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Article Dans Une Revue Journal of Chemical Theory and Computation Année : 2022

An Efficient Gaussian-Accelerated Molecular Dynamics (GaMD) Multilevel Enhanced Sampling Strategy: Application to Polarizable Force Fields Simulations of Large Biological Systems

Résumé

We detail a novel multi-level enhanced sampling strategy grounded on Gaussian accelerated Molecular Dynamics (GaMD). First, we propose a GaMD multi-GPUs accelerated implementation within the Tinker-HP molecular dynamics package. We then introduce the new "dual-water" mode and its use with the flexible AMOEBA polarizable force field. By adding harmonic boosts to the water stretching and bonding terms, it accelerates the solvent-solute interactions while enabling speedups thanks to the use of fast multiple–timestep integrators. To further reduce time-to-solution, we couple GaMD to Umbrella Sampling (US). The GaMD—US/dual–water approach is tested on the 1D Potential of Mean Force (PMF) of the CD2–CD58 system (168000 atoms) allowing the AMOEBA PMF to converge within 1 kcal/mol of the experimental value. Finally, Adaptive Sampling (AS) is added enabling AS–GaMD capabilities but also the introduction of the new Adaptive Sampling–US–GaMD (ASUS–GaMD) scheme. The highly parallel ASUS–GaMD setup decreases time to convergence by respectively 10 and 20 compared to GaMD–US and US.
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Dates et versions

hal-03360596 , version 1 (08-12-2023)

Identifiants

Citer

F. Célerse, T. Jaffrelot Inizan, L. Lagardère, O. Adjoua, P. Monmarché, et al.. An Efficient Gaussian-Accelerated Molecular Dynamics (GaMD) Multilevel Enhanced Sampling Strategy: Application to Polarizable Force Fields Simulations of Large Biological Systems. Journal of Chemical Theory and Computation, 2022, 18 (2), pp.968-977. ⟨10.1021/acs.jctc.1c01024⟩. ⟨hal-03360596⟩
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