We built all MSMs in this study using the PyEMMA (v2.5.12) and deeptime (v.0.4.4) Python packages. We started by choosing features along the F-helix region of the protein (residues 107-115) to characterize the changes in the T4 L99A binding pocket. Using MD-Traj (v.1.9.9), we calculated the pairwise distance between all C α -C α and C β -C β pairs for all residues in the F-helix for a total of 51 distances. We used an implementation of TICA from deeptime (v0.4.4), selecting a TICA lag time of 0.02 ns (10 frames) to transform the set of features into coordinates that described the slowest motions of the system. We then applied k-means clustering on the projection of the trajectory frames onto the two largest TICA components in order to resolve metastable states for each system. We selected a lag time of τ = 0.5 ns (250 frames) to construct each MSM; this was the shortest lag time beyond which the implied timescales for each system plateaued (Figure ).