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dc.contributor.authorVirnau, Peter
dc.contributor.authorRahi, Sahand Jamal
dc.contributor.authorMirny, Leonid A.
dc.contributor.authorKardar, Mehran
dc.date.accessioned2012-05-25T19:57:05Z
dc.date.available2012-05-25T19:57:05Z
dc.date.issued2008-10
dc.date.submitted2008-08
dc.identifier.issn0305-1048
dc.identifier.issn1362-4962
dc.identifier.urihttp://hdl.handle.net/1721.1/70954
dc.description.abstractThe binding of a transcription factor (TF) to a DNA operator site can initiate or repress the expression of a gene. Computational prediction of sites recognized by a TF has traditionally relied upon knowledge of several cognate sites, rather than an ab initio approach. Here, we examine the possibility of using structure-based energy calculations that require no knowledge of bound sites but rather start with the structure of a protein–DNA complex. We study the PurR Escherichia coli TF, and explore to which extent atomistic models of protein–DNA complexes can be used to distinguish between cognate and noncognate DNA sites. Particular emphasis is placed on systematic evaluation of this approach by comparing its performance with bioinformatic methods, by testing it against random decoys and sites of homologous TFs. We also examine a set of experimental mutations in both DNA and the protein. Using our explicit estimates of energy, we show that the specificity for PurR is dominated by direct protein–DNA interactions, and weakly influenced by bending of DNA.en_US
dc.description.sponsorshipNational Science Foundation (U.S.) (Grant DMR-08- 03315)en_US
dc.description.sponsorshipDeutsche Forschungsgemeinschaft (DFG) (Grant VI237/1)en_US
dc.description.sponsorshipNEC Research Support Funden_US
dc.description.sponsorshipNational Institutes of Health. National Centers for Biomedical Computing (Informatics for Integrating Biology and the Bedside)en_US
dc.description.sponsorshipNational Institutes of Health (U.S.) (3U54LM008748-04S1)en_US
dc.language.isoen_US
dc.publisherOxford University Press (OUP)en_US
dc.relation.isversionofhttp://dx.doi.org/10.1093/nar/gkn589en_US
dc.rightsCreative Commons Attribution Non-Commercialen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc/2.5en_US
dc.sourceOxforden_US
dc.titlePredicting transcription factor specificity with all-atom modelsen_US
dc.typeArticleen_US
dc.identifier.citationJamal Rahi, S. et al. “Predicting Transcription Factor Specificity with All-atom Models.” Nucleic Acids Research 36.19 (2008): 6209–6217. Web. 25 May 2012.en_US
dc.contributor.departmentHarvard University--MIT Division of Health Sciences and Technologyen_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Physicsen_US
dc.contributor.approverKardar, Mehran
dc.contributor.mitauthorRahi, Sahand Jamal
dc.contributor.mitauthorMirny, Leonid A.
dc.contributor.mitauthorKardar, Mehran
dc.relation.journalNucleic Acids Researchen_US
dc.eprint.versionFinal published versionen_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dspace.orderedauthorsJamal Rahi, S.; Virnau, P.; Mirny, L. A.; Kardar, M.en
dc.identifier.orcidhttps://orcid.org/0000-0002-0785-5410
dc.identifier.orcidhttps://orcid.org/0000-0002-1112-5912
mit.licensePUBLISHER_CCen_US
mit.metadata.statusComplete


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