Markov State Models Based on Milestoning

Markov State Models (MSMs) have become the tool of choice to analyze large amounts of molec- ular dynamics data by approximating them as a Markov jump process between suitably predefined states. Here we investigate ”Core Set MSMs”, a new type of MSMs that builds on metastable core sets acting as milestones for tracing the rare event kinetics. We present a thorough analysis of Core Set MSMs based on the existing milestoning framework, Bayesian estimation methods and Transi- tion Path Theory (TPT). As a result, Core Set MSMs can now be used to extract phenomenological rate constants between the metastable sets of the system and to approximate the evolution of certain key observables. The performance of Core Set MSMs in comparison to standard MSMs is analyzed and illustrated on a model potential and the torsion angle dynamics of Alanine dipeptide