Your browser doesn't support javascript.
Stochastic Lag Time Parameterization for Markov State Models of Protein Dynamics.
Gong, Shiqi; He, Xinheng; Meng, Qi; Ma, Zhiming; Shao, Bin; Wang, Tong; Liu, Tie-Yan.
  • Gong S; Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Zhongguancun East Road, Beijing100190, China.
  • He X; University of Chinese Academy of Sciences, No. 19 Yuquan Road, Beijing100049, China.
  • Meng Q; Microsoft Research AI4Science, Beijing100080, China.
  • Ma Z; University of Chinese Academy of Sciences, No. 19 Yuquan Road, Beijing100049, China.
  • Shao B; Microsoft Research AI4Science, Beijing100080, China.
  • Wang T; The CAS Key Laboratory of Receptor Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai201203, China.
  • Liu TY; Microsoft Research AI4Science, Beijing100080, China.
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.
Subject(s)

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

Similar

MEDLINE

...
LILACS

LIS


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