Stahl Edward L, Zhou Lei, Ehlert Frederick J, Bohn Laura M
Departments of Molecular Therapeutics and Neuroscience, The Scripps Research Institute, Jupiter, Florida (E.L.S., L.Z., L.M.B.); and Department of Pharmacology, School of Medicine, University of California-Irvine, Irvine, California (F.J.E.)
Departments of Molecular Therapeutics and Neuroscience, The Scripps Research Institute, Jupiter, Florida (E.L.S., L.Z., L.M.B.); and Department of Pharmacology, School of Medicine, University of California-Irvine, Irvine, California (F.J.E.).
Mol Pharmacol. 2015 May;87(5):866-77. doi: 10.1124/mol.114.096503. Epub 2015 Feb 13.
Seven transmembrane receptors were originally named and characterized based on their ability to couple to heterotrimeric G proteins. The assortment of coupling partners for G protein-coupled receptors has subsequently expanded to include other effectors (most notably the βarrestins). This diversity of partners available to the receptor has prompted the pursuit of ligands that selectively activate only a subset of the available partners. A biased or functionally selective ligand may be able to distinguish between different active states of the receptor, and this would result in the preferential activation of one signaling cascade more than another. Although application of the "standard" operational model for analyzing ligand bias is useful and suitable in most cases, there are limitations that arise when the biased agonist fails to induce a significant response in one of the assays being compared. In this article, we describe a quantitative method for measuring ligand bias that is particularly useful for such cases of extreme bias. Using simulations and experimental evidence from several κ opioid receptor agonists, we illustrate a "competitive" model for quantitating the degree and direction of bias. By comparing the results obtained from the competitive model with the standard model, we demonstrate that the competitive model expands the potential for evaluating the bias of very partial agonists. We conclude the competitive model provides a useful mechanism for analyzing the bias of partial agonists that exhibit extreme bias.
七跨膜受体最初是根据其与异源三聚体G蛋白偶联的能力来命名和表征的。随后，G蛋白偶联受体的偶联伙伴种类扩展到包括其他效应器（最显著的是β抑制蛋白）。受体可利用的这种伙伴多样性促使人们寻找仅选择性激活可用伙伴子集的配体。一种偏向性或功能选择性配体可能能够区分受体的不同活性状态，这将导致一个信号级联比另一个信号级联更优先被激活。尽管应用“标准”操作模型来分析配体偏向性在大多数情况下是有用且合适的，但当偏向性激动剂在被比较的一种测定中未能诱导出显著反应时，就会出现局限性。在本文中，我们描述了一种测量配体偏向性的定量方法，该方法对于这种极端偏向性的情况特别有用。利用来自几种κ阿片受体激动剂的模拟和实验证据，我们阐述了一种用于定量偏向程度和方向的“竞争”模型。通过将从竞争模型获得的结果与标准模型进行比较，我们证明竞争模型扩展了评估非常部分激动剂偏向性的潜力。我们得出结论，竞争模型为分析表现出极端偏向性的部分激动剂的偏向性提供了一种有用的机制。
