title: Using Markov state models to study self-assembly.
abstract: 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.
authors.affiliation: Martin Fisher School of Physics, Brandeis University, Waltham, Massachusetts 02474, USA.
authors.forename: Matthew R
authors.initials: MR
authors.lastname: Perkett
authors.affiliation: Martin Fisher School of Physics, Brandeis University, Waltham, Massachusetts 02474, USA.
authors.forename: Michael F
authors.initials: MF
authors.lastname: Hagan
mesh_terms: D008390:Markov Chains; D056004:Molecular Dynamics Simulation; D053758:Nanoparticles / Q000737:chemistry*; D011108:Polymers / Q000737:chemistry*; D017510:Protein Folding; D017434:Protein Structure, Tertiary; D011506:Proteins / Q000737:chemistry*
chemical_list: D011108:Polymers; D011506:Proteins