07.11.23 - Daniel Nagel "Markov State Modeling of Biophysical Dynamics"

Markov State Modeling of Biophysical Dynamics

Markov state models (MSMs) are a popular means of interpreting molecular dynamics trajectories in terms of memoryless transitions between metastable conformational states. In recent years, a robust mathematical framework for MSM has been developed, including a variational principle which states that the MSM giving the slowest implied time scales is the best approximation to the true dynamics. However, optimization using only kinetic
objective functions may conflict with the requirement for high temporal resolution necessary to accurately capture these biomolecular processes.

 
To address this challenge, we revisit the MSM workflow to shift the focus from optimizing time scales alone to finding an optimal trade-off between time scales and spatiotemporal resolution. To this end, we extend and improve several methods of the common workflow to facilitate the identification of biologically relevant conformational states while still accurately capturing the slow time scales of the system.
To demonstrate the strength of our improvements, we apply them to the extensively studied folding of the villin headpiece domain. By evaluating the robustness of our MSM by comparing it to models using alternative methodologies, we are able to identify the key features of the folding process. This allows us not only to propose a reference system for benchmarking MSMs, but also to highlight the critical steps in the overall MSM workflow.