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

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 .

journal_name

J Comput Aided Mol Des

authors

Ignatov M,Liu C,Alekseenko A,Sun Z,Padhorny D,Kotelnikov S,Kazennov A,Grebenkin I,Kholodov Y,Kolosvari I,Perez A,Dill K,Kozakov D

doi

10.1007/s10822-018-0176-0

subject

Has Abstract

pub_date

2019-01-01 00:00:00

pages

119-127

issue

1

eissn

0920-654X

issn

1573-4951

pii

10.1007/s10822-018-0176-0

journal_volume

33

pub_type

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