All Markov State modelling here was performed using pyemma version 2.5.7. The data from apo EPAC1, EPAC1-cAMP, EPAC1 L273W, EPAC1-I942 was pooled together and clustered into 300 microstates using k-means clustering. Each frame of the seeded MD trajectories was then assigned to one of these 300 states, and used to build Markov State Models for each system, with a lag time of 25 ns. The microstates were assigned to 3 metastable states: active, inactive, and intermediate. The state with the lowest domain angle, hinge and PBC RMSD values would be considered the inactive state, while the state with the highest values of these features would be the active. The intermediate state is characterised by a low hinge RMSD value ("inactive"-like) and a high PBC RMSD value ("active"-like), as outlined in Shao et al. Originally, PCCA was applied, however it did not yield a set of states with well-defined state clusters that satisfied the conditions above (Supplementary Figure ). Therefore, the macrostate assignment was done manually. The metastable state centres were assigned based on feature values during equilibrium and seeded MD simulations (see section and Supplementary Table ), and each microstate was assigned to the closest centre. The total macrostate probabilities were computed by summing over the probabilities of the microstates that belong to the macrostate.