Abstract
Markov state models (MSMs) have become one of the most important
techniques for understanding biomolecular transitions from classical
molecular dynamics (MD) simulations. MSMs provide a systematized way
of accessing the long time kinetics of the system of interest from
the short-timescale microscopic transitions observed in simulation
trajectories. At the same time, they provide a consistent description
of the equilibrium and dynamical properties of the system of interest,
and they are ideally suited for comparisons against experiment. A few
software packages exist for building MSMs, which have been widely
adopted. Here we introduce MasterMSM, a new Python package
that uses the master equation formulation of MSMs and provides a
number of new algorithms for building and analyzing these models.
We demonstrate some of the most distinctive features of the package,
including the estimation of rates, definition of core-sets for
transition based assignment of states, the estimation of committors
and fluxes, and the sensitivity analysis of the emerging networks.
The package is available at https://github.com/daviddesancho/MasterMSM.