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• W3193101527 abstract "Markov state models (MSMs) have become one of the preferred methods for the analysis and interpretation of molecular dynamics (MD) simulations of conformational transitions in biopolymers. While there is great variation in terms of implementation, a well-defined workflow involving multiple steps is often adopted. Typically, molecular coordinates are first subjected to dimensionality reduction and then clustered into small “microstates,” which are subsequently lumped into “macrostates” using the information from the slowest eigenmodes. However, the microstate dynamics is often non-Markovian, and long lag times are required to converge the relevant slow dynamics in the MSM. Here, we propose a variation on this typical workflow, taking advantage of hierarchical density-based clustering. When applied to simulation data, this type of clustering separates high population regions of conformational space from others that are rarely visited. In this way, density-based clustering naturally implements assignment of the data based on transitions between metastable states, resulting in a core-set MSM. As a result, the state definition becomes more consistent with the assumption of Markovianity, and the timescales of the slow dynamics of the system are recovered more effectively. We present results of this simplified workflow for a model potential and MD simulations of the alanine dipeptide and the FiP35 WW domain." @default.
• W3193101527 created "2021-08-16" @default.
• W3193101527 creator A5038379755 @default.
• W3193101527 creator A5054817549 @default.
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• W3193101527 date "2021-08-02" @default.
• W3193101527 modified "2023-10-16" @default.
• W3193101527 title "Markov state models from hierarchical density-based assignment" @default.
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• W3193101527 doi "https://doi.org/10.1063/5.0056748" @default.
• W3193101527 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/34364321" @default.
• W3193101527 hasPublicationYear "2021" @default.
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