Carvedilol is among the most effective β-blockers for improving survival after myocardial infarction. Yet the mechanisms by which carvedilol achieves this superior clinical profile are still unclear. Beyond blockade of β1-adrenoceptors, arrestin-biased signalling via β2-adrenoceptors is a molecular mechanism proposed to explain the survival benefits. Here, we offer an alternative mechanism to rationalize carvedilol’s cellular signalling. Using primary and immortalized cells genome-edited by CRISPR/Cas9 to lack either G proteins or arrestins; and combining biological, biochemical, and signalling assays with molecular dynamics simulations, we demonstrate that G proteins drive all detectable carvedilol signalling through β2ARs. Because a clear understanding of how drugs act is imperative to data interpretation in basic and clinical research, to the stratification of clinical trials or to the monitoring of drug effects on the target pathway, the mechanistic insight gained here provides a foundation for the rational development of signalling prototypes that target the β-adrenoceptor system.

G-protein-coupled receptor (GPCR) is an important target class of proteins for drug discovery, with over 27% of FDA-approved drugs targeting GPCRs. However, being a membrane protein, it is difficult to obtain the 3D crystal structures of GPCRs for virtual screening of ligands by molecular docking. Thus, we evaluated the virtual screening performance of homology models of human GPCRs with respect to the corresponding crystal structures. Among the 19 GPCRs involved in this study, we observed that 10GPCRs have homology models that have better or comparable performance with respect to the corresponding X-ray structures, making homology models a viable choice for virtual screening. For a small subset of GPCRs, we also explored how certain methods like consensus enrichment and sidechain perturbation affect the utility of homology models in virtual screening, as well as the selectivity between agonists and antagonists. Most notably, consensus enrichment across multiple homology models often yields results comparable to the best performing model, suggesting that ligand candidates predicted with consensus scores from multiple models can be the optimal option in practical applications where the performance of each model cannot be estimated.

Known off-target interactions frequently cause predictable drug side-effects, e.g. β1antagonists (used for heart disease) risk β2-mediated bronchospasm. Computer-aided drug design would improve if the structural basis of existing drug selectivity was understood. A mutagenesis approach determined the ligand-amino acid interactions required for β1-selective affinity of xamoterol and nebivolol, followed by computer-based modelling to provide possible structural explanations. 3 H-CGP12177 whole cell binding was conducted in CHO cells stably expressing human β1, β2 and chimeric β1/β2-adrenoceptors (ARs). Single point mutations were investigated in transiently transfected cells. Modelling studies involved docking ligands into three-dimensional receptor structures and performing Molecular Dynamics simulations, comparing interaction frequencies between apo and holo structures of β1 and β2-ARs. From these observations, an ICI89406 derivative was investigated that gave further insights into selectivity. Stable cell line studies determined that transmembrane 2 was crucial for the β1-selective affinity of xamoterol and nebivolol. Single point mutations determined that the β1-AR isoleucine (I118) rather than the β2 histidine (H93) explained selectivity. Studies of other β1-ligands found I118 was important for ICI89406 selective affinity but not that for betaxolol, bisoprolol or esmolol. Modelling studies suggested that the interaction energies and solvation of β1-I118 and β2-H93 are factors determining selectivity of xamoterol and ICI89406. ICI89406 without its phenyl group loses its high β1-AR affinity, resulting in the same affinity as for the β2-AR. The human β1-AR residue I118 is crucial for the β1-selective affinity of xamoterol, nebivolol and ICI89406, but not all β1-selective compounds.