Using PyEMMA (v2.5.5), we analyzed and built Markov State Models (MSM) separately between our MD and BLUES simulations. MD simulation frames were stored every 25,000 steps (100ps/frame) and BLUES simulation frames were stored every 10,000 steps (40ps/frame). In this study, we apply a method called time-lagged indepdent component analysis (TICA) with perron-cluster cluster analysis (PCCA) to define our metastable binding modes sampled during our simulation. Here, we analyzed our simulations using the coordinates of the ligand heavy atoms as our chosen feature set. In Figure , the appropriate lagtime for TICA was chosen by performing an analysis on the VAMP2 score and varying the TICA dimensions parameter for several lag times. VAMP2 score measures the kinetic variance in our chosen feature set. Here, we chose a lag time of 1ns to discretize our MD simulations, but lag times below 1ns would have been an appropriate choices as well. For our : VAMP2 scores analysis for 8NY from our MD simulations. This plots the number of TICA dimensions against the VAMP2 scores for several lag times (nanoseconds). At lag times above 1ns we do not observe as large of an increase in the VAMP2 score when using more than four dimensions, i.e. the first four dimensions contains most of the relevant information of the slow dynamics.