RT Journal Article
SR Electronic
T1 Markov state models from hierarchical density-based assignment
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2021.05.13.444064
DO 10.1101/2021.05.13.444064
A1 Ion Mitxelena
A1 Xabier López
A1 David de Sancho
YR 2021
UL http://biorxiv.org/content/early/2021/05/16/2021.05.13.444064.abstract
AB 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 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. 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.Competing Interest StatementThe authors have declared no competing interest.