RESUMO
Technologies for the engineering of biocatalysts for efficient synthesis of pharmaceutical targets have advanced dramatically over the last few years. Integration of computational methods for structural modeling, combined with high through put methods for expression and screening of biocatalysts and algorithms for mining experimental data, have allowed the creation of highly engineered biocatalysts for the efficient synthesis of pharmaceuticals. Methods for the synthesis of chiral alcohols and amines have been particularly successful, along with the creation of non-natural activities for such desirable reactions as cyclopropanation and esterification.
Assuntos
Biocatálise , Descoberta de Drogas/métodos , Indústria Farmacêutica , Enzimas e Coenzimas/metabolismo , Engenharia Metabólica , Preparações Farmacêuticas/síntese química , Álcoois/metabolismo , Aminas/química , Animais , Indústria Farmacêutica/métodos , Indústria Farmacêutica/tendências , Enzimas e Coenzimas/genética , Humanos , Engenharia Metabólica/métodos , Engenharia Metabólica/tendências , Preparações Farmacêuticas/isolamento & purificação , Preparações Farmacêuticas/metabolismo , Bibliotecas de Moléculas Pequenas/isolamento & purificação , Bibliotecas de Moléculas Pequenas/metabolismoAssuntos
Antibacterianos/metabolismo , Enzimas e Coenzimas/metabolismo , Radicais Livres/metabolismo , S-Adenosilmetionina/metabolismo , Antibacterianos/química , Catálise , Ativação Enzimática , Enzimas e Coenzimas/química , Radicais Livres/química , Modelos Químicos , S-Adenosilmetionina/química , Tiazóis/química , Tiazóis/metabolismo , Triptofano/química , Triptofano/metabolismoRESUMO
Study of interactions between drugs and target proteins is an essential step in genomic drug discovery. It is very hard to determine the compound-protein interactions or drug-target interactions by experiment alone. As supplementary, effective prediction model using machine learning or data mining methods can provide much help. In this study, a prediction method based on Nearest Neighbor Algorithm and a novel metric, which was obtained by combining compound similarity and functional domain composition, was proposed. The target proteins were divided into the following groups: enzymes, ion channels, G protein-coupled receptors, and nuclear receptors. As a result, four predictors with the optimal parameters were established. The overall prediction accuracies, evaluated by jackknife cross-validation test, for four groups of target proteins are 90.23%, 94.74%, 97.80%, and 97.51%, respectively, indicating that compound similarity and functional domain composition are very effective to predict drug-target interaction networks.
