Abstract
The characterization of the thermodynamics and time-dependent kinetics is of particular interest in protein-protein association and dissociation. Markov State Models (MSMs) provide a powerful framework to characterize the dynamics of complex molecular systems by constructing a reduced stochastic model of the full system and defining a set of conformational featured microstates to determine the matrix of transition probabilities between them. Here, a systematic examination of a variety of MSMs methodologies is conducted using different combinations of input featurization and simulation methods to study how the robustness and the quality of the information generated from MSMs for protein association is affected.
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