Monte Carlo on the manifold and MD refinement for binding pose prediction of protein–ligand complexes: 2017 D3R Grand Challenge

Manifold representations of rotational/translational motion and conformational space of a ligand were previously shown to be effective for local energy optimization. In this paper we report the development of the Monte-Carlo energy minimization approach (MCM), which uses the same manifold representa...

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Bibliographic Details
Published in:Journal of computer-aided molecular design 2019-01, Vol.33 (1), p.119-127
Main Authors: Ignatov, Mikhail, Liu, Cong, Alekseenko, Andrey, Sun, Zhuyezi, Padhorny, Dzmitry, Kotelnikov, Sergei, Kazennov, Andrey, Grebenkin, Ivan, Kholodov, Yaroslav, Kolosvari, Istvan, Perez, Alberto, Dill, Ken, Kozakov, Dima
Format: Article
Language:English
Subjects:
Animal Anatomy
Binding Sites
Cathepsins - antagonists & inhibitors
Chemistry
Chemistry and Materials Science
Computer Applications in Chemistry
Computer-Aided Design
Coordination compounds
Crystallography, X-Ray
Databases, Protein
Docking
Drug Design
Energy conservation
Histology
Ligands
Manifolds
Molecular Conformation
Molecular Docking Simulation - methods
Molecular Dynamics Simulation
Monte Carlo Method
Monte Carlo simulation
Morphology
Optimization
Physical Chemistry
Protein Binding
Proteins
Representations
Thermodynamics
Translational motion
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Summary:Manifold representations of rotational/translational motion and conformational space of a ligand were previously shown to be effective for local energy optimization. In this paper we report the development of the Monte-Carlo energy minimization approach (MCM), which uses the same manifold representation. The approach was integrated into the docking pipeline developed for the current round of D3R experiment, and according to D3R assessment produced high accuracy poses for Cathepsin S ligands. Additionally, we have shown that (MD) refinement further improves docking quality. The code of the Monte-Carlo minimization is freely available at https://bitbucket.org/abc-group/mcm-demo .
ISSN:0920-654X
1573-4951
DOI:10.1007/s10822-018-0176-0