Publication:
Predicting protein-protein interactions on a proteome scale by matching evolutionary and structural similarities at interfaces using PRISM

dc.contributor.coauthorNussinov, Ruth
dc.contributor.departmentDepartment of Computer Engineering
dc.contributor.departmentDepartment of Chemical and Biological Engineering
dc.contributor.departmentCCBB (The Center for Computational Biology and Bioinformatics)
dc.contributor.facultymemberYes
dc.contributor.kuauthorGürsoy, Attila
dc.contributor.kuauthorKeskin, Özlem
dc.contributor.kuauthorTunçbağ, Nurcan
dc.contributor.schoolcollegeinstituteCollege of Engineering
dc.contributor.schoolcollegeinstituteResearch Center
dc.date.accessioned2024-11-09T23:10:33Z
dc.date.issued2011
dc.description.abstractPrediction of protein-protein interactions at the structural level on the proteome scale is important because it allows prediction of protein function, helps drug discovery and takes steps toward genome-wide structural systems biology. We provide a protocol (termed PRISM, protein interactions by structural matching) for large-scale prediction of protein-protein interactions and assembly of protein complex structures. The method consists of two components: rigid-body structural comparisons of target proteins to known template protein-protein interfaces and flexible refinement using a docking energy function. The PRISM rationale follows our observation that globally different protein structures can interact via similar architectural motifs. PRISM predicts binding residues by using structural similarity and evolutionary conservation of putative binding residue 'hot spots'. Ultimately, PRISM could help to construct cellular pathways and functional, proteome-scale annotation. PRISM is implemented in Python and runs in a UNIX environment. The program accepts Protein Data Bank-formatted protein structures and is available at http://prism.ccbb.ku.edu.tr/prism_protocol/.
dc.description.fulltextNo
dc.description.harvestedfromManual
dc.description.indexedbyWOS
dc.description.indexedbyScopus
dc.description.indexedbyPubMed
dc.description.openaccessYES
dc.description.peerreviewstatusN/A
dc.description.publisherscopeInternational
dc.description.readpublishN/A
dc.description.sponsoredbyTubitakEuTÜBİTAK
dc.description.sponsorshipTUBITAK[109T343, 109E207]
dc.description.sponsorshipNational Cancer Institute, US National Institutes of Health [HHSN261200800001E]
dc.description.sponsorshipNational Institutes of Health, National Cancer Institute, Center for Cancer Research
dc.description.sponsorshipTurkish Academy of Sciences (TUBA) We thank all members of the Koc University Computational Systems Biology group, especially C. Ulubas, A. Selim Aytuna and U. Ogmen (former PRISM development team). We thank former and current members of the Tel Aviv University Structural Bioinformatics group, particularly M. Shatsky (MultiProt) and E. Mashiach (FiberDock). This work has been supported by TUBITAK(Research Grant numbers: 109T343 and 109E207). This project has been funded in whole or in part with federal funds from the National Cancer Institute, US National Institutes of Health (contract number HHSN261200800001E). The content of this publication does not necessarily reflect the views or policies of the Department of Health and Human Services, nor does mention of trade names, commercial products or organizations imply endorsement by the U.S. Government. This research was supported (in part) by the Intramural Research Program of the National Institutes of Health, National Cancer Institute, Center for Cancer Research. O.K. acknowledges support from the Turkish Academy of Sciences (TUBA).
dc.description.versionN/A
dc.identifier.doi10.1038/nprot.2011.367
dc.identifier.eissn1750-2799
dc.identifier.embargoN/A
dc.identifier.grantno109T343
dc.identifier.grantno109E207
dc.identifier.issn1754-2189
dc.identifier.quartileQ1
dc.identifier.scopus2-s2.0-80052411919
dc.identifier.urihttps://doi.org/10.1038/nprot.2011.367
dc.identifier.urihttps://hdl.handle.net/20.500.14288/9482
dc.identifier.wos295362900006
dc.keywordsHot-spots
dc.keywordsFunctional coverage
dc.keywordsDrug discovery
dc.keywordsBinding-sites
dc.keywordsDocking
dc.keywordsSequence
dc.keywordsDatabase
dc.keywordsComplexes
dc.keywordsAlgorithm
dc.keywordsArchitectures
dc.language.isoeng
dc.publisherNature Publishing Group (NPG)
dc.relation.affiliationKoç University
dc.relation.collectionKoç University Institutional Repository
dc.relation.ispartofNature Protocols
dc.relation.openaccessN/A
dc.rightsN/A
dc.subjectBiochemical research methods
dc.titlePredicting protein-protein interactions on a proteome scale by matching evolutionary and structural similarities at interfaces using PRISM
dc.typeJournal Article
dspace.entity.typePublication
local.contributor.kuauthorKeskin, Özlem
local.contributor.kuauthorGürsoy, Attila
local.contributor.kuauthorTunçbağ, Nurcan
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