Proteins Structure Function and Bioinformatics · 2002 · 296 citations · 51 references
PharmacyBioinformaticsPharmacologyDrug DiscoveryPharmacophore ModelingStructural BiologyLigand BindingMolecular DockingPharmacophore ScreeningProtein-ligand DockingReceptor ModelingVirtual ScreeningG Protein-coupled Receptor ModelingG-protein-coupled ReceptorsHomology-based PredictionPharmacophore Model ConstructionPharmacophore-guided DockingProtein Conformational ChangesReceptor Binding ModelsReceptors
The study investigates whether bovine‑rhodopsin‑based homology models of GPCRs are reliable for virtual screening and develops improved models for agonist detection. Homology models of antagonist‑bound D3, M1, and V1a receptors were built from the 2.8 Å rhodopsin crystal structure and screened with Dock, FlexX, and Gold using seven scoring functions, while a new knowledge‑ and pharmacophore‑based method was devised to generate agonist‑bound models. Rhodopsin‑based models effectively distinguished known antagonists from random compounds but failed to retrieve agonists, whereas the new knowledge‑based models succeeded in agonist screening.
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).
51
Julie Thompson, Desmond G. Higgins, Toby J. Gibson · Nucleic Acids Research · 1994 · 64.9K citations · Full text
Crystal Structure of Rhodopsin: A G Protein-Coupled Receptor
Krzysztof Palczewski, Takashi Kumasaka, Tetsuya Hori et al. · Science · 2000 · 5.6K citations