Markov state models from hierarchical density-based assignment
نویسندگان
چکیده
Markov state models (MSMs) have become one of the preferred methods for analysis and interpretation molecular dynamics (MD) simulations conformational transitions in biopolymers. While there is great variation terms implementation, a well-defined workflow involving multiple steps often adopted. Typically, coordinates are first subjected to dimensionality reduction then clustered into small “microstates,” which subsequently lumped “macrostates” using information from slowest eigenmodes. However, microstate non-Markovian, long lag times required converge relevant slow MSM. Here, we propose on this typical workflow, taking advantage hierarchical density-based clustering. When applied simulation data, type clustering separates high population regions space others that rarely visited. In way, naturally implements assignment data based between metastable states, resulting core-set As result, definition becomes more consistent with assumption Markovianity, timescales system recovered effectively. We present results simplified model potential MD alanine dipeptide FiP35 WW domain.
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ژورنال
عنوان ژورنال: Journal of Chemical Physics
سال: 2021
ISSN: ['1520-9032', '1089-7690', '0021-9606']
DOI: https://doi.org/10.1063/5.0056748