Markov State Modeling (MSM) has been used successfully to model the conformational dynamics of proteins . An MSM is a transition matrix that describes the conditional probability of a system transitioning to some state j given that it is in state i, after a given lag time τ . These are then combined to give the distribution of protein conformations, e.g. catalytically active and inactive. The system is treated as memory-less, meaning that the transition probabilities only depend on the current state of the system, and not any previously visited states . A benefit of MSMs is that models of protein dynamics can be built efficiently from multiple short MD trajectories, making use of parallel computing .