Then, these production MD trajectories (whether completed THI or not) were used to construct a reversible Markov state model (MSM) of each motor. The trajectory segments in the stable form after THI were excluded when building the MSM, because the reverse THI process was not sampled. The high dimensional trajectories of atomic positions were first condensed to time series of all the dihedrals present in the topology file of the motor; each dihedral is associated with a force field term. After further dimension-reduction using time-lagged independent component analysis (TICA), the time series in continuous conformational space was discretized to conformational clusters using k-means clustering. Then the clusters were classified by Perron cluster analysis (PCCA) into 4 metastable substates. We chose 4 substates because partitioning the metastable form into a larger number of substates led to some substates being overly similar to each other. The time series of substate classification were then analyzed to produce the MSM, as a 4x4 transition matrix.