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The Usage of ACCLUSTER for Peptide Binding Site Prediction.
Yan, Chengfei; Xu, Xianjin; Zou, Xiaoqin.
Afiliação
  • Yan C; Department of Physics and Astronomy, Department of Biochemistry, Dalton Cardiovascular Research Center, Informatics Institute, University of Missouri, 134 Research Park Drive, 117A Dalton Bldg, Columbia, MO, 65211, USA.
  • Xu X; Department of Physics and Astronomy, Department of Biochemistry, Dalton Cardiovascular Research Center, Informatics Institute, University of Missouri, 134 Research Park Drive, 117A Dalton Bldg, Columbia, MO, 65211, USA.
  • Zou X; Department of Physics and Astronomy, Department of Biochemistry, Dalton Cardiovascular Research Center, Informatics Institute, University of Missouri, 134 Research Park Drive, 117A Dalton Bldg, Columbia, MO, 65211, USA. zoux@missouri.edu.
Methods Mol Biol ; 1561: 3-9, 2017.
Article em En | MEDLINE | ID: mdl-28236229
ABSTRACT
Peptides mediate up to 40 % of protein-protein interactions in a variety of cellular processes and are also attractive drug candidates. Thus, predicting peptide binding sites on the given protein structure is of great importance for mechanistic investigation of protein-peptide interactions and peptide therapeutics development. In this chapter, we describe the usage of our web server, referred to as ACCLUSTER, for peptide binding site prediction for a given protein structure. ACCLUSTER is freely available for users without registration at http//zougrouptoolkit.missouri.edu/accluster .
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Fragmentos de Peptídeos / Proteínas / Bases de Dados de Proteínas Tipo de estudo: Prognostic_studies / Risk_factors_studies Limite: Animals / Humans Idioma: En Ano de publicação: 2017 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Fragmentos de Peptídeos / Proteínas / Bases de Dados de Proteínas Tipo de estudo: Prognostic_studies / Risk_factors_studies Limite: Animals / Humans Idioma: En Ano de publicação: 2017 Tipo de documento: Article