Integrating Path Sampling with Enhanced Sampling for Rare-event Kinetics

Abstract

Studying the kinetics of long-timescale rare events is a fundamental challenge in molecular simulation. To address this problem, we propose an integration of two different rare-event sampling philosophies: biased enhanced sampling and unbiased path sampling. Enhanced sampling methods e.g. metadynamics can facilitate enthalpic barrier crossing by applying an external bias potential. On the contrary, path sampling methods like weighted ensemble (WE) lack explicit mechanisms to overcome energetic barriers. However, they can accelerate the exploration of rugged free energy surfaces through trajectory resampling. We show that a judicious combination of the weighted ensemble with a metadynamics-like algorithm, can synergize the strengths and mitigate the deficiencies of path sampling and enhanced sampling approaches. The resulting integrated sampling (IS) algorithm improves the computational efficiency of calculating the kinetics of peptide conformational transitions, protein unfolding, and the dissociation of a ligand-receptor complex. Furthermore, the IS approach can direct sampling along the minimum free energy pathway even when the collective variable used for biasing is suboptimal. These advantages make the integrated sampling algorithm suitable for studying the kinetics of complex molecular systems of biological and pharmaceutical relevance.