Enhanced Grand Canonical Sampling of Occluded Water Sites Using Nonequilibrium Candidate Monte Carlo

Water molecules play a key role in biomolecular systems, particularly when bound at protein-ligand interfaces. Simulation studies are hampered by the relatively long timescales on which water exchange between protein and solvent can take place. Grand canonical Monte Carlo (GCMC) is a simulation technique which avoids this issue by attempting the direct insertion and deletion of water molecules. GCMC is, however, hampered by low acceptance probabilities for insertions in congested systems. To address this issue, here, we combine GCMC with nonequilibrium candidate Monte Carlo (NCMC) to yield a new method, grand canonical nonequilibrium candidate Monte Carlo (GCNCMC), in which water insertions and deletions are carried out in a gradual, nonequilibrium fashion. We compare GCNCMC and GCMC simulations of bulk water, and three protein binding sites. We find the efficiency of water sampling is improved by GCNCMC, and that increased sampling of bound ligand conformations is also observed.