新编-Protein Interactions教学讲解课件.ppt

1、Lecture 4.11Protein InteractionsMichel DumontierBlueprint Initiativemjdumontblueprint.org blueprint.org Lecture 4.12OutlineMolecular interactions Discovery Experimental Computational Storage Data Mining Future DirectionsLecture 4.13Molecular Interactions Between two molecular objects DNA,RNA,gene,pr

2、otein,molecular complex,small molecule,photon Binding Sites Under an Experimental Condition With a particular Cellular Location Possibly causing some Chemical ActionBALecture 4.14Interaction DiscoveryExpression,Interaction Data,Function,Protein modificationsMicroarrayTwo-HybridMassSpectrometryGeneti

3、csLecture 4.15A measure of confidence?How do you know if the interaction really exists?Each method has its advantages and disadvantages.Be aware of systematic errors(i.e.tag effects)Be aware of contaminating proteins.Each method observes interactions from a slightly different experimental condition.

4、Support from many different sources is certainly better than just one.Lecture 4.16High-throughput Mass Spectrometric Protein Complex Identification(HMS-PCI)Ste12Ho et al.Nature.2019 Jan 10;415(6868):180-3Mike Tyers,SLRILecture 4.17Filtering Remove promiscuously binding proteins:Proteins identified b

5、y MS in control lanes Proteins that were found at a high frequency in the overall experiment Known high-abundant proteins ribosomal elementsLecture 4.19Synthetic Genetic Interactions Synthetic genetic interactions(lethal,slow growth)Mate two mutants without phenotypes to get a daughter cell with a p

6、henotype Synthetic lethal(SL),slow growth robotic mating using the yeast deletion library Genetic interactions provide functional data on protein interactions or redundant genes About 23%of known SLs(1295-YPD+MIPS)are known protein interactions in yeastTong et al.Science.2019 Dec 14;294(5550):2364-8

7、Lecture 4.110Working overtimeCharlie Boones RobotsCell PolarityCell Wall Maintenance Cell StructureMitosisChromosome StructureDNA Synthesis DNA RepairUnknownOthersSynthetic Genetic Interactions in YeastTong,BooneLecture 4.112PreBIND Literature Mining PreBIND is a data mining tool that helps research

8、ers locate biomolecular interaction information in the scientific literature.Start with a protein name or accession find abstracts with significant interaction information.Ranked list of potential interactors based on the number of high-scoring abstracts found and SVM score.Information is only avail

9、able for yeast,mouse and human proteins described by NCBI RefSeq identifiersDonaldson et al.PreBIND and Textomy-mining the biomedical literature for protein-protein interactions using a support vector machine.BMC Bioinformatics.2019 Mar 27;4(1):11.Lecture 4.113Search PreBIND with“Chk1”Lecture 4.114V

10、iew Possible InteractionsLecture 4.115View the abstractLecture 4.116Lecture 4.117Confirm and submit a BIND InteractionLecture 4.118Computational Interaction Prediction By Homology If A and B interact and C is homologous to A and D is homologous to B Do C and D interact?Transitive property They may,i

11、f C&D are from the same species unless host-pathogen interaction Binding surface is conserved note domain interactions Binding residues are conserved Localize to the same cellular compartmentLecture 4.119OutlineMolecular interactions Discovery Storage Databases File Formats Data Mining Future Direct

12、ionsLecture 4.120Information ScopeDBEvolutionary BiologyBiochemistryClinical StudiesChemistryBiophysicsBioinformaticsPharmacologyPopulation BiologyProteomicsEpidemiologyMolecular BiologyImmunologyGenomicsGeneticsLecture 4.121A free,open-source database for archiving and exchanging molecular assembly

13、 information.BIND is managed by the Blueprint Initiative at Mount Sinai Hospital in Toronto.The database contains Interactions/Reactions Molecular complexes PathwaysBIND has an extensive data model,GNU software tools and is based on the NCBI toolkit;extended recently to XML/JavaThe 97000 BIND record

