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dc.contributor.authorLiang, Shide
dc.contributor.authorZhou, Yaoqi
dc.contributor.authorGrishin, Nick
dc.contributor.authorStandley, Daron M
dc.date.accessioned2017-05-03T15:56:07Z
dc.date.available2017-05-03T15:56:07Z
dc.date.issued2011
dc.date.modified2014-05-28T22:28:40Z
dc.identifier.issn0192-8651
dc.identifier.doi10.1002/jcc.21747
dc.identifier.urihttp://hdl.handle.net/10072/57474
dc.description.abstractWe describe the development of new force fields for protein side chain modeling called optimized side chain atomic energy (OSCAR). The distance-dependent energy functions (OSCAR-d) and side-chain dihedral angle potential energy functions were represented as power and Fourier series, respectively. The resulting 802 adjustable parameters were optimized by discriminating the native side chain conformations from non-native conformations, using a training set of 12,000 side chains for each residue type. In the course of optimization, for every residue, its side chain was replaced by varying rotamers, whereas conformations for all other residues were kept as they appeared in the crystal structure. Then, the OSCAR-d were multiplied by an orientation-dependent function to yield OSCAR-o. A total of 1087 parameters of the orientation-dependent energy functions (OSCAR-o) were optimized by maximizing the energy gap between the native conformation and subrotamers calculated as low energy by OSCARd. When OSCAR-o with optimized parameters were used to model side chain conformations simultaneously for 218 recently released protein structures, the prediction accuracies were 88.8% for v1, 79.7% for v1 1 2, 1.24 A ࠯verall root mean square deviation (RMSD), and 0.62 A ࠒMSD for core residues, respectively, compared with the next-best performing side-chain modeling program which achieved 86.6% for v1, 75.7% for v1 1 2, 1.40 A ࠯verall RMSD, and 0.86 A ࠒMSD for core residues, respectively. The continuous energy functions obtained in this study are suitable for gradient-based optimization techniques for protein structure refinement.
dc.description.peerreviewedYes
dc.description.publicationstatusYes
dc.format.extent541997 bytes
dc.format.mimetypeapplication/pdf
dc.languageEnglish
dc.language.isoeng
dc.publisherWiley Periodicals, Inc
dc.publisher.placeUnited States
dc.relation.ispartofstudentpublicationN
dc.relation.ispartofpagefrom1680
dc.relation.ispartofpageto1686
dc.relation.ispartofissue8
dc.relation.ispartofjournalJournal of Computational Chemistry
dc.relation.ispartofvolume32
dc.rights.retentionY
dc.subject.fieldofresearchPhysical chemistry
dc.subject.fieldofresearchTheoretical and computational chemistry
dc.subject.fieldofresearchNanotechnology
dc.subject.fieldofresearchcode3406
dc.subject.fieldofresearchcode3407
dc.subject.fieldofresearchcode4018
dc.titleProtein Side Chain Modeling with Orientation-Dependent Atomic Force Fields Derived by Series Expansions
dc.typeJournal article
dc.type.descriptionC1 - Articles
dc.type.codeC - Journal Articles
gro.rights.copyright© 2011 Wiley Periodicals, Inc.. This is the accepted version of the following article: Protein Side Chain Modeling with Orientation-DependentAtomic Force Fields Derived by Series Expansions, Journal of Computational Chemistry, Vol. 32(8), 2011, pp. 1680-1686, which has been published in final form at dx.doi.org/10.1002/jcc.21747.
gro.date.issued2011
gro.hasfulltextFull Text
gro.griffith.authorZhou, Yaoqi


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