Publication:
Identification of computational hot spots in protein interfaces: combining solvent accessibility and inter-residue potentials improves the accuracy

dc.contributor.coauthorN/A
dc.contributor.departmentDepartment of Computer Engineering
dc.contributor.departmentDepartment of Chemical and Biological Engineering
dc.contributor.kuauthorGürsoy, Attila
dc.contributor.kuauthorKeskin, Özlem
dc.contributor.kuauthorTunçbağ, Nurcan
dc.contributor.schoolcollegeinstituteCollege of Engineering
dc.date.accessioned2024-11-09T23:38:58Z
dc.date.issued2009
dc.description.abstractMotivation: Hot spots are residues comprising only a small fraction of interfaces yet accounting for the majority of the binding energy. These residues are critical in understanding the principles of protein interactions. Experimental studies like alanine scanning mutagenesis require significant effort; therefore, there is a need for computational methods to predict hot spots in protein interfaces. Results: We present a new intuitive efficient method to determine computational hot spots based on conservation (C), solvent accessibility [accessible surface area (ASA)] and statistical pairwise residue potentials (PP) of the interface residues. Combination of these features is examined in a comprehensive way to study their effect in hot spot detection. The predicted hot spots are observed to match with the experimental hot spots with an accuracy of 70% and a precision of 64% in Alanine Scanning Energetics Database (ASEdb), and accuracy of 70% and a precision of 73% in Binding Interface Database (BID). Several machine learning methods are also applied to predict hot spots. Performance of our empirical approach exceeds learning-based methods and other existing hot spot prediction methods. Residue occlusion from solvent in the complexes and pairwise potentials are found to be the main discriminative features in hot spot prediction. Conclusion: Our empirical method is a simple approach in hot spot prediction yet with its high accuracy and computational effectiveness. We believe that this method provides insights for the researchers working on characterization of protein binding sites and design of specific therapeutic agents for protein interactions.
dc.description.indexedbyWOS
dc.description.indexedbyScopus
dc.description.indexedbyPubMed
dc.description.issue12
dc.description.openaccessYES
dc.description.sponsoredbyTubitakEuN/A
dc.description.sponsorshipTUBITAK TUBITAK ( to N. T.)
dc.description.volume25
dc.identifier.doi10.1093/bioinformatics/btp240
dc.identifier.eissn1460-2059
dc.identifier.issn1367-4803
dc.identifier.scopus2-s2.0-66349094681
dc.identifier.urihttps://doi.org/10.1093/bioinformatics/btp240
dc.identifier.urihttps://hdl.handle.net/20.500.14288/13038
dc.identifier.wos266498300054
dc.keywordsGlobular-proteins
dc.keywordsSubunit interfaces
dc.keywordsReceptor complex
dc.keywordsBinding-energy
dc.keywordsSequence
dc.keywordsPrediction
dc.keywordsConservation
dc.keywordsDatabase
dc.keywordsSites
dc.keywordsHormone
dc.language.isoeng
dc.publisherOxford Univ Press
dc.relation.ispartofBioinformatics
dc.subjectBiochemical research methods
dc.subjectBiotechnology
dc.subjectApplied microbiology
dc.subjectComputer science
dc.subjectMathematical and computational biology
dc.subjectStatistics and probability
dc.titleIdentification of computational hot spots in protein interfaces: combining solvent accessibility and inter-residue potentials improves the accuracy
dc.typeJournal Article
dspace.entity.typePublication
local.contributor.kuauthorTunçbağ, Nurcan
local.contributor.kuauthorGürsoy, Attila
local.contributor.kuauthorKeskin, Özlem
local.publication.orgunit1College of Engineering
local.publication.orgunit2Department of Chemical and Biological Engineering
local.publication.orgunit2Department of Computer Engineering
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relation.isParentOrgUnitOfPublication8e756b23-2d4a-4ce8-b1b3-62c794a8c164
relation.isParentOrgUnitOfPublication.latestForDiscovery8e756b23-2d4a-4ce8-b1b3-62c794a8c164

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