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Algorithms for protein design.
Gainza, Pablo; Nisonoff, Hunter M; Donald, Bruce R.
Afiliación
  • Gainza P; Department of Computer Science, Duke University, Durham, NC, United States.
  • Nisonoff HM; Department of Computer Science, Duke University, Durham, NC, United States.
  • Donald BR; Department of Computer Science, Duke University, Durham, NC, United States; Department of Biochemistry, Duke University Medical Center, Durham, NC, United States; Department of Chemistry, Duke University, Durham, NC, United States. Electronic address: brd+cosb15@cs.duke.edu.
Curr Opin Struct Biol ; 39: 16-26, 2016 08.
Article en En | MEDLINE | ID: mdl-27086078
Computational structure-based protein design programs are becoming an increasingly important tool in molecular biology. These programs compute protein sequences that are predicted to fold to a target structure and perform a desired function. The success of a program's predictions largely relies on two components: first, the input biophysical model, and second, the algorithm that computes the best sequence(s) and structure(s) according to the biophysical model. Improving both the model and the algorithm in tandem is essential to improving the success rate of current programs, and here we review recent developments in algorithms for protein design, emphasizing how novel algorithms enable the use of more accurate biophysical models. We conclude with a list of algorithmic challenges in computational protein design that we believe will be especially important for the design of therapeutic proteins and protein assemblies.
Asunto(s)

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Algoritmos / Ingeniería de Proteínas / Proteínas / Biología Computacional Tipo de estudio: Prognostic_studies Idioma: En Revista: Curr Opin Struct Biol Asunto de la revista: BIOLOGIA MOLECULAR Año: 2016 Tipo del documento: Article País de afiliación: Estados Unidos

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Algoritmos / Ingeniería de Proteínas / Proteínas / Biología Computacional Tipo de estudio: Prognostic_studies Idioma: En Revista: Curr Opin Struct Biol Asunto de la revista: BIOLOGIA MOLECULAR Año: 2016 Tipo del documento: Article País de afiliación: Estados Unidos
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