14、s are curated and validated.bind.caBader GD,Betel D,Hogue CW.(2019)BIND:the Biomolecular Interaction Network Database.Nucleic Acids Res.31(1):248-50 PMID:12520193 Lecture 4.122Browse Interface(v2.5)Lecture 4.123BIND Submit Record View(v3.0)Lecture 4.124Publication LinksLecture 4.125OntoglyphsLecture

15、 4.126Gene Ontology Functional protein annotation geneontology.org Controlled vocabulary for protein function and localization Molecular function e.g.DNA helicase Biological process e.g.mitosis Cellular Component e.g.nucleusLecture 4.127BIND Interaction Viewer 3.0Lecture 4.128Database of Interacting

16、 Proteins(DIP)The DIP database catalogs experimentally determined interactions between proteins.It combines information from a variety of sources to create a single,consistent set of protein-protein interactions.The data stored within the DIP database were curated,both,manually by expert curators an

17、d also automatically using computational approaches that utilize knowledge about the protein-protein interaction networks extracted from the most reliable,core subset of the DIP data.44349 interactions from 107 organisms involving 17048 proteinsSalwinski L,Miller CS,Smith AJ,Pettit FK,Bowie JU,Eisen

18、berg D(2019)The Database of Interacting Proteins:2019 update.NAR 32 Database issue:D449-51dip.doe-mbi.ucla.eduCopyright 2019-2019 UCLAThe DIP database is the property of the Regents of the University of California.It is forbidden to redistribute,derivatize,or encapsulate the DIP in another database

19、without permission from UCLA and David Eisenberg.Lecture 4.129DIP Search InterfaceLecture 4.130MINT MINT database v3.0.MINT is a relational database designed to store interactions between biological molecules.MINT focuses on experimentally verified protein interactions with special emphasis on prote

20、omes from mammalian organisms.MINT consists of entries mined in the scientific literature by curators.The curated data can be analyzed in the context of the high throughput data and viewed graphically through the MINT Viewer.42534 Interactions with 18148 proteinsZanzoni A.,Montecchi-Palazzi L.,Quond

21、am M.,Ausiello G.,Helmer-Citterich M.and Cesareni G.MINT:a Molecular INTeraction database.(2019)FEBS Letters,513(1);135-140.mint.bio.uniroma2.it/mint Lecture 4.131MINT Record ViewLecture 4.132MINT Interaction ViewerLecture 4.133Other Interaction Databases MIPS mips.gsf.de/proj/yeast/tables/interacti

22、on/IntAct EBIs interaction database ebi.ac.uk/intact/Human Protein Interaction Database hpid.org/TRANSFAC transcription factors gene-regulation/General Repository for Interaction Sets(GRID)biodata.mshri.on.ca/grid/servlet/IndexLecture 4.134Data Exchange File Formats BIND bind.ca Peer reviewed but cl

23、osed process(Spec v3.1)ASN.1 or XML DTD/Schema PSI-MI psidev.sourceforge Peer reviewed,HUPO community standard Widely adopted BioPax biopax.org Community schema(Sloan Kettering,BioPathways Consortium)XML Schema,OWL,Protg and GKB SBML Widely adopted for representing models of biochemical reaction net

24、worksLecture 4.135BINDASN.1(text)XMLFlat FileLecture 4.136MINT PSI level 1Lecture 4.137PSI Record FormatLecture 4.138BioPAX Collaborative effort to create a data exchange format for biological pathway data biopax.org Lecture 4.139OutlineMolecular interactions Discovery Storage Data Mining Graph Theo

25、ry Comparisons Visualization Tools Future DirectionsLecture 4.140Integrated Data Mining Determine new relationships between data.Identify non-obvious patterns Statistical clustering(e.g.microarray)Graph theoryLecture 4.141Graph TheoryVertex(node)EdgeCycle-5Directed Edge(Arc)Weighted Edge710We map mo

26、lecular interaction networks to graphsLecture 4.142Useful Graph Operations A graph can be treated as a set of vertices and edges:intersection,difference,union e.g.What is the intersection of my interaction set with all known published interactions?Filtering e.g.Give me all protein interactions where

