Improving the Efficiency of Non-equilibrium Sampling in the Aqueous Environment via Implicit-Solvent Simulations

By means of estimators based on non-equilibrium work, equilibrium free energy differences or potentials of mean force (PMFs) of a system of interest can be computed from biased molecular dynamics (MD) simulations. The approach, however, is often plagued by slow conformational sampling and poor convergence, especially when the solvent effects are taken into account. Here, as a possible way to alleviate the problem, several widely used implicit-solvent models, which are derived from the analytic generalized Born (GB) equation and implemented in the AMBER suite of programs, were employed in free energy calculations based on non-equilibrium work and evaluated for their abilities to emulate explicit water. As a test case, pulling MD simulations were carried out on an alanine polypeptide with different solvent models and protocols, followed by comparisons of the reconstructed PMF profiles along the unfolding coordinate. The results show that when employing the non-equilibrium work method, sampling with an implicit-solvent model is several times faster and, more importantly, converges more rapidly than that with explicit water due to reduction of dissipation. Among the assessed GB models, the Neck variants outperform the OBC and HCT variants in terms of accuracy, whereas their computational costs are comparable. In addition, for the best-performing models, the impact of the solvent-accessible surface area (SASA) dependent nonpolar solvation term was also examined. The present study highlights the advantages of implicit-solvent models for non-equilibrium sampling.