The 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.

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The human histamine H3 receptor (hH3R) is predominantly expressed in the CNS, where it regulates the synthesis and release of histamine and other neurotransmitters. Due to its neuromodulatory role, the hH3R has been associated with various CNS disorders, including Alzheimer’s and Parkinson’s disease. Markedly, the hH3R gene undergoes extensive splicing, resulting in 20 isoforms, of which 7TM isoforms exhibit variations in the intracellular loop 3 (IL3) and/or C-terminal tail. Particularly, hH3R isoforms that display variations in IL3 (e.g., hH3R-365) are shown to differentially signal via Gαi-dependent pathways upon binding of biased agonists (e.g., immepip, proxifan, imetit). Nevertheless, the mechanisms underlying biased agonism at hH3R isoforms remain unknown. Using a structure–function relationship study with a broad range of H3R agonists, we thereby explored determinants underlying isoform bias at hH3R isoforms that exhibit variations in IL3 (i.e., hH3R-445, -415, -365, and -329) in a Gαi-dependent pathway (cAMP inhibition). Hence, we systematically characterized hH3R isoforms on isoform bias by comparing various ligand properties (i.e., structural and molecular) to the degree of isoform bias. Importantly, our study provides novel insights into the structural and molecular basis of receptor isoform bias, highlighting the importance to study GPCRs with multiple isoforms to better tailor drugs.