Markov State Models with Weighted Ensemble Simulation: How to Eliminate the Trajectory Merging Bias.

ABSTRACT: The weighted ensemble (WE) algorithm is gaining popularity as a rare event method for studying long timescale processes with molecular dynamics. WE is particularly useful for determining kinetic properties, such as rates of protein (un)folding and ligand (un)binding, where transition rates can be calculated from the flux of trajectories into a target basin of interest. However, this flux depends exponentially on the number of splitting events that a given trajectory experiences before reaching the target state and can vary by orders of magnitude between WE replicates. Markov state models (MSMs) are helpful tools to aggregate information across multiple WE simulations and have previously been shown to provide more accurate transition rates than WE alone. Discrete-time MSMs are models that c

SUBMITTER: Bose S 

PROVIDER: S-EPMC11866749 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature