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Meta-server for automatic analysis, scoring and ranking of docking models.
Anashkina, Anastasia A; Kravatsky, Yuri; Kuznetsov, Eugene; Makarov, Alexander A; Adzhubei, Alexei A.
Afiliação
  • Anashkina AA; Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, Moscow, Russia.
  • Kravatsky Y; Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, Moscow, Russia.
  • Kuznetsov E; V. A. Trapeznikov Institute of Control Sciences of Russian Academy of Sciences, Moscow, Russia.
  • Makarov AA; Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, Moscow, Russia.
  • Adzhubei AA; Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, Moscow, Russia.
Bioinformatics ; 34(2): 297-299, 2018 Jan 15.
Article em En | MEDLINE | ID: mdl-28968724
ABSTRACT
MOTIVATION Modelling with multiple servers that use different algorithms for docking results in more reliable predictions of interaction sites. However, the scoring and comparison of all models by an expert is time-consuming and is not feasible for large volumes of data generated by such modelling.

RESULTS:

Quality ASsessment of DOcking Models (QASDOM) Server is a simple and efficient tool for real-time simultaneous analysis, scoring and ranking of data sets of receptor-ligand complexes built by a range of docking techniques. This meta-server is designed to analyse large data sets of docking models and rank them by scoring criteria developed in this study. It produces two types of output showing the likelihood of specific residues and clusters of residues to be involved in receptor-ligand interactions and the ranking of models. The server also allows visualizing residues that form interaction sites in the receptor and ligand sequence and displays 3D model structures of the receptor-ligand complexes.

AVAILABILITY:

http//qasdom.eimb.ru. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Revista: Bioinformatics Ano de publicação: 2018 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Revista: Bioinformatics Ano de publicação: 2018 Tipo de documento: Article