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dc.contributor.authorMoretti, Rocco
dc.contributor.authorFleishman, Sarel J.
dc.contributor.authorAgius, Rudi
dc.contributor.authorTorchala, Mieczyslaw
dc.contributor.authorBates, Paul A.
dc.contributor.authorKastritis, Panagiotis L.
dc.contributor.authorRodrigues, Joao P.G.L.M
dc.contributor.authorTrellet, Mikael
dc.contributor.authorBonvin, Alexandre M.J.J.
dc.contributor.authorCui, Meng
dc.contributor.authorPierce, Brian G.
dc.contributor.authorHwang, Howook
dc.contributor.authorVreven, Thom
dc.contributor.authorWeng, Zhiping
dc.contributor.authorBaker, David
dc.date2022-08-11T08:07:59.000
dc.date.accessioned2022-08-23T15:38:22Z
dc.date.available2022-08-23T15:38:22Z
dc.date.issued2013-11-01
dc.date.submitted2015-06-24
dc.identifier.citationProteins. 2013 Nov;81(11):1980-7. doi: 10.1002/prot.24356. Epub 2013 Aug 23. <a href="http://dx.doi.org/10.1002/prot.24356">Link to article on publisher's site</a>
dc.identifier.issn0887-3585 (Linking)
dc.identifier.doi10.1002/prot.24356
dc.identifier.pmid23843247
dc.identifier.urihttp://hdl.handle.net/20.500.14038/25922
dc.description<p>Full author list omitted for brevity. For the full list of authors, see article.</p>
dc.description.abstractCommunity-wide blind prediction experiments such as CAPRI and CASP provide an objective measure of the current state of predictive methodology. Here we describe a community-wide assessment of methods to predict the effects of mutations on protein-protein interactions. Twenty-two groups predicted the effects of comprehensive saturation mutagenesis for two designed influenza hemagglutinin binders and the results were compared with experimental yeast display enrichment data obtained using deep sequencing. The most successful methods explicitly considered the effects of mutation on monomer stability in addition to binding affinity, carried out explicit side-chain sampling and backbone relaxation, evaluated packing, electrostatic, and solvation effects, and correctly identified around a third of the beneficial mutations. Much room for improvement remains for even the best techniques, and large-scale fitness landscapes should continue to provide an excellent test bed for continued evaluation of both existing and new prediction methodologies.
dc.language.isoen_US
dc.relation<a href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=pubmed&cmd=Retrieve&list_uids=23843247&dopt=Abstract">Link to Article in PubMed</a>
dc.relation.urlhttp://www.ncbi.nlm.nih.gov/pmc/articles/PMC4143140/
dc.subjectAlgorithms
dc.subject*Databases, Protein
dc.subjectMutation
dc.subjectProtein Binding
dc.subject*Protein Interaction Mapping
dc.subjectBiochemistry, Biophysics, and Structural Biology
dc.subjectBioinformatics
dc.subjectComputational Biology
dc.subjectIntegrative Biology
dc.subjectStructural Biology
dc.subjectSystems Biology
dc.titleCommunity-wide evaluation of methods for predicting the effect of mutations on protein-protein interactions
dc.typeJournal Article
dc.source.journaltitleProteins
dc.source.volume81
dc.source.issue11
dc.identifier.legacycoverpagehttps://escholarship.umassmed.edu/bioinformatics_pubs/62
dc.identifier.contextkey7256021
html.description.abstract<p>Community-wide blind prediction experiments such as CAPRI and CASP provide an objective measure of the current state of predictive methodology. Here we describe a community-wide assessment of methods to predict the effects of mutations on protein-protein interactions. Twenty-two groups predicted the effects of comprehensive saturation mutagenesis for two designed influenza hemagglutinin binders and the results were compared with experimental yeast display enrichment data obtained using deep sequencing. The most successful methods explicitly considered the effects of mutation on monomer stability in addition to binding affinity, carried out explicit side-chain sampling and backbone relaxation, evaluated packing, electrostatic, and solvation effects, and correctly identified around a third of the beneficial mutations. Much room for improvement remains for even the best techniques, and large-scale fitness landscapes should continue to provide an excellent test bed for continued evaluation of both existing and new prediction methodologies.</p>
dc.identifier.submissionpathbioinformatics_pubs/62
dc.contributor.departmentProgram in Bioinformatics and Integrative Biology
dc.source.pages1980-7


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