Modeling of human prokineticin receptors: interactions with novel small-molecule binders and potential off-target drugs.

Background and Motivation: The Prokineticin receptor (PKR) 1 and 2 subtypes are novel members of family A GPCRs, which exhibit an unusually high degree of sequence similarity. Prokineticins (PKs), their cognate ligands, are small secreted proteins of ∼80 amino acids; however, non-peptidic low-molecular weight antagonists have also been identified. PKs and their receptors play important roles under various physiological conditions such as maintaining circadian rhythm and pain perception, as well as regulating angiogenesis and modulating immunity. Identifying binding sites for known antagonists and for additional potential binders will facilitate studying and regulating these novel receptors. Blocking PKRs may serve as a therapeutic tool for various diseases, including acute pain, inflammation and cancer.
Methods and Results: Ligand-based pharmacophore models were derived from known antagonists, and virtual screening performed on the DrugBank dataset identified potential human PKR (hPKR) ligands with novel scaffolds. Interestingly, these included several HIV protease inhibitors for which endothelial cell dysfunction is a documented side effect. Our results suggest that the side effects might be due to inhibition of the PKR signaling pathway. Docking of known binders to a 3D homology model of hPKR1 is in agreement with the well-established canonical TM-bundle binding site of family A GPCRs. Furthermore, the docking results highlight residues that may form specific contacts with the ligands. These contacts provide structural explanation for the importance of several chemical features that were obtained from the structure-activity analysis of known binders. With the exception of a single loop residue that might be perused in the future for obtaining subtype-specific regulation, the results suggest an identical TM-bundle binding site for hPKR1 and hPKR2. In addition, analysis of the intracellular regions highlights variable regions that may provide subtype specificity.