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Review
. 2018:160:63-104.
doi: 10.1016/bs.pmbts.2018.08.001. Epub 2018 Oct 15.

Analysis of Biased Agonism

Affiliations
Review

Analysis of Biased Agonism

Frederick J Ehlert. Prog Mol Biol Transl Sci. 2018.

Abstract

Agonists and most natural ligands bind to receptors in their inactive state and quickly induce an active receptor conformation that initiates cell signaling. The active receptor state initiates signaling because of its structural complementariness with coupling proteins that activate signaling pathways, such as G proteins and G protein-coupled receptor kinases. Agonist bias refers to the propensity of an agonist to direct receptor signaling through one pathway relative to another. Thus, if the agonist exhibits much higher affinity for active state 1 compared to active state 2, it will cause a robust activation of receptor coupling protein 1 but not 2, and ultimately, a preferential stimulation of signaling pathway 1. Biased agonists are potentially more selective therapeutic agents because there are numerous cases where the therapeutic and adverse effects of an agonist are mediated by distinct pathways involving G proteins and β-arrestin. Given the mechanism for agonist bias, the most straightforward approach for quantifying bias involves the estimation of agonist affinity for the inactive receptor state and the active receptor states involved in signaling through different pathways. The approach provides quantitative estimates of the sensitivities of different signaling pathways, enabling one to determine to what extent the observed selectivity is caused by agonist or system bias. In addition, the approach is a powerful adjunct to in silico docking studies and can be applied to in vivo assays, structure-activity relationships, and the analysis of published agonist concentration-response curves.

Keywords: Active receptor state; Agonist bias; Allosterism; Analysis of receptor function; Constitutive activity; Inactive receptor state; Operational model; Receptor theory; Single-receptor activity; System bias.

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