Comte Arthur, Fiorucci Sébastien, Jacquin-Joly Emmanuelle
INRAE, Sorbonne Université, CNRS, IRD, Université Paris Cité, Université Paris-Est Créteil Val de Marne, Institut d'Ecologie et des Sciences de l'Environnement de Paris (iEES-Paris) , Versailles, France.
Université Côte d'Azur, CNRS, Institut de Chimie de Nice, Nice, France.
Methods Mol Biol. 2025;2915:101-116. doi: 10.1007/978-1-0716-4466-9_5.
Insects rely on olfaction in many aspects of their life, and odorant receptors are key proteins in this process. Whereas a plethora of insect odorant receptor sequences is available, most of them are still orphan or uncompletely characterized, since their functional studies are usually limited by restricted odorant panels. With joint approaches that combine computational methods like machine learning and electrophysiology measurements, researchers can expand the chemical space of insect odorant receptors and speed up the discovery of new active ligands. This chapter details the methodology for setting up a quantitative structure-activity relationship (QSAR) predictive model for identifying odorant receptor agonists and for conducting single sensillum recordings to validate the predictions.
昆虫在其生命的许多方面都依赖嗅觉，而气味受体是这一过程中的关键蛋白质。尽管有大量昆虫气味受体序列可供使用，但其中大多数仍然是孤儿受体或特征不完全明确，因为它们的功能研究通常受到有限的气味剂组合的限制。通过结合机器学习等计算方法和电生理测量的联合方法，研究人员可以扩展昆虫气味受体的化学空间，并加快新活性配体的发现。本章详细介绍了建立定量构效关系（QSAR）预测模型以识别气味受体激动剂并进行单感器记录以验证预测的方法。
