Your browser doesn't support javascript.
loading
Protein structure prediction using Rosetta in CASP12.
Ovchinnikov, Sergey; Park, Hahnbeom; Kim, David E; DiMaio, Frank; Baker, David.
Afiliación
  • Ovchinnikov S; Department of Biochemistry, University of Washington, Seattle, Washington.
  • Park H; Institute for Protein Design, University of Washington, Seattle, Washington.
  • Kim DE; Department of Biochemistry, University of Washington, Seattle, Washington.
  • DiMaio F; Institute for Protein Design, University of Washington, Seattle, Washington.
  • Baker D; Institute for Protein Design, University of Washington, Seattle, Washington.
Proteins ; 86 Suppl 1: 113-121, 2018 03.
Article en En | MEDLINE | ID: mdl-28940798
ABSTRACT
We describe several notable aspects of our structure predictions using Rosetta in CASP12 in the free modeling (FM) and refinement (TR) categories. First, we had previously generated (and published) models for most large protein families lacking experimentally determined structures using Rosetta guided by co-evolution based contact predictions, and for several targets these models proved better starting points for comparative modeling than any known crystal structure-our model database thus starts to fulfill one of the goals of the original protein structure initiative. Second, while our "human" group simply submitted ROBETTA models for most targets, for six targets expert intervention improved predictions considerably; the largest improvement was for T0886 where we correctly parsed two discontinuous domains guided by predicted contact maps to accurately identify a structural homolog of the same fold. Third, Rosetta all atom refinement followed by MD simulations led to consistent but small improvements when starting models were close to the native structure, and larger but less consistent improvements when starting models were further away.
Asunto(s)
Palabras clave

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Conformación Proteica / Proteínas / Modelos Moleculares / Pliegue de Proteína / Biología Computacional Tipo de estudio: Prognostic_studies / Risk_factors_studies Límite: Humans Idioma: En Revista: Proteins Asunto de la revista: BIOQUIMICA Año: 2018 Tipo del documento: Article

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Conformación Proteica / Proteínas / Modelos Moleculares / Pliegue de Proteína / Biología Computacional Tipo de estudio: Prognostic_studies / Risk_factors_studies Límite: Humans Idioma: En Revista: Proteins Asunto de la revista: BIOQUIMICA Año: 2018 Tipo del documento: Article