27、 at least one partner is nuclear localized Overall statistics e.g.Find the average number of interactions for cell cycle proteinsLecture 4.143k-core A part of a graph where every node is connected to other nodes with at least k edges(k=0,1,2,3.)Highest k-core is a central most densely connected regi

28、on of a graph Regions of dense connectivity may represent molecular complexes Therefore,high k-cores may be molecular complexesLecture 4.144k-coreBatagelj,V.,Mrvar,A.Lecture 4.145Spoke and Matrix Models Vrp1(bait),Las17,Rad51,Sla1,Tfp1,Ypt7SpokeMatrixSimple modelUseful for data navigationTheoretical

29、 max.number of interactionsPossible ActualTopologyLecture 4.146Graph Visualization Visualize the connectivity of a given list of interactionsAde2Rad18Sed4Ybr134wAde6Ylr386wAde8Med4Soh1Bud31Adh2Yfl042cAhc1Ycr082wAip1Apc9Alr1Gcd7Pgd1Ygl024wYnl086wApg12Aut1Apg16Apg13Apg1Pup2Sec35Apg5Apg7Aut7Apm1Apl2Apm

30、2Arh1Cdc39Gif1Arp10Arp1Atp20She2Bem4Scs3PajekLecture 4.147Graph Annotation ColouringGene Ontology Biological ProcessGO ProcessColourDNA metabolismBlueautophagyOrangecarbohydrate metabolismYellowcell cyclePurplecell organization and biogenesis Redgeneral metabolismBrownprotein biosynthesisCyanprotein

31、 degradationPinkprotein transportMaroontranscriptionGraytransportGreenUnknownBlackLecture 4.148Stand-Alone Visualization Tools Cytoscape Visualize molecular interaction networks and integrate interactions with gene expression profiles and other state data.Data filters&custom plug-in architecture.cyt

32、oscape.org/Osprey Visualization of complex interaction networks Data enriched with Gene Ontology&GRID db biodata.mshri.on.ca/osprey/servlet/Index Pajek General network analysis Used to analyze social,biological,web networks vlado.fmf.uni-lj.si/pub/networks/pajek/Lecture 4.149Compare HMS-PCI to Other

33、 Large-scale Data Sets Co-Immunoprecipitation(CoIP)data Population of complexes of unknown topology Want to compare this data to pairwise interactions Must model CoIP as pairwise interactionsLecture 4.150Literature Benchmarks Manually curated collection of published interactions(not including large-

34、scale experiments)MIPS-1353 interactions PreBIND-1196 YPD-2205 Combined,non-redundant-3310Is HMS-PCI Better?Compare to comprehensive high-throughput yeast two-hybrid(HT-Y2H)Ito et al.Uetz et al.Each set compared to a literature benchmark MIPS+PreBIND-1003 interactions with HMS-PCI baits HMS-PCI find

35、s 2-3x more benchmark interactions than HT-Y2HDatasetInteractionsHMS-PCI Spoke166HMS-PCI Matrix214Ito et al.49Uetz et al.63Ito+Uetz86Ito et al.PNAS 98 p4569 2019&Uetz et al.Nature 403 p623 2000Another Large-scale MS StudyGavin et al.Ho et al.Overall MethodTAP/MALDI-TOF MSFLAG/LC-MS/MSTag Size200 ami

36、no acids,C-terminal7 amino acids,C-terminalBait ExpressionEndogenous promoterEctopic;GAL1 or tet promoterCloningHomologousRecombination,chromosome basedGatewayTM Recombination,vectorbasedAttempted Number ofGenes Tagged1,739725Culture volume2L500mlAffinity Isolation methodTandem Affinity Purification

37、ImmunoprecipitationUnique Bait ProteinsDetected as Expressed1,167600Affinity Isolation attempts5881,558(repeated baits)Mass SpectrometryMALDI-TOF MSLC-MS/MSProtein ID MethodPeptide Fragment MassMS/MS,SequenceMS samples20,94615,683Protein Ids16,83035,000Baits with Hits454493Filtered,Unique Proteins1,

