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Leveraging structure for enzyme function prediction: methods, opportunities, and challenges.
Jacobson, Matthew P; Kalyanaraman, Chakrapani; Zhao, Suwen; Tian, Boxue.
Afiliación
  • Jacobson MP; Department of Pharmaceutical Chemistry, School of Pharmacy, University of California, San Francisco, CA 94158, USA; California Institute for Quantitative Biomedical Research, University of California, San Francisco, CA 94158, USA. Electronic address: matt.jacobson@ucsf.edu.
  • Kalyanaraman C; Department of Pharmaceutical Chemistry, School of Pharmacy, University of California, San Francisco, CA 94158, USA; California Institute for Quantitative Biomedical Research, University of California, San Francisco, CA 94158, USA.
  • Zhao S; Department of Pharmaceutical Chemistry, School of Pharmacy, University of California, San Francisco, CA 94158, USA; California Institute for Quantitative Biomedical Research, University of California, San Francisco, CA 94158, USA.
  • Tian B; Department of Pharmaceutical Chemistry, School of Pharmacy, University of California, San Francisco, CA 94158, USA; California Institute for Quantitative Biomedical Research, University of California, San Francisco, CA 94158, USA.
Trends Biochem Sci ; 39(8): 363-71, 2014 Aug.
Article en En | MEDLINE | ID: mdl-24998033
The rapid growth of the number of protein sequences that can be inferred from sequenced genomes presents challenges for function assignment, because only a small fraction (currently <1%) has been experimentally characterized. Bioinformatics tools are commonly used to predict functions of uncharacterized proteins. Recently, there has been significant progress in using protein structures as an additional source of information to infer aspects of enzyme function, which is the focus of this review. Successful application of these approaches has led to the identification of novel metabolites, enzyme activities, and biochemical pathways. We discuss opportunities to elucidate systematically protein domains of unknown function, orphan enzyme activities, dead-end metabolites, and pathways in secondary metabolism.
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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Proteínas / Modelos Moleculares Tipo de estudio: Prognostic_studies / Risk_factors_studies Límite: Animals / Humans Idioma: En Revista: Trends Biochem Sci Año: 2014 Tipo del documento: Article

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Proteínas / Modelos Moleculares Tipo de estudio: Prognostic_studies / Risk_factors_studies Límite: Animals / Humans Idioma: En Revista: Trends Biochem Sci Año: 2014 Tipo del documento: Article