(656a) Hybrid Monte Carlo – Molecular Dynamics Simulations

Inherent to all computer simulations to understand biological structure and function, or perform drug design/discovery, is the need to efficiently sample the necessary phase space. However, there are a number of situations where the standard molecular dynamics simulations will fail to produce converged results [1, 2, 3, 4]. For example, ligand-protein binding energies are strongly affected by protein hydration state [2], and slow water exchange with the surroundings can lead to incorrect results [2, 3].

To address this issue, we present a hybrid Monte Carlo-molecular dynamics approach, which utilizes the strengths of each method to optimize sampling efficiency, and enable previously intractable calculations to be performed. This work builds on the software GPU Optimized Monte Carlo (GOMC) [5, 6], which is linked to the molecular dynamics software NAMD via a Python manager code that oversees information transfer and schedules the execution of each code [7, 8]. A number of illustrative examples are provided to highlight the potential of this approach, including prediction of the phase behavior of water, adsorption in a graphite slit pore [4], and hydration of protein binding pockets [3].

References

[1] Y. Deng and B. Roux, "Computation of binding free energy with molecular dynamics and grand canonical Monte Carlo simulations," J. Chem. Phys., vol. 128, p. 115103, 2008.

[2] G. Ross, E. Russell, Y. Deng, C. Lu, E. Harder, R. Abel and L. Wang, "Enhancing Water Sampling in Free Energy Calculations with Grand Canonical Monte Carlo," Journal of Chemical Theory and Computation, vol. 16, p. 6061–6076, 2020.

[3] M. Samways, H. Bruce Macdonald and J. Essex, "grand: A Python Module for Grand Canonical Water Sampling in OpenMM," Journal of Chemical Information and Modeling, vol. 60, p. 4436–4441, 2020.

[4] I. Ben-Shalom, C. Lin, T. Kurtzman, R. Walker and M. Gilson, "Simulating Water Exchange to Buried Binding Sites," Journal of Chemical Theory and Computation, vol. 15, p. 2684–2691, 2019.

[5] Y. Nejahi, M. Barhaghi, J. Mick, B. Jackman, K. Rushaidat, Y. Li, L. Schwiebert and J. Potoff, "GOMC: GPU Optimized Monte Carlo for the simulation of phase equilibria and physical properties of complex fluids," SoftwareX, vol. 9, p. 20–27, 2019.

[6] Y. Nejahi, M. Barhaghi, G. Schwing, L. Schwiebert and J. Potoff, "Update 2.70 to GOMC: GPU Optimized Monte Carlo for the simulation of phase equilibria and physical properties of complex fluids," SoftwareX , vol. 13, p. 100627, 2021.

[7] J. Phillips, R. Braun, W. Wang, J. Gumbart, E. Tajkhorshid, E. Villa, C. Chipot, R. Skeel, L. Kale and K. Schulten, "Scalable molecular dynamics with NAMD," Journal of Computational Chemistry, vol. 26, p. 1781–1802, 2005.

[8] J. Phillips, D. Hardy, J. Maia, J. Stone, J. Ribeiro, R. Bernardi, R. Buch, G. Fiorin, J. Hénin, W. Jiang, R. McGreevy, M. Melo, B. Radak, R. Skeel, A. Singharoy, Y. Wang, B. Roux, A. Aksimentiev, Z. Luthey-Schulten, L. Kalé, K. Schulten, C. Chipot, E. Tajkhorshid, " Scalable molecular dynamics on CPU and GPU architectures with NAMD," Journal of Chemical Physics, vol. 153, p. 044130, 2020.