Enhancing water sampling of buried binding sites using nonequilibrium candidate Monte Carlo

Abstract

Water molecules can be found interacting with the surface and within cavities in proteins. However, water exchange between bulk and buried hydration sites can be slow compared to simulation timescales, thus leading to the inefficient sampling of the locations of water. This can pose problems for free energy calculations for computer-aided drug design. Here, we apply a hybrid method that combines nonequilibrium candidate Monte Carlo (NCMC) simulations and molecular dynamics (MD) to enhance sampling of water in specific areas of a system, such as the binding site of a protein. Our approach uses NCMC to gradually remove interactions between a selected water molecule and its environment, then translates the water to a new region, before turning the interactions back on. This approach of gradual removal of interactions, followed by a move and then reintroduction of interactions, allows the environment relax in response to the proposed water translation, improving acceptance of moves and thereby accelerating water exchange and sampling. We validate this approach on several test systems including the ligand-bound MUP-1 and HSP90 proteins with buried crystallographic waters removed. We show that our NCMC/MD method enhances water sampling relative to normal MD when applied to these systems. Thus, this approach provides a strategy to improve water sampling in molecular simulations which may be useful in practical applications in drug discovery and biomolecular design.

Enhancing Sampling of Water Rehydration on Ligand Binding: A Comparison of Techniques

“…BLUES combines nonequilibrium candidate Monte Carlo (NCMC) 33 with classical MD simulations to enhance the sampling of important degrees of freedom in ligand binding. 32, 54–57 One advantage of using NCMC moves in water sampling is they can efficiently hop water molecules between energy basins and the likelihood of these moves is independent of the barrier heights which is normally a challenge in conventional MD simulations. The details of theory and implementation of BLUES in water sampling can be found in prior work.…”

“…A variety of methods seek to advance the knowledge of optimal placement of water molecules and facilitate binding free energy calculations. 21–32 Among these methods, we are especially interested in two: nonequilibrium candidate Monte Carlo (NCMC) 33 which efficiently hops water molecules between energy basins, and grand canonical Monte Carlo (GCMC) 34–37 which allows the fluctuations in the number of water molecules in a simulation according to a specified chemical potential. Both methods show promise in improving water sampling in molecular simulations and GCMC has shown the ability to incorporate the thermodynamics of buried water in binding free energy calculations.…”

“…Both methods show promise in improving water sampling in molecular simulations and GCMC has shown the ability to incorporate the thermodynamics of buried water in binding free energy calculations. 31, 32, 38–40 Ben-Shalom et al 30, 41 recently studied a Monte Carlo (MC)/MD hybrid approach and found robust sampling of buried hydration sites and improved accuracy in relative binding free energy calculations. However, this approach was implemented in a different simulation engine (AMBER package 42 ) than the one used in this work (OpenMM 43 ) for MD, NCMC and GCMC.…”

Fast Equilibration of Water between Buried Sites and Bulk by MD with Parallel Monte Carlo Water Moves on GPUs

“…Although we took several algorithmic measures to speed the MC/MD method, 24 integration of the MC steps with MD nonetheless markedly slowed the simulations in our initial implementation. We have also collaboratively explored the use of nonequilibrium candidate MC (NCMC) moves to increase the acceptance rate of the large translational MC water moves in this approach, 25 but efficiency remains a concern.…”

Fast Equilibration of Water between Buried Sites and Bulk by MD with Parallel Monte Carlo Water Moves on GPUs

“…Although we took several algorithmic measures to speed the MC/MD method, 15 integration of the MC steps with MD nonetheless markedly slowed the simulations in our initial implementation. We have also collaboratively explored the use of nonequilibrium candidate MC (NCMC) moves to increase the acceptance rate of the large translational MC water moves in this approach, 17 but efficiency remains a concern.…”