Markov state models (MSMs) are network representations of a protein's free-energy landscape, providing map representations of protein conformational space with thermodynamic and kinetic properties taken from equilibrium simulations . Rather than depending on a single long simulation that explores multiple states sequentially, MSMs are capable of stitching together multiple short trajectories into a single unified landscape. Thus MSMs capture slow events, and their intermediates, far beyond the reach of any individual simulation. Thanks to distributed networks like Folding@home, gathering large numbers of short trajectories is tractable in a reasonable time-frame. Many reviews have been dedicated to providing accessible and in-depth explanations of MSM technology . MSMs require two components to describe the dynamics of a biomolecular system: (i): A discretization of a high dimensional state space into n conformational states, and (ii) A model of the stochastic transitions between each states, represented as a transition matrix P. The probability of transitioning from state i to state j (P ij ) is: