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Lecture 4.1,1,Protein Interactions,Michel Dumontier Blueprint Initiative ,Lecture 4.1,2,Outline,Molecular interactions Discovery Experimental Computational Storage Data Mining Future Directions,Lecture 4.1,3,Molecular Interactions,Between two molecular objects DNA, RNA, gene, protein, molecular complex, small molecule, photon Binding Sites Under an Experimental Condition With a particular Cellular Location Possibly causing some Chemical Action,B,A,Lecture 4.1,4,Interaction Discovery,Expression, Interaction Data, Function, Protein modifications,Lecture 4.1,5,A 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. Support from many different sources is certainly better than just one.,Lecture 4.1,6,High-throughput Mass Spectrometric Protein Complex Identification (HMS-PCI),Ste12,Ho et al. Nature. 2002 Jan 10;415(6868):180-3,Mike Tyers, SLRI,Lecture 4.1,7,Filtering,Remove promiscuously binding proteins: Proteins identified by MS in control lanes Proteins that were found at a high frequency in the overall experiment Known high-abundant proteins ribosomal elements,Lecture 4.1,9,Synthetic Genetic Interactions,Synthetic genetic interactions (lethal, slow growth) Mate two mutants without phenotypes to get a daughter cell with a phenotype 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 yeast,Tong et al. Science. 2001 Dec 14;294(5550):2364-8,Lecture 4.1,10,Working overtime Charlie Boones Robots,Synthetic Genetic Interactions in Yeast,Tong, Boone,Lecture 4.1,12,PreBIND Literature Mining,PreBIND is a data mining tool that helps researchers 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 available for yeast, mouse and human proteins described by NCBI RefSeq identifiers,Donaldson et al. PreBIND and Textomy-mining the biomedical literature for protein-protein interactions using a support vector machine. BMC Bioinformatics. 2003 Mar 27;4(1):11.,Lecture 4.1,13,Search PreBIND with “Chk1”,Lecture 4.1,14,View Possible Interactions,Lecture 4.1,15,View the abstract,Lecture 4.1,16,Lecture 4.1,17,Confirm and submit a BIND Interaction,Lecture 4.1,18,Computational 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, if 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 compartment,Lecture 4.1,19,Outline,Molecular interactions Discovery Storage Databases File Formats Data Mining Future Directions,Lecture 4.1,20,Information Scope,Lecture 4.1,21,A free, open-source database for archiving and exchanging molecular assembly information. BIND is managed by the Blueprint Initiative at Mount Sinai Hospital in Toronto. The database contains Interactions/Reactions Molecular complexes Pathways BIND has an extensive data model, GNU software tools and is based on the NCBI toolkit; extended recently to XML/Java The 97000 BIND records are curated and validated.,http:/bind.ca,Bader GD, Betel D, Hogue CW. (2003) BIND: the Biomolecular Interaction Network Database. Nucleic Acids Res. 31(1):248-50 PMID: 12519993,Lecture 4.1,22,Browse Interface (v2.5),Lecture 4.1,23,BIND Submit Record View (v3.0),Lecture 4.1,24,Publication Links,Lecture 4.1,25,Ontoglyphs,Lecture 4.1,26,Gene Ontology,Functional protein annotation Controlled vocabulary for protein function and localization Molecular function e.g. DNA helicase Biological process e.g. mitosis Cellular Component e.g. nucleus,Lecture 4.1,27,BIND Interaction Viewer 3.0,Lecture 4.1,28,Database of Interacting 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 and 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 proteins,Salwinski L, Miller CS, Smith AJ, Pettit FK, Bowie JU, Eisenberg D (2004) The Database of Interacting Proteins: 2004 update. NAR 32 Database issue:D449-51,,Lecture 4.1,29,DIP Search Interface,Lecture 4.1,30,MINT,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 proteomes 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 proteins,Zanzoni A., Montecchi-Palazzi L., Quondam M., Ausiello G., Helmer-Citterich M. and Cesareni G. MINT: a Molecular INTeraction database. (2002) FEBS Letters, 513(1);135-140.,http:/mint.bio.uniroma2.it/mint,Lecture 4.1,31,MINT Record View,Lecture 4.1,32,MINT Interaction Viewer,Lecture 4.1,33,Other Interaction Databases,MIPS http:/mips.gsf.de/proj/yeast/tables/interaction/ IntAct EBIs interaction database http:/www.ebi.ac.uk/intact/ Human Protein Interaction Database / TRANSFAC transcription factors / General Repository for Interaction Sets (GRID) http:/biodata.mshri.on.ca/grid/servlet/Index,Lecture 4.1,34,Data Exchange File Formats,BIND http:/bind.ca Peer reviewed but closed process (Spec v3.1) ASN.1 or XML DTD/Schema PSI-MI Peer reviewed, HUPO community standard Widely adopted BioPax Community schema (Sloan Kettering, BioPathways Consortium) XML Schema, OWL, Protg and GKB SBML Widely adopted for representing models of biochemical reaction networks,Lecture 4.1,35,BIND,ASN.1 (text),XML,Flat File,Lecture 4.1,36,MINT PSI level 1,Lecture 4.1,37,PSI Record Format,Lecture 4.1,38,BioPAX,Collaborative effort to create a data exchange format for biological pathway data,,Lecture 4.1,39,Outline,Molecular interactions Discovery Storage Data Mining Graph Theory Comparisons Visualization Tools Future Directions,Lecture 4.1,40,Integrated Data Mining,Determine new relationships between data. Identify non-obvious patterns Statistical clustering (e.g. microarray) Graph theory,Lecture 4.1,41,Graph Theory,Vertex (node),Edge,Cycle,-5,Directed Edge (Arc),Weighted Edge,7,10,We map molecular interaction networks to graphs,Lecture 4.1,42,Useful 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 at least one partner is nuclear localized Overall statistics e.g. Find the average number of interactions for cell cycle proteins,Lecture 4.1,43,k-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 region of a graph Regions of dense connectivity may represent molecular complexes Therefore, high k-cores may be molecular complexes,Lecture 4.1,44,k-core,Batagelj,V., Mrvar, A.,Lecture 4.1,45,Spoke and Matrix Models,Vrp1 (bait), Las17, Rad51, Sla1, Tfp1, Ypt7,Spoke,Matrix,Simple model Useful for data navigation,Theoretical max. number of interactions,Possible Actual Topology,Lecture 4.1,46,Graph Visualization,Visualize the connectivity of a given list of interactions,Lecture 4.1,47,Graph Annotation Colouring,Gene Ontology Biological Process,Lecture 4.1,48,Stand-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. / Osprey Visualization of complex interaction networks Data enriched with Gene Ontology & GRID db http:/biodata.mshri.on.ca/osprey/servlet/Index Pajek General network analysis Used to analyze social, biological, web networks http:/vlado.fmf.uni-lj.si/pub/networks/pajek/,Lecture 4.1,49,Compare HMS-PCI to Other 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 interactions,Lecture 4.1,50,Literature Benchmarks,Manually curated collection of published interactions (not including large-scale experiments) MIPS - 1353 interactions PreBIND - 1196 YPD - 2205 Combined, non-redundant - 3310,Is 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 finds 2-3x more benchmark interactions than HT-Y2H,Ito et al. PNAS 98 p4569 2001 & Uetz et al. Nature 403 p623 2000,Another Large-scale MS Study,Ho et al. Nature. 2002 Jan 10;415(6868):180-3 Gavin et al. Nature. 2002 Jan 10;415(6868):141-7,Ho - more sensitive Added info. for 446 proteins,Comparing Data Sets,Only 115 common baits (Ho 600; Gavin 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,Ho et al. Nature. 2002 Jan 10;415(6868):180-3 Gavin et al. Nature. 2002 Jan 10;415(6868):141-7,Ho 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 functional links to RNA processing, cell cycle control Nucleolus is the ribosome factory, but recently implicated in cell cycle control, RNA transport/processing,Andersen et al. Curr Biol. 2002 Jan 8;12(1):1-11,Pre MS,Ho,Gavin,Union,6-core,6-core,6-core,9-core,Interaction can define function,Lecture 4.1,57,Outline,Molecular interactions Discovery Storage Data Mining Future Directions,Lecture 4.1,58,Systems 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 gene 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 4.1,59,Systems Biology,Current 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 4.1,60,Space and Time resolved simulations,Dorsal-Ventral Patterning in the Early Drosophila Embryo Michaelis-Menten kinetics Observe dynamics of the network behavior,For a simple transcription factor(TF) gene interaction TF + G G*, k1 = binding coefficient G* + P G* + P + nX, k2 = transcription rate coefficient where P represents 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 sample point represents a 10 m