Using Markov State Models to Study Self-Assembly

Markov state models (MSMs) have been demonstrated to be a powerful method for computationally studying intramolecular processes such as protein folding and macromolecular conformational changes. In this article, we present a new approach to construct MSMs that is applicable to modeling a broad class...

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Bibliographic Details
Published in:arXiv.org 2014-02
Main Authors: Perkett, Matthew R, Hagan, Michael F
Format: Article
Language:English
Subjects:
Computer simulation
Markov chains
Proteins
Self-assembly
Viruses
Online Access:Get full text
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Summary:Markov state models (MSMs) have been demonstrated to be a powerful method for computationally studying intramolecular processes such as protein folding and macromolecular conformational changes. In this article, we present a new approach to construct MSMs that is applicable to modeling a broad class of multi-molecular assembly reactions. Distinct structures formed during assembly are distinguished by their undirected graphs, which are defined by strong subunit interactions. Spatial inhomogeneities of free subunits are accounted for using a recently developed Gaussian-based signature. Simplifications to this state identification are also investigated. The feasibility of this approach is demonstrated on two different coarse-grained models for virus self-assembly. We find good agreement between the dynamics predicted by the MSMs and long, unbiased simulations, and that the MSMs can reduce overall simulation time by orders of magnitude.
ISSN:2331-8422
DOI:10.48550/arxiv.1402.1784