38、3631,578ContaminantFrequency Cutoff3.5%determinedempirically3.0%determined analyticallyTotal ProteinsExcluded66434Ho et al.Nature.2019 Jan 10;415(6868):180-3Gavin et al.Nature.2019 Jan 10;415(6868):141-7Ho-more sensitiveAdded info.for 446 proteinsComparing Data Sets Only 115 common baits(Ho 600;Gavi

39、n 587)Ho vs.Gavin(spoke)198 interactions,222 proteins Ho+Gavin(matrix-44,680 intx)vs.all HT-Y2H(5 data sets-5614 intx):304 interactions,388 proteins Ho+Gavin(matrix)vs.benchmark(2078 interactions involving Ho+Gavin baits):693 interactions,619 proteins-33%Conclusion:Interaction data is not saturating

40、Ho et al.Nature.2019 Jan 10;415(6868):180-3Gavin et al.Nature.2019 Jan 10;415(6868):141-7Ho vs.Gavin(spoke)Analysis of Combined Data Set Combine Ho,Gavin,previously known data-Whats new?Find protein complexes by finding k-cores MCODE software by Gary Bader&Chris Hogue Large nucleolar complex with fu

41、nctional links to RNA processing,cell cycle control Nucleolus is the ribosome factory,but recently implicated in cell cycle control,RNA transport/processingAndersen et al.Curr Biol.2019 Jan 8;12(1):1-11Pre MSHoGavinUnion6-core6-core6-core9-coreInteraction can define function Lecture 4.157OutlineMole

42、cular interactions Discovery Storage Data Mining Future DirectionsLecture 4.158Systems Biology Systems biology is the study of living organisms in terms of their underlying network structure as a function of the interactions of individual molecular components.Since a“system can be anything from a ge

43、ne regulatory network to a cell,a tissue,or an entire organism,this approach requires the use and development of novel high throughput,sensitive and reliable analytical methods for the identification and characterization of genes,and/or their products,based on function.Lecture 4.159Systems Biology C

44、urrent experimental methods such as mass spectrometry,microarray,NMR and microscopy will be instrumental in generating essential data that will require innovative computational approaches to manage and interpret the vast amounts of data required to understand complex biological systems.Lecture 4.160

45、Space and Time resolved simulationsDorsal-Ventral Patterning in the Early Drosophila EmbryoMichaelis-Menten kineticsObserve dynamics of the network behaviorFor a simple transcription factor(TF)gene interaction TF+G G*,k1=binding coefficientG*+P-G*+P+nX,k2=transcription rate coefficientwhere P repres

46、ents the RNA polymerase concentration,and X is the gene product concentration,n represents the number of X proteins per mRNA transcript The vertical scale is concentration in units of number of molecules.The horizontal coordinate is space,running from the ventral point to the dorsal point Each sampl

47、e point represents a 10 micron bin.The total time course is,in physiological time,400 secsquad.bic.caltech.edu/kastner/drosophila.html Lecture 4.161E-Cell E-Cell system is an object-oriented software suite for modeling,simulation,and analysis of large scale complex systems such as biological cellse-

48、cell.orgLecture 4.162Systems Biology WorkbenchThe Systems Biology Workbench(SBW)is a modular,broker-based,message-passing framework for simplified communication between applications that aid in research in systems biology.Support for Windows,FreeBSD and LinuxBidirectional CORBA-SBW gateway Collectio

49、n of modules provided with the base distribution:Simple stochastic simulator An SBML-to-MATLAB ODE and Simulink file translator An SBML reader module A clipboard module for exchanging SBML models A Browser that outputs module interface definitions A simple plotting module for time-series data A gene

50、ric simulation-control GUI interface sbw.sourceforge Lecture 4.163ConclusionSystems Biology Requirements:Parts List(Integrated Sequence And Structure DB)Molecular Interactions List(Biomolecular+Kinetics)Cellular Localization&Initial Concentrations Stochastic Simulation&Distributed computing infrastr