title: Do crystal structures obviate the need for theoretical models of GPCRs for structure-based virtual screening?
abstract: Recent highly expected structural characterizations of agonist-bound and antagonist-bound beta-2 adrenoreceptor (β2AR) by X-ray crystallography have been widely regarded as critical advances to enable more effective structure-based discovery of GPCRs ligands. It appears that this very important development may have undermined many previous efforts to develop 3D theoretical models of GPCRs. To address this question directly, we have compared several historical β2AR models versus the inactive state and nanobody-stabilized active state of β2AR crystal structures in terms of their structural similarity and effectiveness of use in virtual screening for β2AR specific agonists and antagonists. Theoretical models, incluing both homology and de novo types, were collected from five different groups who have published extensively in the field of GPCRs modeling. All models were built before X-ray structures became available. In general, β2AR theoretical models differ significantly from the crystal structure in terms of TMH definition and the global packing. Nevertheless, surprisingly, several models afforded hit rates resulting from virtual screening of large chemical library enriched by known β2AR ligands that exceeded those using X-ray structures. The hit rates were particularly higher for agonists. Furthemore, the screening performance of models is associated with local structural quality, such as the RMSDs for binding pocket residues and the ability to capture accurately, most if not all critical protein/ligand interactions. These results suggest that carefully built models of GPCRs could capture critical chemical and structural features of the binding pocket, and thus may be even more useful for practical structure-based drug discovery than X-ray structures.
authors.affiliation: Laboratory for Molecular Modeling, Division of Medicinal Chemistry and Natural Products and Carolina Exploratory Center for Cheminformatics Research, School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599-7360, USA.
authors.forename: Hao
authors.initials: H
authors.lastname: Tang
authors.forename: Xiang Simon
authors.initials: XS
authors.lastname: Wang
authors.forename: Jui-Hua
authors.initials: JH
authors.lastname: Hsieh
authors.forename: Alexander
authors.initials: A
authors.lastname: Tropsha
mesh_terms: D058666:Adrenergic beta-2 Receptor Agonists / Q000737:chemistry / Q000378:metabolism; D058672:Adrenergic beta-2 Receptor Antagonists / Q000737:chemistry / Q000378:metabolism; D000595:Amino Acid Sequence; D001665:Binding Sites; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D018360:Crystallography, X-Ray; D055808:Drug Discovery; D006801:Humans; D008958:Models, Molecular; D056004:Molecular Dynamics Simulation; D008969:Molecular Sequence Data; D012372:ROC Curve; D018343:Receptors, Adrenergic, beta-2 / Q000737:chemistry* / Q000378:metabolism; D043562:Receptors, G-Protein-Coupled / Q000737:chemistry* / Q000378:metabolism; D016415:Sequence Alignment
chemical_list: D058666:Adrenergic beta-2 Receptor Agonists; D058672:Adrenergic beta-2 Receptor Antagonists; D018343:Receptors, Adrenergic, beta-2; D043562:Receptors, G-Protein-Coupled