Enhancing Water Sampling in Free Energy Calculations with Grand Canonical Monte Carlo

The prediction of protein–ligand
binding affinities using
free energy perturbation (FEP) is becoming increasingly routine in
structure-based drug discovery. Most FEP packages use molecular dynamics
(MD) to sample the configurations of proteins and ligands, as MD is
well-suited to capturing coupled motion. However, MD can be prohibitively
inefficient at sampling water molecules that are buried within binding
sites, which has severely limited the domain of applicability of FEP
and its prospective usage in drug discovery. In this paper, we present
an advancement of FEP that augments MD with grand canonical Monte
Carlo (GCMC), an enhanced sampling method, to overcome the problem
of sampling water. We accomplished this without degrading computational
performance. On both old and newly assembled data sets of protein–ligand
complexes, we show that the use of GCMC in FEP is essential for accurate
and robust predictions for ligand perturbations that disrupt buried
water.