A divide-and-conquer strategy to improve diffusion sampling in generalized ensemble simulations

Abstract

Generalized ensemble simulations generally suffer from the associated diffusion-sampling problem; the increased entropic barrier can greatly abolish sampling efficiency, in particular, with the increase of number of degrees of freedom in the target conformational space. Taking advantage of the recent simulated scaling method, we formulate a divide-and-conquer sampling strategy to solve this problem so as to robustly improve the sampling efficiency in generalized ensemble simulations. In the present method, the target conformational space sampling enhancement is decomposed to the sampling enhancements of several subconformational regions, and multiple independent SS simulations are performed to establish the individual sampling enhancement for each of the subconformational regions; in order to realize the global importance sampling, structure exchanges among these replicas are performed based on the Monte Carlo acceptance/rejection procedure. As demonstrated in our studies, the present divide-and-conquer sampling algorithm, named by us as "simulated scaling based variant Hamiltonian replica exchange method," has superior sampling capability so as to possibly play an essential role in dealing with the present bottleneck of generalized ensemble method developments: the system size limitations.