A strategy using NMR peptide structures of thromboxane A2 receptor as templates to construct ligand-recognition pocket of prostacyclin receptor
Cheng-Huai Ruan, Jaixin Wu, Ke-He Ruan, Cheng-Huai Ruan, Jaixin Wu, Ke-He Ruan
Abstract
Background: Prostacyclin receptor (IP) and thromboxane A2 receptor (TP) belong to rhodopsin-type G protein-coupling receptors and respectively bind to prostacyclin and thromboxane A2 derived from arachidonic acid. Recently, we have determined the extracellular loop (eLP) structures of the human TP receptor by 2-D 1H NMR spectroscopy using constrained peptides mimicking the individual eLP segments. The studies have identified the segment along with several residues in the eLP domains important to ligand recognition, as well as proposed a ligand recognition pocket for the TP receptor.
Results: The IP receptor shares a similar primary structure in the eLPs with those of the TP receptor. Forty percent residues in the second eLPs of the receptors are identical, which is the major region involved in forming the ligand recognition pocket in the TP receptor. Based on the high homology score, the eLP domains of the IP receptor were constructed by the homology modeling approach using the NMR structures of the TP eLPs as templates, and then configured to the seven transmembrane (TM) domains model constructed using the crystal structure of the bovine rhodopsin as a template. A NMR structure of iloprost was docked into the modeled IP ligand recognition pocket. After dynamic studies, the segments and residues involved in the IP ligand recognition were proposed. A key residue, Arg173 involved in the ligand recognition for the IP receptor, as predicted from the modeling, was confirmed by site-directed mutagenesis.
Conclusion: A 3-D model of the human IP receptor was constructed by homology modeling using the crystal structure of bovine rhodopsin TM domains and the NMR structures of the synthetic constrained peptides of the eLP domains of the TP receptor as templates. This strategy can be applied to molecular modeling and the prediction of ligand recognition pockets for other prostanoid receptors.
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