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Modeling complex RNA tertiary folds with Rosetta.
Cheng, Clarence Yu; Chou, Fang-Chieh; Das, Rhiju.
Afiliación
  • Cheng CY; Department of Biochemistry, Stanford University, Stanford, California, USA.
  • Chou FC; Department of Biochemistry, Stanford University, Stanford, California, USA.
  • Das R; Department of Biochemistry, Stanford University, Stanford, California, USA; Department of Physics, Stanford University, Stanford, California, USA. Electronic address: rhiju@stanford.edu.
Methods Enzymol ; 553: 35-64, 2015.
Article en En | MEDLINE | ID: mdl-25726460
ABSTRACT
Reliable modeling of RNA tertiary structures is key to both understanding these structures' roles in complex biological machines and to eventually facilitating their design for molecular computing and robotics. In recent years, a concerted effort to improve computational prediction of RNA structure through the RNA-Puzzles blind prediction trials has accelerated advances in the field. Among other approaches, the versatile and expanding Rosetta molecular modeling software now permits modeling of RNAs in the 100-300 nucleotide size range at consistent subhelical (~1 nm) resolution. Our laboratory's current state-of-the-art methods for RNAs in this size range involve Fragment Assembly of RNA with Full-Atom Refinement (FARFAR), which optimizes RNA conformations in the context of a physically realistic energy function, as well as hybrid techniques that leverage experimental data to inform computational modeling. In this chapter, we give a practical guide to our current workflow for modeling RNA three-dimensional structures using FARFAR, including strategies for using data from multidimensional chemical mapping experiments to focus sampling and select accurate conformations.
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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Programas Informáticos / ARN / Modelos Moleculares Tipo de estudio: Prognostic_studies Idioma: En Revista: Methods Enzymol Año: 2015 Tipo del documento: Article País de afiliación: Estados Unidos

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Programas Informáticos / ARN / Modelos Moleculares Tipo de estudio: Prognostic_studies Idioma: En Revista: Methods Enzymol Año: 2015 Tipo del documento: Article País de afiliación: Estados Unidos