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dc.contributor.authorVreven, Thom
dc.contributor.authorPierce, Brian G.
dc.contributor.authorBorrman, Tyler M.
dc.contributor.authorWeng, Zhiping
dc.date2022-08-11T08:07:58.000
dc.date.accessioned2022-08-23T15:37:55Z
dc.date.available2022-08-23T15:37:55Z
dc.date.issued2017-03-01
dc.date.submitted2017-07-12
dc.identifier.citation<p>Proteins. 2017 Mar;85(3):408-416. doi: 10.1002/prot.25186. Epub 2016 Oct 24. <a href="https://doi.org/10.1002/prot.25186">Link to article on publisher's site</a></p>
dc.identifier.issn0887-3585 (Linking)
dc.identifier.doi10.1002/prot.25186
dc.identifier.pmid27718275
dc.identifier.urihttp://hdl.handle.net/20.500.14038/25826
dc.description.abstractWe report the performance of our protein-protein docking pipeline, including the ZDOCK rigid-body docking algorithm, on 19 targets in CAPRI rounds 28-34. Following the docking step, we reranked the ZDOCK predictions using the IRAD scoring function, pruned redundant predictions, performed energy landscape analysis, and utilized our interface prediction approach RCF. In addition, we applied constraints to the search space based on biological information that we culled from the literature, which increased the chance of making a correct prediction. For all but two targets we were able to find and apply biological information and we found the information to be highly accurate, indicating that effective incorporation of biological information is an important component for protein-protein docking.
dc.language.isoen_US
dc.relation<p><a href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=pubmed&cmd=Retrieve&list_uids=27718275&dopt=Abstract">Link to Article in PubMed</a></p>
dc.relation.urlhttps://doi.org/10.1002/prot.25186
dc.subjectZRANK
dc.subjectcomplex
dc.subjectdocking
dc.subjectprotein-protein interaction
dc.subjectstructure
dc.subjectBiochemistry, Biophysics, and Structural Biology
dc.subjectBioinformatics
dc.subjectComputational Biology
dc.subjectIntegrative Biology
dc.subjectSystems Biology
dc.titlePerformance of ZDOCK and IRAD in CAPRI rounds 28-34
dc.typeJournal Article
dc.source.journaltitleProteins
dc.source.volume85
dc.source.issue3
dc.identifier.legacycoverpagehttps://escholarship.umassmed.edu/bioinformatics_pubs/118
dc.identifier.contextkey10417171
html.description.abstract<p>We report the performance of our protein-protein docking pipeline, including the ZDOCK rigid-body docking algorithm, on 19 targets in CAPRI rounds 28-34. Following the docking step, we reranked the ZDOCK predictions using the IRAD scoring function, pruned redundant predictions, performed energy landscape analysis, and utilized our interface prediction approach RCF. In addition, we applied constraints to the search space based on biological information that we culled from the literature, which increased the chance of making a correct prediction. For all but two targets we were able to find and apply biological information and we found the information to be highly accurate, indicating that effective incorporation of biological information is an important component for protein-protein docking.</p>
dc.identifier.submissionpathbioinformatics_pubs/118
dc.contributor.departmentProgram in Bioinformatics and Integrative Biology
dc.source.pages408-416


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