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dc.contributor.authorDaniels, N. M.
dc.contributor.authorCowen, L. J.
dc.contributor.authorHosur, Raghavendra
dc.contributor.authorBerger Leighton, Bonnie
dc.date.accessioned2017-06-22T19:49:57Z
dc.date.available2017-06-22T19:49:57Z
dc.date.issued2012-03
dc.identifier.issn1367-4803
dc.identifier.issn1460-2059
dc.identifier.issn1460-2059
dc.identifier.urihttp://hdl.handle.net/1721.1/110175
dc.description.abstractMotivation: One of the most successful methods to date for recognizing protein sequences that are evolutionarily related has been profile hidden Markov models (HMMs). However, these models do not capture pairwise statistical preferences of residues that are hydrogen bonded in beta sheets. These dependencies have been partially captured in the HMM setting by simulated evolution in the training phase and can be fully captured by Markov random fields (MRFs). However, the MRFs can be computationally prohibitive when beta strands are interleaved in complex topologies. We introduce SMURFLite, a method that combines both simplified MRFs and simulated evolution to substantially improve remote homology detection for beta structures. Unlike previous MRF-based methods, SMURFLite is computationally feasible on any beta-structural motif. Results: We test SMURFLite on all propeller and barrel folds in the mainly-beta class of the SCOP hierarchy in stringent cross-validation experiments. We show a mean 26% (median 16%) improvement in area under curve (AUC) for beta-structural motif recognition as compared with HMMER (a well-known HMM method) and a mean 33% (median 19%) improvement as compared with RAPTOR (a well-known threading method) and even a mean 18% (median 10%) improvement in AUC over HHPred (a profile–profile HMM method), despite HHpred's use of extensive additional training data. We demonstrate SMURFLite's ability to scale to whole genomes by running a SMURFLite library of 207 beta-structural SCOP superfamilies against the entire genome of Thermotoga maritima, and make over a 100 new fold predictions.en_US
dc.description.sponsorshipNational Institutes of Health (U.S.) (grant 1R01GM08187)en_US
dc.language.isoen_US
dc.publisherOxford University Pressen_US
dc.relation.isversionofhttp://dx.doi.org/10.1093/bioinformatics/bts110en_US
dc.rightsCreative Commons Attribution-NonCommercial 3.0 Unported licenceen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc/3.0/en_US
dc.sourceOxford University Pressen_US
dc.titleSMURFLite: combining simplified Markov random fields with simulated evolution improves remote homology detection for beta-structural proteins into the twilight zoneen_US
dc.typeArticleen_US
dc.identifier.citationDaniels, N. M., R. Hosur, B. Berger, and L. J. Cowen. “SMURFLite: Combining Simplified Markov Random Fields with Simulated Evolution Improves Remote Homology Detection for Beta-Structural Proteins into the Twilight Zone.” Bioinformatics 28, no. 9 (March 9, 2012): 1216–1222.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratoryen_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Scienceen_US
dc.audience.educationlevel
dc.contributor.mitauthorHosur, Raghavendra
dc.contributor.mitauthorBerger Leighton, Bonnie
dc.relation.journalBioinformaticsen_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.orderedauthorsDaniels, N. M.; Hosur, R.; Berger, B.; Cowen, L. J.en_US
dspace.embargo.termsNen_US
dspace.mitauthor.errortrue
mit.licensePUBLISHER_CCen_US


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