@article{gong2022stochastic,
author = {Gong, Shiqi and He, Xinheng and Meng, Qi and Ma, Zhiming and Shao, Bin and Wang, Tong and Liu, Tie-Yan},
title = {Stochastic Lag Time Parameterization for Markov State Models of Protein Dynamics},
year = {2022},
month = {November},
abstract = {Markov state models (MSMs) play a key role in studying protein conformational dynamics. A sliding count window with a fixed lag time is widely used to sample sub-trajectories for transition counting and MSM construction. However, sub-trajectories sampled with a fixed lag time may not perform well under different selections of lag time, which requires strong prior practice and leads to less robust estimation. To alleviate it, we propose a novel stochastic method from a Poisson process to generate perturbative lag time for sub-trajectory sampling and utilize it to construct a Markov chain. Comprehensive evaluations on the double-well system, WW domain, BPTI, and RBD–ACE2 complex of SARS-CoV-2 reveal that our algorithm significantly increases the robustness and power of a constructed MSM without disturbing the Markovian properties. Furthermore, the superiority of our algorithm is amplified for slow dynamic modes in complex biological processes.},
url = {http://approjects.co.za/?big=en-us/research/publication/stochastic-lag-time-parameterization-for-markov-state-models-of-protein-dynamics/},
journal = {The Journal of Physical Chemistry B},
}