The Predicted 3D Structure of Human DP Prostaglandin G Protein-Coupled Receptor Bound to CPI Antagonist

Abstract

Prostaglandins play a critical physiological role in both cardiovascular and immune systems, acting through their interactions with 9 prostanoid G protein-coupled receptors (GPCRs). These receptors are important therapeutic targets for a variety of diseases including arthritis, allergies, type 2 diabetes, and cancer. The DP prostaglandin receptor is of interest because it has unique structural and physiological properties. Most notably, DP does not have the 3–6 ionic lock common to Class A GPCRs. However, the lack of X-ray structures for any of the 9 prostaglandin GPCRs hampers the application of structure-based drug design methods to develop more selective and active medications to specific receptors. We predict here 3D structures for the DP prostaglandin GPCR, based on the GEnSeMBLE complete sampling with hierarchical scoring (CS-HS) methodology. This involves evaluating the energy of 13 trillion packings to finally select the best 20 that are stable enough to be relevant for binding to antagonists, agonists, and modulators. To validate the predicted structures, we predict the binding site for the Merck cyclopentanoindole (CPI) selective antagonist docked to DP. We find that the CPI binds vertically in the 1–2–7 binding pocket, interacting favorably with residues R310^(7.40) and K76^(2.54) with additional interactions with S313^(7.43), S316^(7.46), S19^(1.35), etc. This binding site differs significantly from that of antagonists to known Class A GPCRs where the ligand binds in the 3–4–5–6 region. We find that the predicted binding site leads to reasonable agreement with experimental Structure–Activity Relationship (SAR). We suggest additional mutation experiments including K76^(2.54), E129^(3.49), L123^(3.43), M270^(6.40), F274^(6.44) to further validate the structure, function, and activation mechanism of receptors in the prostaglandin family. Our structures and binding sites are largely consistent and improve upon the predictions by Li et al. ( J. Am. Chem. Soc. 2007, 129 (35), 10720) that used our earlier MembStruk prediction methodology.

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