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High throughput and quantitative enzymology in the genomic era.
Mokhtari, D A; Appel, M J; Fordyce, P M; Herschlag, D.
Afiliação
  • Mokhtari DA; Department of Biochemistry, Stanford University, Stanford, CA, 94305, USA.
  • Appel MJ; Department of Biochemistry, Stanford University, Stanford, CA, 94305, USA.
  • Fordyce PM; Department of Bioengineering, Stanford University, Stanford, CA, 94305, USA; ChEM-H Institute, Stanford University, Stanford, CA, 94305, USA; Department of Genetics, Stanford University, Stanford, CA, 94305, USA; Chan Zuckerberg Biohub San Francisco, CA, 94110, USA. Electronic address: pfordyce@stan
  • Herschlag D; Department of Biochemistry, Stanford University, Stanford, CA, 94305, USA; Department of Chemical Engineering, Stanford University, Stanford, CA, 94305, USA; ChEM-H Institute, Stanford University, Stanford, CA, 94305, USA. Electronic address: herschla@stanford.edu.
Curr Opin Struct Biol ; 71: 259-273, 2021 12.
Article em En | MEDLINE | ID: mdl-34592682
Accurate predictions from models based on physical principles are the ultimate metric of our biophysical understanding. Although there has been stunning progress toward structure prediction, quantitative prediction of enzyme function has remained challenging. Realizing this goal will require large numbers of quantitative measurements of rate and binding constants and the use of these ground-truth data sets to guide the development and testing of these quantitative models. Ground truth data more closely linked to the underlying physical forces are also desired. Here, we describe technological advances that enable both types of ground truth measurements. These advances allow classic models to be tested, provide novel mechanistic insights, and place us on the path toward a predictive understanding of enzyme structure and function.
Assuntos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Genômica Tipo de estudo: Prognostic_studies Idioma: En Revista: Curr Opin Struct Biol Assunto da revista: BIOLOGIA MOLECULAR Ano de publicação: 2021 Tipo de documento: Article País de afiliação: Estados Unidos País de publicação: Reino Unido

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Genômica Tipo de estudo: Prognostic_studies Idioma: En Revista: Curr Opin Struct Biol Assunto da revista: BIOLOGIA MOLECULAR Ano de publicação: 2021 Tipo de documento: Article País de afiliação: Estados Unidos País de publicação: Reino Unido