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计算嗅觉受体 Olfr73 的建模表明了低效力嗅觉受体激活化合物的分子基础。

Computational modeling of the olfactory receptor Olfr73 suggests a molecular basis for low potency of olfactory receptor-activating compounds.

机构信息

1Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055 China.

2Institute of Chemical Sciences and Engineering, Ecole Polytechnique Fédérale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland.

出版信息

Commun Biol. 2019 Apr 23;2:141. doi: 10.1038/s42003-019-0384-8. eCollection 2019.

Abstract

The mammalian olfactory system uses hundreds of specialized G-protein-coupled olfactory receptors (ORs) to discriminate a nearly unlimited number of odorants. Cognate agonists of most ORs have not yet been identified and potential non-olfactory processes mediated by ORs are unknown. Here, we used molecular modeling, fingerprint interaction analysis and molecular dynamics simulations to show that the binding pocket of the prototypical olfactory receptor Olfr73 is smaller, but more flexible, than binding pockets of typical non-olfactory G-protein-coupled receptors. We extended our modeling to virtual screening of a library of 1.6 million compounds against Olfr73. Our screen predicted 25 Olfr73 agonists beyond traditional odorants, of which 17 compounds, some with therapeutic potential, were validated in cell-based assays. Our modeling suggests a molecular basis for reduced interaction contacts between an odorant and its OR and thus the typical low potency of OR-activating compounds. These results provide a proof-of-principle for identifying novel therapeutic OR agonists.

摘要

哺乳动物嗅觉系统使用数百种特异性 G 蛋白偶联嗅觉受体(OR)来区分几乎无限数量的气味。大多数 OR 的同源激动剂尚未被鉴定,而 OR 介导的潜在非嗅觉过程尚不清楚。在这里,我们使用分子建模、指纹相互作用分析和分子动力学模拟表明,典型嗅觉受体 Olfr73 的结合口袋比典型非嗅觉 G 蛋白偶联受体的结合口袋更小,但更灵活。我们将我们的模型扩展到针对 Olfr73 的 160 万化合物库的虚拟筛选。我们的筛选预测了 25 种超出传统气味的 Olfr73 激动剂,其中 17 种化合物,包括一些具有治疗潜力的化合物,在基于细胞的测定中得到了验证。我们的模型为气味与其 OR 之间的相互作用接触减少提供了分子基础,因此 OR 激活化合物的典型低效力。这些结果为鉴定新型治疗性 OR 激动剂提供了原理证明。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8112/6478719/b0ec313eb57a/42003_2019_384_Fig1_HTML.jpg

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