The high dimensional trajectories of atomic positions were first condensed to time series of all the dihedrals in the solute motor's topology file, each associated with a forcefield term. The time series were calculated at intervals of 0.8ps. This interval was also used as the lag time, i.e., the time resolution of the MSM. We also tested lag times up to 80 ps, and found that longer lag times did not significantly change the computed rate constants. Then the number of dimensions were further reduced to 8 for Motor S, and 19 for Motor N, by using time-lagged independent component analysis (TICA) on the time series. The number of dimensions was chosed as the minimal number that still preserves high VAMP2 score. Next, the continuous time series were discretized to 64 conformational clusters using k-means clustering. Then the clusters were classified by Perron cluster analysis (PCCA) into four metastable substates. We used sharp classification instead of fuzzy probabilistic classification, so that pairs of substates that do not interconvert directly receive transition counts of exactly zero rather than small numbers. Then the MSM, as a 4x4 transition matrix, was computed from the time series of interconversion among these four substates. The Chapman-Kolmogorov test was conducted which validates the MSM. Our workflow is available in a more detailed and easily reproducible format, as Jupyter notebooks in our Github repository.