Stochastic Lag Time Parameterization for Markov State Models of Protein Dynamics.
J Phys Chem B
; 126(46): 9465-9475, 2022 Nov 24.
Article
in English
| MEDLINE | ID: covidwho-2106303
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.
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
SARS-CoV-2
/
COVID-19
Type of study:
Experimental Studies
Limits:
Humans
Language:
English
Journal:
J Phys Chem B
Journal subject:
Chemistry
Year:
2022
Document Type:
Article
Affiliation country:
Acs.jpcb.2c03711
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