gen.parRep: a first implementation of the Generalized Parallel Replica dynamics for the long time simulation of metastable biochemical systems

Florent Hédin 1, 2 Tony Lelievre 1, 2
2 MATHERIALS - MATHematics for MatERIALS
CERMICS - Centre d'Enseignement et de Recherche en Mathématiques et Calcul Scientifique, Inria de Paris
Abstract : Metastability is one of the major encountered obstacle when performing long molecular dynamics simulations, and many methods were developed to address this challenge. The "Parallel Replica"(ParRep) dynamics is known for allowing to simulate very long trajectories of metastable Langevin dynamics in the materials science community, but it relies on assumptions that can hardly be transposed to the world of biochemical simulations. The later developed "Generalized ParRep" variant solves those issues, but it was not applied to significant systems of interest so far. In this article we present a new implementation of the Generalized Parallel Replica method, targeting frequently encountered metastable biochemical systems, such as conformational equilibria (validation with alanine dipeptide) or dissociation of protein-ligand complexes (study of the FKBP-DMSO system). It will be shown that the resulting software exhibits a strong linear scalability, providing up to 70 % of the maximum possible speedup on several hundreds of CPUs.
Type de document :
Pré-publication, Document de travail
49 pages (including references), 12 numbered Figures + 1 Table Of Content Figure, 4 numbered Tables. 2018
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https://hal.archives-ouvertes.fr/hal-01832823
Contributeur : Tony Lelievre <>
Soumis le : lundi 9 juillet 2018 - 09:39:34
Dernière modification le : mardi 11 décembre 2018 - 10:32:08

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  • HAL Id : hal-01832823, version 1
  • ARXIV : 1807.02431

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Florent Hédin, Tony Lelievre. gen.parRep: a first implementation of the Generalized Parallel Replica dynamics for the long time simulation of metastable biochemical systems. 49 pages (including references), 12 numbered Figures + 1 Table Of Content Figure, 4 numbered Tables. 2018. 〈hal-01832823〉

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