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

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 the Tinker-HP molecular dynamics package. We 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 solvated 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 times compared to GaMD-US and US. Overall, beside the acceleration of PMF computations, Tinker-HP now allows for the simultaneous use of Adaptive Sampling and GaMD-"dual water" enhanced sampling approaches increasing the applicability of polarizable force fields to large scale simulations of biological systems.

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

10.33774/chemrxiv-2021-ggjfx-v3 ◽

2021 ◽

Author(s):

Fréderic Célerse ◽

Theo Jaffrelot-Inizan ◽

Louis Lagardère ◽

Olivier Adjoua ◽

Pierre Monmarché ◽

...

Keyword(s):

Molecular Dynamics ◽

Adaptive Sampling ◽

Sampling Strategy ◽

Umbrella Sampling ◽

Potential Of Mean Force ◽

Enhanced Sampling ◽

Polarizable Force Field ◽

Accelerated Molecular Dynamics ◽

Polarizable Force Fields ◽

Multi Level

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

10.33774/chemrxiv-2021-ggjfx-v2 ◽

2021 ◽

Author(s):

Fréderic Célerse ◽

Theo Jaffrelot-Inizan ◽

Louis Lagardère ◽

Olivier Adjoua ◽

Pierre Monmarché ◽

...

Keyword(s):

Molecular Dynamics ◽

Adaptive Sampling ◽

Sampling Strategy ◽

Umbrella Sampling ◽

Potential Of Mean Force ◽

Enhanced Sampling ◽

Polarizable Force Field ◽

Accelerated Molecular Dynamics ◽

Polarizable Force Fields ◽

Multi Level

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

10.33774/chemrxiv-2021-ggjfx ◽

2021 ◽

Author(s):

Fréderic Célerse ◽

Theo Jaffrelot-Inizan ◽

Louis Lagardère ◽

Olivier Adjoua ◽

Pierre Monmarché ◽

...

Keyword(s):

Adaptive Sampling ◽

Sampling Strategy ◽

Umbrella Sampling ◽

Potential Of Mean Force ◽

Enhanced Sampling ◽

Polarizable Force Field ◽

Accelerated Molecular Dynamics ◽

Polarizable Force Fields ◽

Solute Interactions ◽

Multi Level

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

Chemical Science ◽

10.1039/d1sc00145k ◽

2021 ◽

Author(s):

Théo Jaffrelot Inizan ◽

Frédéric Célerse ◽

Olivier Adjoua ◽

Dina El Ahdab ◽

Luc-Henri Jolly ◽

...

Keyword(s):

Molecular Dynamics ◽

High Resolution ◽

Adaptive Sampling ◽

Force Fields ◽

Sampling Strategy ◽

Md Simulations ◽

Conformational Space ◽

Many Body ◽

Polarizable Force Fields ◽

Main Protease

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).

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Identification of rare Lewis oligosaccharide conformers in aqueous solution using enhanced sampling molecular dynamics

10.26434/chemrxiv.5469241.v1 ◽

2017 ◽

Author(s):

Irfan Alibay ◽

Kepa K. Burusco ◽

Neil J. Bruce ◽

Richard A. Bryce

Keyword(s):

Molecular Dynamics ◽

Aqueous Solution ◽

Md Simulations ◽

Umbrella Sampling ◽

Biological Action ◽

Sialyl Lewis X ◽

Enhanced Sampling ◽

Sialyl Lewis ◽

Aqueous Solvent ◽

A Minor

Determining the conformations accessible to carbohydrate ligands in aqueous solution is important for understanding their biological action. In this work, we evaluate the conformational free energy surfaces of Lewis oligosaccharides in explicit aqueous solvent using a multidimensional variant of the swarm-enhanced sampling molecular dynamics (msesMD) method; we compare with multi-microsecond unbiased MD simulations, umbrella sampling and accelerated MD approaches. For the sialyl Lewis A tetrasaccharide, msesMD simulations in aqueous solution predict conformer landscapes in general agreement with the other biased methods and with triplicate unbiased 10 ms trajectories; these simulations find a predominance of closed conformer and a range of low occupancy open forms. The msesMD simulations also suggest closed-to-open transitions in the tetrasaccharide are facilitated by changes in ring puckering of its GlcNAc residue away from the 4C1 form, in line with previous work. For sialyl Lewis X tetrasaccharide, msesMD simulations predict a minor population of an open form in solution, corresponding to a rare lectin-bound pose observed crystallographically. Overall, from comparison with biased MD calculations, we find that triplicate 10 ms unbiased MD simulations may not be enough to fully sample glycan conformations in aqueous solution. However, the computational efficiency and intuitive approach of the msesMD method suggest potential for its application in glycomics as a tool for analysis of oligosaccharide conformation.

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Use of Umbrella Sampling in the Calculation of the Potential of Mean Force for Maltose in Vacuum From Molecular Dynamics Simulations

The Journal of Physical Chemistry ◽

10.1021/j100029a007 ◽

1995 ◽

Vol 99 (29) ◽

pp. 11339-11343 ◽

Cited By ~ 28

Author(s):

R. K. Schmidt ◽

B. Teo ◽

J. W. Brady

Keyword(s):

Molecular Dynamics ◽

Molecular Dynamics Simulations ◽

Umbrella Sampling ◽

Potential Of Mean Force ◽

Dynamics Simulations

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Competition among Li+, Na+, K+, and Rb+Monovalent Ions for DNA in Molecular Dynamics Simulations Using the Additive CHARMM36 and Drude Polarizable Force Fields

The Journal of Physical Chemistry B ◽

10.1021/acs.jpcb.5b00683 ◽

2015 ◽

Vol 119 (12) ◽

pp. 4428-4440 ◽

Cited By ~ 52

Author(s):

Alexey Savelyev ◽

Alexander D. MacKerell

Keyword(s):

Molecular Dynamics ◽

Molecular Dynamics Simulations ◽

Force Fields ◽

Polarizable Force Fields ◽

Monovalent Ions ◽

Dynamics Simulations

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How well do force fields capture the strength of salt bridges in proteins?

10.1101/272799 ◽

2018 ◽

Author(s):

Mustapha Carab Ahmed ◽

Elena Papaleo ◽

Kresten Lindorff-Larsen

Keyword(s):

Molecular Dynamics ◽

Molecular Dynamics Simulations ◽

Force Fields ◽

Salt Bridge ◽

Salt Bridges ◽

Enhanced Sampling ◽

Important Interaction ◽

Close Proximity ◽

The Stability ◽

Dynamics Simulations

AbstractSalt bridges form between pairs of ionisable residues in close proximity and are important interactions in proteins. While salt bridges are known to be important both for protein stability, recognition and regulation, we still do not have fully accurate predictive models to assess the energetic contributions of salt bridges. Molecular dynamics simulations is one technique that may be used study the complex relationship between structure, solvation and energetics of salt bridges, but the accuracy of such simulations depend on the force field used. We have used NMR data on the B1 domain of protein G (GB1) to benchmark molecular dynamics simulations. Using enhanced sampling simulations, we calculated the free energy of forming a salt bridge for three possible ionic interactions in GB1. The NMR experiments showed that these interactions are either not formed, or only very weakly formed, in solution. In contrast, we show that the stability of the salt bridges is slightly overestimated in simulations of GB1 using six commonly used combinations of force fields and water models. We therefore conclude that further work is needed to refine our ability to model quantitatively the stability of salt bridges through simulations, and that comparisons between experiments and simulations will play a crucial role in furthering our understanding of this important interaction.

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