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 pKa 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 pKa-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.