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

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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.

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Acknowledgements

This work was supported by Grants NIH R21 GM127952, NIH R01 GM12581301, NSF AF 1816314, NSF AF 1645512 and RSF No 14-11-00877.

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Authors and Affiliations

  1. Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY, USA

    Mikhail Ignatov, Dzmitry Padhorny, Sergei Kotelnikov & Dima Kozakov

  2. Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY, USA

    Mikhail Ignatov, Cong Liu, Dzmitry Padhorny, Sergei Kotelnikov, Alberto Perez, Ken Dill & Dima Kozakov

  3. Institute for Advanced Computational Sciences, Stony Brook University, Stony Brook, NY, USA

    Mikhail Ignatov, Dzmitry Padhorny, Sergei Kotelnikov & Dima Kozakov

  4. Moscow Institute of Physics and Technology, Moscow, Russia

    Sergei Kotelnikov & Andrey Kazennov

  5. Department of Biomedical Engineering, Boston University, Boston, MA, USA

    Zhuyezi Sun & Istvan Kolosvari

  6. Innopolis University, Innopolis, Russia

    Andrey Alekseenko, Ivan Grebenkin & Yaroslav Kholodov

  7. Institute for Computer-Aided Design, Russian Academy of Sciences, Moscow, Russia

    Mikhail Ignatov, Andrey Alekseenko, Dzmitry Padhorny, Sergei Kotelnikov, Andrey Kazennov, Ivan Grebenkin & Yaroslav Kholodov

Authors
  1. Mikhail Ignatov
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  2. Cong Liu
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  3. Andrey Alekseenko
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  9. Yaroslav Kholodov
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  10. Istvan Kolosvari
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  11. Alberto Perez
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  12. Ken Dill
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  13. Dima Kozakov
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Corresponding author

Correspondence to Dima Kozakov.

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Ignatov, M., Liu, C., Alekseenko, A. et al. Monte Carlo on the manifold and MD refinement for binding pose prediction of protein–ligand complexes: 2017 D3R Grand Challenge. J Comput Aided Mol Des 33, 119–127 (2019). https://doi.org/10.1007/s10822-018-0176-0

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  • DOI: https://doi.org/10.1007/s10822-018-0176-0

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