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

  • Mikhail Ignatov
  • , Cong Liu
  • , Andrey Alekseenko
  • , Zhuyezi Sun
  • , Dzmitry Padhorny
  • , Sergei Kotelnikov
  • , Andrey Kazennov
  • , Ivan Grebenkin
  • , Yaroslav Kholodov
  • , Istvan Kolosvari
  • , Alberto Perez
  • , Ken Dill
  • , Dima Kozakov
  • Stony Brook University
  • Russian Academy of Sciences
  • Innopolis University
  • Boston University
  • Moscow Institute of Physics and Technology

Research output: Contribution to journalArticlepeer-review

10 Scopus citations

Abstract

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.

Original languageEnglish
Pages (from-to)119-127
Number of pages9
JournalJournal of Computer-Aided Molecular Design
Volume33
Issue number1
DOIs
StatePublished - Jan 15 2019

Keywords

  • Cathepsin S
  • D3R
  • Manifold
  • MD
  • Minimization
  • Monte Carlo

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