Chemistry

Inherent structure versus geometric metric for state space discretization

Document Type

Article

Abstract

Inherent structure (IS) and geometry-based clustering methods are commonly used for analyzing molecular dynamics trajectories. ISs are obtained by minimizing the sampled conformations into local minima on potential/effective energy surface. The conformations that are minimized into the same energy basin belong to one cluster. We investigate the influence of the applications of these two methods of trajectory decomposition on our understanding of the thermodynamics and kinetics of alanine tetrapeptide. We find that at the microcluster level, the IS approach and root-mean-square deviation (RMSD)-based clustering method give totally different results. Depending on the local features of energy landscape, the conformations with close RMSDs can be minimized into different minima, while the conformations with large RMSDs could be minimized into the same basin. However, the relaxation timescales calculated based on the transition matrices built from the microclusters are similar. The discrepancy at the microcluster level leads to different macroclusters. Although the dynamic models established through both clustering methods are validated approximately Markovian, the IS approach seems to give a meaningful state space discretization at the macrocluster level in terms of conformational features and kinetics.

Publication Title

Journal of Computational Chemistry

Publication Date

5-30-2016

Volume

37

Issue

14

First Page

1251

Last Page

1258

ISSN

0192-8651

DOI

10.1002/jcc.24315

Keywords

clustering method, effective energy surface, inherent structure, Markov state model, mean first passage time

Cross Post Location

Student Publications

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