Handle URI:
http://hdl.handle.net/10754/334644
Title:
A protein-dependent side-chain rotamer library.
Authors:
Bhuyan, M.S.; Gao, Xin ( 0000-0002-7108-3574 )
Abstract:
Protein side-chain packing problem has remained one of the key open problems in bioinformatics. The three main components of protein side-chain prediction methods are a rotamer library, an energy function and a search algorithm. Rotamer libraries summarize the existing knowledge of the experimentally determined structures quantitatively. Depending on how much contextual information is encoded, there are backbone-independent rotamer libraries and backbone-dependent rotamer libraries. Backbone-independent libraries only encode sequential information, whereas backbone-dependent libraries encode both sequential and locally structural information. However, side-chain conformations are determined by spatially local information, rather than sequentially local information. Since in the side-chain prediction problem, the backbone structure is given, spatially local information should ideally be encoded into the rotamer libraries. In this paper, we propose a new type of backbone-dependent rotamer library, which encodes structural information of all the spatially neighboring residues. We call it protein-dependent rotamer libraries. Given any rotamer library and a protein backbone structure, we first model the protein structure as a Markov random field. Then the marginal distributions are estimated by the inference algorithms, without doing global optimization or search. The rotamers from the given library are then re-ranked and associated with the updated probabilities. Experimental results demonstrate that the proposed protein-dependent libraries significantly outperform the widely used backbone-dependent libraries in terms of the side-chain prediction accuracy and the rotamer ranking ability. Furthermore, without global optimization/search, the side-chain prediction power of the protein-dependent library is still comparable to the global-search-based side-chain prediction methods.
KAUST Department:
Computer Science Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Publisher:
BioMed Central
Journal:
BMC bioinformatics
Issue Date:
14-Dec-2011
DOI:
10.1186/1471-2105-12-S14-S10
PubMed ID:
22373394
PubMed Central ID:
PMC3287466
Type:
Article
ISSN:
1471-2105
Appears in Collections:
Articles; Computer Science Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorBhuyan, M.S.en
dc.contributor.authorGao, Xinen
dc.date.accessioned2014-11-11T14:33:24Z-
dc.date.available2014-11-11T14:33:24Z-
dc.date.issued2011-12-14en
dc.identifier.issn1471-2105en
dc.identifier.pmid22373394en
dc.identifier.doi10.1186/1471-2105-12-S14-S10en
dc.identifier.urihttp://hdl.handle.net/10754/334644en
dc.description.abstractProtein side-chain packing problem has remained one of the key open problems in bioinformatics. The three main components of protein side-chain prediction methods are a rotamer library, an energy function and a search algorithm. Rotamer libraries summarize the existing knowledge of the experimentally determined structures quantitatively. Depending on how much contextual information is encoded, there are backbone-independent rotamer libraries and backbone-dependent rotamer libraries. Backbone-independent libraries only encode sequential information, whereas backbone-dependent libraries encode both sequential and locally structural information. However, side-chain conformations are determined by spatially local information, rather than sequentially local information. Since in the side-chain prediction problem, the backbone structure is given, spatially local information should ideally be encoded into the rotamer libraries. In this paper, we propose a new type of backbone-dependent rotamer library, which encodes structural information of all the spatially neighboring residues. We call it protein-dependent rotamer libraries. Given any rotamer library and a protein backbone structure, we first model the protein structure as a Markov random field. Then the marginal distributions are estimated by the inference algorithms, without doing global optimization or search. The rotamers from the given library are then re-ranked and associated with the updated probabilities. Experimental results demonstrate that the proposed protein-dependent libraries significantly outperform the widely used backbone-dependent libraries in terms of the side-chain prediction accuracy and the rotamer ranking ability. Furthermore, without global optimization/search, the side-chain prediction power of the protein-dependent library is still comparable to the global-search-based side-chain prediction methods.en
dc.language.isoenen
dc.publisherBioMed Centralen
dc.rightsThis is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.en
dc.rightsArchived with thanks to BMC bioinformaticsen
dc.rights.urihttp://creativecommons.org/licenses/by/2.0/en
dc.subjectproteinen
dc.subjectalgorithmen
dc.subjectbiologyen
dc.subjectchemical structureen
dc.subjectchemistryen
dc.subjectmethodologyen
dc.subjectpeptide libraryen
dc.subjectprobabilityen
dc.subjectprotein conformationen
dc.subjectAlgorithmsen
dc.subjectComputational Biologyen
dc.subjectModels, Molecularen
dc.subjectPeptide Libraryen
dc.subjectProbabilityen
dc.subjectProtein Conformationen
dc.subjectProteinsen
dc.titleA protein-dependent side-chain rotamer library.en
dc.typeArticleen
dc.contributor.departmentComputer Science Programen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.identifier.journalBMC bioinformaticsen
dc.identifier.pmcidPMC3287466en
dc.eprint.versionPublisher's Version/PDFen
dc.contributor.affiliationKing Abdullah University of Science and Technology (KAUST)en
kaust.authorBhuyan, Sharifulislamen
This item is licensed under a Creative Commons License
Creative Commons
All Items in KAUST are protected by copyright, with all rights reserved, unless otherwise indicated.