Adaptive Markov State Model estimation using short reseeding trajectoriesIn 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.
arxiv.org 
