Adaptive Markov State Model estimation using short reseeding trajectories

TL;DR

This paper evaluates adaptive Markov State Model estimation using short reseeding trajectories, highlighting practical challenges and proposing reweighting methods to improve thermodynamic and kinetic estimates in biomolecular simulations.

Contribution

It introduces a simple reweighting approach for reseeding trajectories to enhance MSM estimation accuracy, addressing challenges in adaptive sampling methods.

Findings

Reweighting improves thermodynamic estimates.

Reseeding trajectories face practical challenges.

Method applied to protein folding MSMs.

Abstract

In the last decade, advances in molecular dynamics (MD) and Markov State Model (MSM) methodologies have made possible accurate and efficient estimation of kinetic rates and reactive pathways for complex biomolecular dynamics occurring on slow timescales. A promising approach to enhanced sampling of MSMs is to use so-called "adaptive" methods, in which new MD trajectories are "seeded" preferentially from previously identified states. Here, we investigate the performance of various MSM estimators applied to reseeding trajectory data, for both a simple 1D free energy landscape, and for mini-protein folding MSMs of WW domain and NTL9(1-39). Our results reveal the practical challenges of reseeding simulations, and suggest a simple way to reweight seeding trajectory data to better estimate both thermodynamic and kinetic quantities.