蛋白质相互作用的生物信息学精选PPT.ppt

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1、蛋白质相互作用的生物信息学第1页，此课件共35页哦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第2页，此课件共35页哦实验数据实验数据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。第3页，此课件共35页哦高通量实验方法高通量实验方

2、法CurrOpinStructBiol2003,13:377CurrOpinStructBiol2003,13:377第4页，此课件共35页哦Yeasttwo-hybridassayBenefits:Benefits:invivo.invivo.Dontneedpureproteins.Dontneedpureproteins.DontneedAb.DontneedAb.Drawbacks:Drawbacks:onlyonly twotwo proteinsproteins areare testedtested atat a a timetime(no(no cooperativecoope

3、rativebinding);binding);itit takestakesplaceplace inin thethe nucleus,nucleus,sosomanymanyproteinsproteins areare notnotinintheirtheir nativenative compartment;compartment;andand itit predictspredicts possiblepossibleinteractions,butisunrelatedtothephysiologicalsetting.interactions,butisunrelatedtot

4、hephysiologicalsetting.第5页，此课件共35页哦Massspectrometryofpurifiedcomplexes Benefits:Benefits:severalmembersofacomplexcanbeseveralmembersofacomplexcanbe tagged,givingantagged,givinganinternalcheckforconsistency;internalcheckforconsistency;anditdetectsanditdetects realcomplexesinphysiologicalsettings.real

5、complexesinphysiologicalsettings.Drawbacks:Drawbacks:itmightitmight misssomecomplexesthatarenotpresentunderthemisssomecomplexesthatarenotpresentunderthegivenconditions;givenconditions;taggingmaydisturbcomplexformation;andlooselytaggingmaydisturbcomplexformation;andlooselyassociatedcomponentsmaybewas

6、hedoffduringassociatedcomponentsmaybewashedoffduringpurification.purification.第6页，此课件共35页哦CorrelatedmRNAexpression Benefits:Benefits:itisaninvivotechnique,albeitanindirectone;itisaninvivotechnique,albeitanindirectone;andithasmuchbroadercoverageofcellularconditionsandithasmuchbroadercoverageofcellula

7、rconditionsthanothermethods.thanothermethods.Drawbacks:Drawbacks:itisapowerfulmethodfordiscriminatingcellstatesordiseaseitisapowerfulmethodfordiscriminatingcellstatesordiseaseoutcomes,butisarelativelyinaccuratepredictorofdirectoutcomes,butisarelativelyinaccuratepredictorofdirectphysicalinteraction;p

8、hysicalinteraction;anditisverysensitivetoparameterchoicesandclusteringanditisverysensitivetoparameterchoicesandclusteringmethodsduringanalysis.methodsduringanalysis.第7页，此课件共35页哦Geneticinteractions(syntheticlethality).Benefits:itisaninvivotechnique,albeitan indirect one;and it is amenable tounbiasedg

9、enome-widescreens.Drawbacks:notnecessarilyphysicalinteractions第8页，此课件共35页哦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第9页，此课件共35页哦蛋白质相互作用数据库蛋白质相互作用数据库CurrOpinStructBiol2003,13:377CurrOpinStructBiol2003,13:377第10页，此课件共35页

10、哦THEDIPDATABASEDatabaseofInteractingProteinsTheDIPdatabasecatalogsexperimentallydeterminedinteractionsbetweenproteins.第11页，此课件共35页哦DIP相互作用的表达相互作用的表达 NucleicAcidsResearch,2000,28,289NucleicAcidsResearch,2000,28,289291291第12页，此课件共35页哦DIP数据库结构数据库结构 NucleicAcidsResearch,2000,28,289NucleicAcidsResearch,2

11、000,28,289 291291第13页，此课件共35页哦BIND：theBiomolecularInteractionNetworkDatabaseNucleicAcidsResearch,2001,29,242-245NucleicAcidsResearch,2001,29,242-245第14页，此课件共35页哦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第15页，此课件共35页哦高通

12、量实验数据需要验证高通量实验数据需要验证CurrOpinStructBiol2003,13:377CurrOpinStructBiol2003,13:377第16页，此课件共35页哦与可信的数据相比与可信的数据相比CurrOpinStructBiol2003,13:377CurrOpinStructBiol2003,13:377第17页，此课件共35页哦ExpressionProfileReliabilityEPRIndexExpressionProfileReliabilityIndex(EPRIndex)evaluatesthequalityofalarge-scaleprotein-pr

