Application of Free Energy Perturbation (FEP+) to Understanding Ligand Selectivity: A Case Study to Assess Selectivity Between Pairs of Phosphodiesterases (PDE's).

Cyclic nucleotide phosphodiesterases (PDE's) are metalloenzymes that play a key role in regulating the levels of the ubiquitous second messengers, cyclic adenosine monophosphate (cAMP) and cyclic guanosine monophosphate (cGMP). In humans, 11 PDE protein families mediate numerous biochemical pathways throughout the body and are effective drug targets for the treatment of diseases ranging from central nervous system disorders to heart and pulmonary diseases. PDE's also share a highly conserved catalytic site (about 50%), thus making the design of selective drug candidates very challenging with classical structure-based design approaches given also the lack of publicly available co-crystal structures of pairs of PDE's with consistent biological data to be compared, as we show in our work. In this retrospective study, we apply free energy perturbation (FEP+) to predict the selectivity of inhibitors that bind two pairs of closely related PDE families: PDE9/1 and PDE5/6 where only 1 co-crystal structure per pair is publicly available. As another challenge, the p Ka of the PDE5/6 inhibitor is close to the experimental pH, making unclear the exact protonation state that should be used in the computational workflow. We demonstrate that running FEP+ on homology models constructed for these metalloenzymes accurately reproduces experimentally observed selectivity profiles also addressing the unclear protonation state to be used during computation with our recently developed p Ka-correction method. Based on these data, we conclude that FEP+ is a robust method for prediction of selectivity for this target class and may be helpful to address related lead optimization challenges in drug discovery.