High-resolution mining of the SARS-CoV-2 main protease conformational space: supercomputer-driven unsupervised adaptive sampling-Reference-Cited by-同舟云学术

High-resolution mining of the SARS-CoV-2 main protease conformational space: supercomputer-driven unsupervised adaptive sampling

Published:2021 Issue:13 Volume:12 Page:4889-4907
ISSN:2041-6520
Container-title:Chemical Science
language:en
Short-container-title:Chem. Sci.

Author:

Jaffrelot Inizan Théo¹²³⁴⁵^ORCID,Célerse Frédéric¹²³⁴⁵^ORCID,Adjoua Olivier¹²³⁴⁵,El Ahdab Dina¹²³⁴⁵^ORCID,Jolly Luc-Henri¹⁶⁷⁴⁵,Liu Chengwen⁸⁹¹⁰,Ren Pengyu⁸⁹¹⁰,Montes Matthieu¹¹¹²¹³¹⁴⁴,Lagarde Nathalie¹¹¹²¹³¹⁴⁴,Lagardère Louis¹²³⁴⁵,Monmarché Pierre¹²³⁴⁵,Piquemal Jean-Philip¹²³⁴⁵^ORCID

Affiliation:

1. Sorbonne Université

2. LCT

3. UMR 7616 CNRS

4. Paris

5. France

6. IP2CT

7. FR 2622 CNRS

8. University of Texas at Austin

9. Department of Biomedical Engineering

10. USA

11. Laboratoire GBCM

12. EA 7528

13. CNAM

14. Hésam Université

Abstract

We provide an unsupervised adaptive sampling strategy capable of producing μs-timescale molecular dynamics (MD) simulations of large biosystems using many-body polarizable force fields (PFFs).

Funder

H2020 European Research Council

Ministère de la Défense

Grand Équipement National De Calcul Intensif

National Institutes of Health

Agence Nationale de la Recherche

Conseil National de la Recherche Scientifique

Partnership for Advanced Computing in Europe AISBL

Welch Foundation

Publisher

Royal Society of Chemistry (RSC)

Subject