  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.
