Publication:
Human cancer protein-protein interaction network: a structural perspective

dc.contributor.departmentDepartment of Computer Engineering
dc.contributor.departmentDepartment of Chemical and Biological Engineering
dc.contributor.kuauthorKar, Gözde
dc.contributor.kuauthorGürsoy, Attila
dc.contributor.kuauthorKeskin, Özlem
dc.contributor.kuprofileFaculty Member
dc.contributor.otherDepartment of Computer Engineering
dc.contributor.otherDepartment of Chemical and Biological Engineering
dc.contributor.schoolcollegeinstituteCollege of Engineering
dc.contributor.yokidN/A
dc.contributor.yokid8745
dc.contributor.yokid26605
dc.date.accessioned2024-11-09T11:49:11Z
dc.date.issued2009
dc.description.abstractProtein-protein interaction networks provide a global picture of cellular function and biological processes. Some proteins act as hub proteins, highly connected to others, whereas some others have few interactions. The dysfunction of some interactions causes many diseases, including cancer. Proteins interact through their interfaces. Therefore, studying the interface properties of cancer-related proteins will help explain their role in the interaction networks. Similar or overlapping binding sites should be used repeatedly in single interface hub proteins, making them promiscuous. Alternatively, multi-interface hub proteins make use of several distinct binding sites to bind to different partners. We propose a methodology to integrate protein interfaces into cancer interaction networks (ciSPIN, cancer structural protein interface network). The interactions in the human protein interaction network are replaced by interfaces, coming from either known or predicted complexes. We provide a detailed analysis of cancer related human protein-protein interfaces and the topological properties of the cancer network. The results reveal that cancer-related proteins have smaller, more planar, more charged and less hydrophobic binding sites than non-cancer proteins, which may indicate low affinity and high specificity of the cancer-related interactions. We also classified the genes in ciSPIN according to phenotypes. Within phenotypes, for breast cancer, colorectal cancer and leukemia, interface properties were found to be discriminating from non-cancer interfaces with an accuracy of 71%, 67%, 61%, respectively. In addition, cancer-related proteins tend to interact with their partners through distinct interfaces, corresponding mostly to multi-interface hubs, which comprise 56% of cancer-related proteins, and constituting the nodes with higher essentiality in the network (76%). We illustrate the interface related affinity properties of two cancer-related hub proteins: Erbb3, a multi interface, and Raf1, a single interface hub. The results reveal that affinity of interactions of the multi-interface hub tends to be higher than that of the single-interface hub. These findings might be important in obtaining new targets in cancer as well as finding the details of specific binding regions of putative cancer drug candidates.
dc.description.fulltextYES
dc.description.indexedbyWoS
dc.description.indexedbyScopus
dc.description.indexedbyPubMed
dc.description.issue12
dc.description.openaccessYES
dc.description.publisherscopeInternational
dc.description.sponsoredbyTubitakEuTÜBİTAK
dc.description.sponsorshipScientific and Technological Research Council of Turkey (TÜBİTAK)
dc.description.versionPublisher version
dc.description.volume5
dc.formatpdf
dc.identifier.doi10.1371/journal.pcbi.1000601
dc.identifier.eissn1553-7358
dc.identifier.embargoNO
dc.identifier.filenameinventorynoIR00817
dc.identifier.issn1553-734X
dc.identifier.linkhttps://doi.org/10.1371/journal.pcbi.1000601
dc.identifier.quartileQ1
dc.identifier.scopus2-s2.0-74549134502
dc.identifier.urihttps://hdl.handle.net/20.500.14288/636
dc.identifier.wos274229000019
dc.keywordsComputational Hot-Spots
dc.keywordsBiological Networks
dc.keywordsMolecular-Dynamics
dc.keywordsInteraction Sites
dc.keywordsInterfaces
dc.keywordsDatabase
dc.keywordsGenes
dc.keywordsIdentification
dc.keywordsPrediction
dc.keywordsComplexes
dc.languageEnglish
dc.publisherPublic Library of Science
dc.relation.grantno104T504
dc.relation.urihttp://cdm21054.contentdm.oclc.org/cdm/ref/collection/IR/id/823
dc.sourcePLOS Computational Biology
dc.subjectBiochemical research methods
dc.titleHuman cancer protein-protein interaction network: a structural perspective
dc.typeJournal Article
dspace.entity.typePublication
local.contributor.authoridN/A
local.contributor.authorid0000-0002-2297-2113
local.contributor.authorid0000-0002-4202-4049
local.contributor.kuauthorKar, Gözde
local.contributor.kuauthorGürsoy, Attila
local.contributor.kuauthorKeskin, Özlem
relation.isOrgUnitOfPublication89352e43-bf09-4ef4-82f6-6f9d0174ebae
relation.isOrgUnitOfPublicationc747a256-6e0c-4969-b1bf-3b9f2f674289
relation.isOrgUnitOfPublication.latestForDiscovery89352e43-bf09-4ef4-82f6-6f9d0174ebae

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