1. Meta-server for automatic analysis, scoring and ranking of docking models
- Author
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Yuri V. Kravatsky, Alexander A. Makarov, Eugene N. Kuznetsov, Anastasia A. Anashkina, and Alexei A. Adzhubei
- Subjects
0301 basic medicine ,Statistics and Probability ,ComputingMethodologies_SIMULATIONANDMODELING ,Computer science ,business.industry ,Ligand ,Machine learning ,computer.software_genre ,Biochemistry ,Computer Science Applications ,03 medical and health sciences ,Computational Mathematics ,ComputingMethodologies_PATTERNRECOGNITION ,030104 developmental biology ,0302 clinical medicine ,Computational Theory and Mathematics ,Docking (molecular) ,Artificial intelligence ,business ,Molecular Biology ,computer ,030217 neurology & neurosurgery - 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.
- Published
- 2017
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