Month: January 2020

Congrats to Hongbin, Yunhui and Asghar on the publication our new paper on inferring conformational ensembles from MD and HDX, now out in the Journal of Chemical Theory and Computation! In this work, we used biased molecular dynamics (MD) simulations to construct a series of Markov Models which are then refined against hydrogen deuterium exchange (HDX) protection factor data using our Bayesian Inference of Conformational Populations (BICePs) algorithm. As part of this work, we also had to develop a new model to predict HDX protection factors from molecular simulation data. As an example, we apply this approach to modeling the partially disordered regions of apomyoglobin.

You can read the full paper here: https://pubs.acs.org/doi/10.1021/acs.jctc.9b01240

Hot off the press is our new paper on adaptive seeding in Journal of Chemical Physics. Congrats, Hongbin!

Hongbin Wan and Vincent A. Voelz. Adaptive Markov state model estimation using short reseeding trajectories. J. Chem. Phys. 152, 024103 (2020); https://doi.org/10.1063/1.5142457

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 time scales. A promising approach to enhanced sampling of MSMs is to use “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 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.