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Structure prediction using sparse simulated NOE restraints with Rosetta in CASP11.
Ovchinnikov, Sergey; Park, Hahnbeom; Kim, David E; Liu, Yuan; Wang, Ray Yu-Ruei; Baker, David.
Afiliação
  • Ovchinnikov S; Department of Biochemistry, University of Washington, Seattle, Washington, 98195.
  • Park H; Institute for Protein Design, University of Washington, Seattle, Washington, 98195.
  • Kim DE; Department of Biochemistry, University of Washington, Seattle, Washington, 98195.
  • Liu Y; Institute for Protein Design, University of Washington, Seattle, Washington, 98195.
  • Wang RY; Institute for Protein Design, University of Washington, Seattle, Washington, 98195.
  • Baker D; Howard Hughes Medical Institute, University of Washington, Seattle, Washington, 98195.
Proteins ; 84 Suppl 1: 181-8, 2016 09.
Article em En | MEDLINE | ID: mdl-26857542
ABSTRACT
In CASP11 we generated protein structure models using simulated ambiguous and unambiguous nuclear Overhauser effect (NOE) restraints with a two stage protocol. Low resolution models were generated guided by the unambiguous restraints using continuous chain folding for alpha and alpha-beta proteins, and iterative annealing for all beta proteins to take advantage of the strand pairing information implicit in the restraints. The Rosetta fragment/model hybridization protocol was then used to recombine and regularize these models, and refine them in the Rosetta full atom energy function guided by both the unambiguous and the ambiguous restraints. Fifteen out of 19 targets were modeled with GDT-TS quality scores greater than 60 for Model 1, significantly improving upon the non-assisted predictions. Our results suggest that atomic level accuracy is achievable using sparse NOE data when there is at least one correctly assigned NOE for every residue. Proteins 2016; 84(Suppl 1)181-188. © 2016 Wiley Periodicals, Inc.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Software / Proteínas / Modelos Moleculares / Modelos Estatísticos / Biologia Computacional Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Ano de publicação: 2016 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Software / Proteínas / Modelos Moleculares / Modelos Estatísticos / Biologia Computacional Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Ano de publicação: 2016 Tipo de documento: Article