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Structure-based prediction of ligand-protein interactions on a genome-wide scale.
Hwang, Howook; Dey, Fabian; Petrey, Donald; Honig, Barry.
Afiliação
  • Hwang H; Department of Systems Biology, Columbia University, New York, NY 10032.
  • Dey F; Center for Computational Biology and Bioinformatics, Columbia University, New York, NY 10032.
  • Petrey D; Department of Biochemistry and Molecular Biophysics, Columbia University, New York, NY 10032.
  • Honig B; Howard Hughes Medical Institute, Columbia University, New York, NY 10032.
Proc Natl Acad Sci U S A ; 114(52): 13685-13690, 2017 12 26.
Article em En | MEDLINE | ID: mdl-29229851
We report a template-based method, LT-scanner, which scans the human proteome using protein structural alignment to identify proteins that are likely to bind ligands that are present in experimentally determined complexes. A scoring function that rapidly accounts for binding site similarities between the template and the proteins being scanned is a crucial feature of the method. The overall approach is first tested based on its ability to predict the residues on the surface of a protein that are likely to bind small-molecule ligands. The algorithm that we present, LBias, is shown to compare very favorably to existing algorithms for binding site residue prediction. LT-scanner's performance is evaluated based on its ability to identify known targets of Food and Drug Administration (FDA)-approved drugs and it too proves to be highly effective. The specificity of the scoring function that we use is demonstrated by the ability of LT-scanner to identify the known targets of FDA-approved kinase inhibitors based on templates involving other kinases. Combining sequence with structural information further improves LT-scanner performance. The approach we describe is extendable to the more general problem of identifying binding partners of known ligands even if they do not appear in a structurally determined complex, although this will require the integration of methods that combine protein structure and chemical compound databases.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Proteínas / Genoma / Bases de Dados de Proteínas / Inibidores de Proteínas Quinases Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Ano de publicação: 2017 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Proteínas / Genoma / Bases de Dados de Proteínas / Inibidores de Proteínas Quinases Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Ano de publicação: 2017 Tipo de documento: Article