Genomics-aided structure prediction

dc.citation.firstpage10340en_US
dc.citation.issueNumber26en_US
dc.citation.journalTitlePNASen_US
dc.citation.lastpage10345en_US
dc.citation.volumeNumber109en_US
dc.contributor.authorSulkowska, Joanna I.en_US
dc.contributor.authorMorcos, Farucken_US
dc.contributor.authorWeigt, Martinen_US
dc.contributor.authorHwa, Terenceen_US
dc.contributor.authorOnuchic, José N.en_US
dc.contributor.funderNational Science Foundationen_US
dc.contributor.orgCenter for Theoretical Biological Physicsen_US
dc.date.accessioned2013-03-14T15:17:54Zen_US
dc.date.available2013-03-14T15:17:54Zen_US
dc.date.issued2012en_US
dc.description.abstractWe introduce a theoretical framework that exploits the everincreasing genomic sequence information for protein structure prediction. Structure-based models are modified to incorporate constraints by a large number of non-local contacts estimated from direct coupling analysis (DCA) of co-evolving genomic sequences. A simple hybrid method, called DCA-fold, integrating DCA contacts with an accurate knowledge of local information (e.g., the local secondary structure) is sufficient to fold proteins in the range of 1–3 Å resolution.en_US
dc.embargo.termsnoneen_US
dc.identifier.citationSulkowska, Joanna I., Morcos, Faruck, Weigt, Martin, et al.. "Genomics-aided structure prediction." <i>PNAS,</i> 109, no. 26 (2012) 10340-10345. http://dx.doi.org/10.1073/pnas.1207864109.en_US
dc.identifier.doihttp://dx.doi.org/10.1073/pnas.1207864109en_US
dc.identifier.grantIDPHY-0822283 (National Science Foundation)en_US
dc.identifier.grantIDNSF-MCB-1214457 (National Science Foundation)en_US
dc.identifier.urihttps://hdl.handle.net/1911/70585en_US
dc.language.isoengen_US
dc.subject.keywordprotein foldingen_US
dc.subject.keywordresidue contact predictionen_US
dc.subject.keywordcontact map estimationen_US
dc.subject.keywordresidue-residue coevolutionen_US
dc.subject.keywordstatistical potentialsen_US
dc.titleGenomics-aided structure predictionen_US
dc.typeJournal articleen_US
dc.type.dcmiTexten_US
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