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Inherent structure versus geometric metric for state space discretization
Author(s) -
Liu Hanzhong,
Li Minghai,
Fan Jue,
Huo Shuanghong
Publication year - 2016
Publication title -
journal of computational chemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.907
H-Index - 188
eISSN - 1096-987X
pISSN - 0192-8651
DOI - 10.1002/jcc.24315
Subject(s) - maxima and minima , discretization , cluster analysis , potential energy surface , relaxation (psychology) , metric (unit) , statistical physics , mathematics , energy landscape , state space , dihedral angle , trajectory , configuration space , cluster (spacecraft) , computer science , chemistry , physics , thermodynamics , molecule , mathematical analysis , artificial intelligence , psychology , social psychology , operations management , statistics , organic chemistry , economics , programming language , hydrogen bond , quantum mechanics , astronomy
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. © 2016 Wiley Periodicals, Inc.