Agonists of G‑Protein-Coupled Odorant Receptors Are Predicted from Chemical Features

Predicting the activity of chemicals for a given odorant receptor is a longstanding challenge. Here the activity of 258 chemicals on the human G-protein-coupled odorant receptor (OR)­51E1, also known as prostate-specific G-protein-coupled receptor 2 (PSGR2), was virtually screened by machine learning using 4884 chemical descriptors as input. A systematic control by functional in vitro assays revealed that a support vector machine algorithm accurately predicted the activity of a screened library. It allowed us to identify two novel agonists in vitro for OR51E1. The transferability of the protocol was assessed on OR1A1, OR2W1, and MOR256-3 odorant receptors, and, in each case, novel agonists were identified with a hit rate of 39–50%. We further show how ligands’ efficacy is encoded into residues within OR51E1 cavity using a molecular modeling protocol. Our approach allows widening the chemical spaces associated with odorant receptors. This machine-learning protocol based on chemical features thus represents an efficient tool for screening ligands for G-protein-coupled odorant receptors that modulate non-olfactory functions or, upon combinatorial activation, give rise to our sense of smell.

针对特定气味受体预测化学品活性，是一项长期存在的科研挑战。本研究以4884个化学描述符（chemical descriptors）作为输入，通过机器学习方法对258种化学品在人源G蛋白偶联气味受体（G-protein-coupled odorant receptor，OR）51E1——又名前列腺特异性G蛋白偶联受体2（prostate-specific G-protein-coupled receptor 2，PSGR2）——上的活性开展了虚拟筛选。经体外功能实验开展的系统性对照验证表明，支持向量机（support vector machine）算法可精准预测筛选化合物库的活性，借此我们成功在体外鉴定出OR51E1的两种新型激动剂（agonist）。本研究进一步在OR1A1、OR2W1及MOR256-3三种气味受体上评估了该实验方案的可迁移性，在每个受体组中均以39%~50%的命中率鉴定出了新型激动剂。我们还通过分子建模（molecular modeling）实验方案，揭示了配体（ligand）效力如何编码于OR51E1结合空腔的氨基酸残基之中。本研究方法可拓展与气味受体相关的化学空间范围。综上，这种基于化学特征的机器学习实验方案，可作为高效工具，用于筛选调控非嗅觉功能的G蛋白偶联气味受体配体，或是经组合激活后介导人类嗅觉感知的G蛋白偶联气味受体配体。