Enhanced Sampling with Sub-optimal Collective Variables: Reconciling Accuracy and Convergence Speed

Abstract

We introduce an enhanced sampling algorithm to obtain converged free energy landscapes of molecular rare events, even when the collective variable (CV) used for biasing is not optimal. Our approach samples a time-dependent target distribution by combining the On-the-fly probability enhanced sampling (OPES) and its exploratory variant, OPES Explore (OPESe). This promotes more transitions between the relevant metastable states and accelerates the convergence speed of the free energy estimate. This is accomplished We demonstrate the successful application of this combined algorithm on the two-dimensional Wolfe-Quapp potential, millisecond timescale ligand-receptor binding in trypsin-benzamidine complex, and folding-unfolding transitions in chignolin mini-protein. Our proposed algorithm can compute accurate free energies at an affordable computational cost and is robust in terms of the choice of collective variables, making it particularly promising for the simulation of complex biomolecular systems.