An Efficient GaMD Multi-Level Enhanced Sampling Strategy for Polarizable Force Fields Simulations of Large Biological Systems

Frédéric Célerse, Theo Jaffrelot-Inizan, Louis Lagardère, Olivier Adjoua, Pierre Monmarché, Yinglong Mia, E. Derat, Jean‐Philip Piquemal
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Abstract

We introduce a novel multi-level enhanced sampling strategy grounded on Gaussian accelerated Molecular Dynamics (GaMD). First, we propose a GaMD multi-GPUs -accelerated implementation within Tinker-HP. For the specific use with the flexible AMOEBA polarizable force field (PFF), we introduce the new "dual–water" GaMD mode. By adding harmonic boosts to the water stretching and bonding terms, it accelerates the solvent-solute interactions while enabling speedups with 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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一种用于大型生物系统极化力场模拟的高效GaMD多级增强采样策略
我们介绍了一种新的基于高斯加速分子动力学(GaMD)的多级增强采样策略。首先,我们提出了一个GaMD多GPU加速在廷克惠普实现。对于柔性AMOEBA极化力场(PFF)的特定用途,我们引入了新的“双水”GaMD模式。通过在水拉伸和键合项中添加谐波增强,它加速了溶剂-溶质的相互作用,同时实现了快速多时间步长积分器的加速。为了进一步缩短求解时间,我们将GaMD与Umbrella Sampling(US)相结合。在CD2–CD58系统(168000个原子)的1D平均力势能(PMF)上测试了GaMD-US/双水方法,使AMOEBA PMF收敛在实验值的1 kcal/mol内。最后,增加了自适应采样(AS),实现了AS–GaMD功能,但也引入了新的自适应采样–US–GaMD(ASUS–GaMD)方案。与GaMD–US和US相比,高度并行的ASUS–GaMD设置将收敛时间分别缩短了10和20。
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