BACKGROUND AND PURPOSEThe adenosine A2A receptor belongs to the superfamily of GPCRs and is a promising therapeutic target. Traditionally, the discovery of novel agents for the A2A receptor has been guided by their affinity for the receptor. This parameter is determined under equilibrium conditions, largely ignoring the kinetic aspects of the ligand-receptor interaction. The aim of this study was to assess the binding kinetics of A2A receptor agonists and explore a possible relationship with their functional efficacy. EXPERIMENTAL APPROACHWe set up, validated and optimized a kinetic radioligand binding assay (a so-called competition association assay) at the A2A receptor from which the binding kinetics of unlabelled ligands were determined. Subsequently, functional efficacies of A2A receptor agonists were determined in two different assays: a novel label-free impedance-based assay and a more traditional cAMP determination. KEY RESULTSA simplified competition association assay yielded an accurate determination of the association and dissociation rates of unlabelled A2A receptor ligands at their receptor. A correlation was observed between the receptor residence time of A2A receptor agonists and their intrinsic efficacies in both functional assays. The affinity of A2A receptor agonists was not correlated to their functional efficacy. CONCLUSIONS AND IMPLICATIONSThis study indicates that the molecular basis of different agonist efficacies at the A2A receptor lies within their different residence times at this receptor. AbbreviationsADA, adenosine deaminase; BCA, bicinchoninic acid; CHAPS, 3-[(3-cholamidopropyl) dimethylammonio]-1-propanesulfonate; CI, cell index; HEK293hA2AR, human embryonic kidney 293 cells stably expressing the hA2A receptor; k3, the association rate constant of the unlabelled ligand; k4, the dissociation rate constant of the unlabelled ligand; RT, residence time; Z, cell-electrode impedance; ZM241385, 4-(2-[7-amino-2-(2-furyl) [1,2,4]
The adenosine A(2B) receptor is the least well characterized of the four known adenosine receptor subtypes because of the absence of potent, selective agonists. Here, we present five non-adenosine agonists. Among them, 2-amino-4-(4-hydroxyphenyl)-6-(1H-imidazol-2-ylmethylsulfanyl)pyridine-3,5-dicarbonitrile, 17, LUF5834, is a high-efficacy partial agonist with EC(50) = 12 nM and 45-fold selectivity over the adenosine A(3) receptor but lacking selectivity versus the A(1) and A(2A) subtypes. Compound 18, LUF5835, the 3-hydroxyphenyl analogue, is a full agonist with EC(50) = 10 nM.
The rapid growth of structural information for G-protein-coupled receptors (GPCRs) has led to a greater understanding of their structure, function, selectivity, and ligand binding. Although novel ligands have been identified using methods such as virtual screening, computationally driven lead optimization has been possible only in isolated cases because of challenges associated with predicting binding free energies for related compounds. Here, we provide a systematic characterization of the performance of free-energy perturbation (FEP) calculations to predict relative binding free energies of congeneric ligands binding to GPCR targets using a consistent protocol and no adjustable parameters. Using the FEP+ package, first we validated the protocol, which includes a full lipid bilayer and explicit solvent, by predicting the binding affinity for a total of 45 different ligands across four different GPCRs (adenosine A2AAR, β1 adrenergic, CXCR4 chemokine, and δ opioid receptors). Comparison with experimental binding affinity measurements revealed a highly predictive ranking correlation (average spearman ρ = 0.55) and low root-mean-square error (0.80 kcal/mol). Next, we applied FEP+ in a prospective project, where we predicted the affinity of novel, potent adenosine A2A receptor (A2AR) antagonists. Four novel compounds were synthesized and tested in a radioligand displacement assay, yielding affinity values in the nanomolar range. The affinity of two out of the four novel ligands (plus three previously reported compounds) was correctly predicted (within 1 kcal/mol), including one compound with approximately a tenfold increase in affinity compared to the starting compound. Detailed analyses of the simulations underlying the predictions provided insights into the structural basis for the two cases where the affinity was overpredicted. Taken together, these results establish a protocol for systematically applying FEP+ to GPCRs and provide guidelines for identifying potent molecules in drug discovery lead optimization projects.
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