)GeneClusteringBasedon:Functionalcouplinggenesareinconservedgeneclustersindifferentgenomes.Gene ClusteringGene ClusteringOverbeek R PNAS 96,2896(2019)Overbeek R PNAS 96,2896(2019)Overbeek R PNAS 96,2896(2019)Overbeek R PNAS 96,2896(2019)PhylogeneticprofilePNAS(2019)96,4285-4288ACombinedExperimentalandComputationalStrategy1)1)ScreenrandompeptidelibrariesbyphageScreenrandompeptidelibrariesbyphagedisplaytodefinetheconsensussequencesfordisplaytodefinetheconsensussequencesforpreferredligandsthatbindtoeachpreferredligandsthatbindtoeach peptidepeptiderecognitionmodule.recognitionmodule.2)Onthebasisoftheseconsensussequences,2)Onthebasisoftheseconsensussequences,computationallyderiveaprotein-proteincomputationallyderiveaprotein-proteininteractionnetworkthatlinkseachinteractionnetworkthatlinkseach peptidepeptiderecognitionmoduletoproteinscontainingarecognitionmoduletoproteinscontainingapreferredpreferred peptideligand.peptideligand.ScienceScience2019295,3212019295,3213)3)Experimentallyderiveaprotein-proteinExperimentallyderiveaprotein-proteininteractionnetworkbytestingeachpeptideinteractionnetworkbytestingeachpeptiderecognitionmoduleforassociationrecognitionmoduleforassociation toeachtoeachproteinoftheinferredproteomeintheyeastproteinoftheinferredproteomeintheyeasttwo-hybridtwo-hybrid system.system.4)Determinetheintersectionofthepredicted4)Determinetheintersectionofthepredictedandexperimentalnetworksandtestinvivotheandexperimentalnetworksandtestinvivothebiologicalrelevanceofkeybiologicalrelevanceofkey interactionswithininteractionswithinthisset.thisset.ACombinedExperimentalandComputationalStrategyScienceScience2019295,3212019295,321高友鹤高友鹤 Thank you拯畏怖汾关炉烹霉躲渠早膘岸缅兰辆坐蔬光膊列板哮瞥疹傻俘源拯割宜跟三叉神经痛-治疗三叉神经痛-治疗