13、oteininteractiondatasetsbycomparingtheexpressionprofileoftheinteractingdatasetwiththatofthehigh-qualitysubsetoftheDIPdatabase.第18页，此课件共35页哦高通量数据互相比高通量数据互相比CurrOpinStructBiol2003,13:377CurrOpinStructBiol2003,13:377第19页，此课件共35页哦ParalogousVerificationMethodPVMScoreTheParalogousVerification(PVM)methodju

14、dgesaninteractionprobableiftheputativelyinteractingpairhasparalogsthatalsointeract.第20页，此课件共35页哦DomainPairVerificationDPVScoreTheDomainPairVerification(DPV)methodjudgesaninteractionprobableifpotentialdomain-domaininteractionsbetweenthepairaredeemedprobable.第21页，此课件共35页哦CorrelationdistanceNature Biot

15、echnologyNature Biotechnology2003,22,782003,22,78第22页，此课件共35页哦蛋白质相互作用网络蛋白质相互作用网络NatureNature2001,411,41-422001,411,41-42第23页，此课件共35页哦相互作用网络的用途相互作用网络的用途Themosthighlyconnectedproteinsinthecellarethemostimportantforitssurvival.NatureNature2001,411,41-422001,411,41-42第24页，此课件共35页哦蛋白质相互作用的生物信息学蛋白质相互作用的生物

16、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第25页，此课件共35页哦计算预测蛋白质相互作用计算预测蛋白质相互作用CurrOpinStructBiol2003,13:377CurrOpinStructBiol2003,13:377第26页，此课件共35页哦DockingNeed3DStructuresCAPRI:CriticalAssessmentofPredictedInteractions,acommunity-wideexperim

17、entforassessingthepredictivepoweroftheseprocedures.第27页，此课件共35页哦ProteinFusion Basedon:SomepairsofinteractingproteinsBasedon:Somepairsofinteractingproteinsencodedinseparategenesinoneorganismareencodedinseparategenesinoneorganismarefusedtoproducesinglehomologousproteinsinfusedtoproducesinglehomologous

18、proteinsinotherorganism.otherorganism.CompareE.Coliwithothergenomes:6,809CompareE.Coliwithothergenomes:6,809putativeprotein-proteininteractionsputativeprotein-proteininteractionsMarcotteEMScienceMarcotteEMScience285,751(1999)285,751(1999)Compareyeastwithothers:45,502putativeCompareyeastwithothers:45

19、,502putativeinteractionsinteractionsEnrightAJNature402,86(1999)EnrightAJNature402,86(1999)第28页，此课件共35页哦GeneClusteringBasedon:Functionalcouplinggenesareinconservedgeneclustersindifferentgenomes.第29页，此课件共35页哦Gene ClusteringGene ClusteringOverbeek R PNAS 96,2896(1999)Overbeek R PNAS 96,2896(1999)第30页，此

20、课件共35页哦Overbeek R PNAS 96,2896(1999)Overbeek R PNAS 96,2896(1999)第31页，此课件共35页哦PhylogeneticprofilePNAS(1999)96,4285-4288第32页，此课件共35页哦ACombinedExperimentalandComputationalStrategy 1)1)ScreenrandompeptidelibrariesbyphagedisplaytoScreenrandompeptidelibrariesbyphagedisplaytodefinetheconsensussequencesfor

21、preferredligandsdefinetheconsensussequencesforpreferredligandsthatbindtoeachthatbindtoeach peptiderecognitionmodule.peptiderecognitionmodule.2)Onthebasisoftheseconsensussequences,2)Onthebasisoftheseconsensussequences,computationallyderiveaprotein-proteincomputationallyderiveaprotein-proteininteracti

22、onnetworkthatlinkseachinteractionnetworkthatlinkseach peptidepeptiderecognitionmoduletoproteinscontainingapreferredrecognitionmoduletoproteinscontainingapreferred peptideligand.peptideligand.ScienceScience2002295,3212002295,321第33页，此课件共35页哦 3)3)Experimentallyderiveaprotein-proteinExperimentallyderiv

23、eaprotein-proteininteractionnetworkbytestingeachpeptideinteractionnetworkbytestingeachpeptiderecognitionmoduleforassociationrecognitionmoduleforassociation toeachproteintoeachproteinoftheinferredproteomeintheyeasttwo-hybridoftheinferredproteomeintheyeasttwo-hybrid system.system.4)Determinetheinterse

24、ctionofthepredictedand4)Determinetheintersectionofthepredictedandexperimentalnetworksandtestinvivothebiologicalexperimentalnetworksandtestinvivothebiologicalrelevanceofkeyrelevanceofkey interactionswithinthisset.interactionswithinthisset.ACombinedExperimentalandComputationalStrategyScienceScience2002295,3212002295,321第34页，此课件共35页哦高友鹤高友鹤第35页，此课件共35页哦