Function-specific virtual screening for GPCR ligands using a combined scoring method

• DOI: 10.1038/srep28288
• Publication Date: 2016-06-24T09:14:42Z
• Authors: Albert J. Kooistra; Henry F. Vischer; Daniel McNaught-F...; Rob Leurs; Iwan J. P. de Esch; Chris de Graaf
• Keywords: Models, Molecular; 0303 health sciences; Binding Sites; Adrenergic beta-Antagonists; Drug Evaluation, Preclinical; Adrenergic beta-Agonists; Crystallography, X-Ray; Ligands; Article; Recombinant Proteins; Receptors, G-Protein-Coupled; Histamine Agonists; Molecular Docking Simulation; Radioligand Assay; 03 medical and health sciences; HEK293 Cells; Drug Discovery; Receptors, Adrenergic, beta; Histamine H1 Antagonists; Humans; Computer Simulation; SDG 7 - Affordable and Clean Energy; Receptors, Histamine H1; Protein Binding

AbstractThe ability of scoring functions to correctly select and rank docking poses of small molecules in protein binding sites is highly target dependent, which presents a challenge for structure-based drug discovery. Here we describe a virtual screening method that combines an energy-based docking scoring function with a molecular interaction fingerprint (IFP) to identify new ligands based on G protein-coupled receptor (GPCR) crystal structures. The consensus scoring method is prospectively evaluated by: 1) the discovery of chemically novel, fragment-like, high affinity histamine H1 receptor (H1R) antagonists/inverse agonists, 2) the selective structure-based identification of ß2-adrenoceptor (ß2R) agonists and 3) the experimental validation and comparison of the combined and individual scoring approaches. Systematic retrospective virtual screening simulations allowed the definition of scoring cut-offs for the identification of H1R and ß2R ligands and the selection of an optimal ß-adrenoceptor crystal structure for the discrimination between ß2R agonists and antagonists. The consensus approach resulted in the experimental validation of 53% of the ß2R and 73% of the H1R virtual screening hits with up to nanomolar affinities and potencies. The selective identification of ß2R agonists shows the possibilities of structure-based prediction of GPCR ligand function by integrating protein-ligand binding mode information.