Reducing weighted ensemble variance with optimal trajectory management.

Weighted ensemble (WE) is a path-sampling method that is conceptually simple, widely applicable, and statistically unbiased. In a WE simulation, an ensemble of trajectories is periodically pruned or replicated to enhance the sampling of rare transitions and improve the estimation of mean first-passage times (MFPTs). However, poor choices of the parameters governing pruning and replication can lead to high variance in MFPT estimates. Our previous work [Aristoff et al., J. Chem. Phys. 158, 014108 (2023)] presented an optimal WE parameterization strategy and applied it to low-dimensional example systems. The strategy harnesses estimated local MFPTs from different initial configurations to a single target state. In the present work, we apply the optimal parameterization strategy to more challenging high-dimensional molecular models, namely, synthetic molecular dynamics (MD) models of Trp-cage folding and unfolding, as well as atomistic MD models of NTL9 folding in high-friction and low-friction continuum solvents. In each system, we use WE to estimate the MFPT for folding or unfolding events. We show that the optimal parameterization reduces the variance of MFPT estimates in three of four systems, with a dramatic improvement in the most challenging atomistic system. Overall, the parameterization strategy improves the accuracy and reliability of WE estimates for the kinetics of biophysical processes.