title: Protein-based virtual screening of chemical databases. II. Are homology models of G-Protein Coupled Receptors suitable targets?
abstract: The aim of the current study is to investigate whether homology models of G-Protein-Coupled Receptors (GPCRs) that are based on bovine rhodopsin are reliable enough to be used for virtual screening of chemical databases. Starting from the recently described 2.8 A-resolution X-ray structure of bovine rhodopsin, homology models of an "antagonist-bound" form of three human GPCRs (dopamine D3 receptor, muscarinic M1 receptor, vasopressin V1a receptor) were constructed. The homology models were used to screen three-dimensional databases using three different docking programs (Dock, FlexX, Gold) in combination with seven scoring functions (ChemScore, Dock, FlexX, Fresno, Gold, Pmf, Score). Rhodopsin-based homology models turned out to be suitable, indeed, for virtual screening since known antagonists seeded in the test databases could be distinguished from randomly chosen molecules. However, such models are not accurate enough for retrieving known agonists. To generate receptor models better suited for agonist screening, we developed a new knowledge- and pharmacophore-based modeling procedure that might partly simulate the conformational changes occurring in the active site during receptor activation. Receptor coordinates generated by this new procedure are now suitable for agonist screening. We thus propose two alternative strategies for the virtual screening of GPCR ligands, relying on a different set of receptor coordinates (antagonist-bound and agonist-bound states).
authors.affiliation: Laboratoire de Pharmacochimie de la Communication Cellulaire, UMR CNRS 7081, 74 route du Rhin, B.P. 24, F-67401 Illkirch, France.
authors.forename: Caterina
authors.initials: C
authors.lastname: Bissantz
authors.forename: Philippe
authors.initials: P
authors.lastname: Bernard
authors.forename: Marcel
authors.initials: M
authors.lastname: Hibert
authors.forename: Didier
authors.initials: D
authors.lastname: Rognan
mesh_terms: D058666:Adrenergic beta-2 Receptor Agonists; D000465:Algorithms; D000595:Amino Acid Sequence; D000818:Animals; D065092:Antidiuretic Hormone Receptor Antagonists; D002417:Cattle; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D016208:Databases, Factual; D065127:Dopamine D2 Receptor Antagonists; D016503:Drug Delivery Systems; D020962:Heterotrimeric GTP-Binding Proteins / Q000378:metabolism; D006801:Humans; D008024:Ligands; D008958:Models, Molecular; D008969:Molecular Sequence Data; D043643:Receptor, Muscarinic M1; D018343:Receptors, Adrenergic, beta-2 / Q000737:chemistry; D011956:Receptors, Cell Surface / Q000819:agonists / Q000037:antagonists & inhibitors / Q000737:chemistry; D017448:Receptors, Dopamine D2 / Q000819:agonists / Q000737:chemistry; D050637:Receptors, Dopamine D3; D011976:Receptors, Muscarinic / Q000737:chemistry; D017465:Receptors, Opioid, delta / Q000819:agonists / Q000737:chemistry; D017483:Receptors, Vasopressin / Q000737:chemistry; D012243:Rhodopsin / Q000737:chemistry; D016415:Sequence Alignment; D017386:Sequence Homology, Amino Acid
chemical_list: D058666:Adrenergic beta-2 Receptor Agonists; D065092:Antidiuretic Hormone Receptor Antagonists; C493593:DRD3 protein, human; D065127:Dopamine D2 Receptor Antagonists; D008024:Ligands; D043643:Receptor, Muscarinic M1; D018343:Receptors, Adrenergic, beta-2; D011956:Receptors, Cell Surface; D017448:Receptors, Dopamine D2; D050637:Receptors, Dopamine D3; D011976:Receptors, Muscarinic; D017465:Receptors, Opioid, delta; D017483:Receptors, Vasopressin; D012243:Rhodopsin; D020962:Heterotrimeric GTP-Binding Proteins