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机器学习解码化学特征,以鉴定一种飞蛾气味受体的新型激动剂。

Machine learning decodes chemical features to identify novel agonists of a moth odorant receptor.

机构信息

INRAE, Sorbonne Université, CNRS, IRD, UPEC, Université Paris Diderot, Institute of Ecology and Environmental Sciences of Paris, Paris, Versailles, France.

Institute of Chemistry of Nice, UMR CNRS 7272, Université Côte d'Azur, Nice, France.

出版信息

Sci Rep. 2020 Feb 3;10(1):1655. doi: 10.1038/s41598-020-58564-9.

DOI:10.1038/s41598-020-58564-9
PMID:32015393
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6997167/
Abstract

Odorant receptors expressed at the peripheral olfactory organs are key proteins for animal volatile sensing. Although they determine the odor space of a given species, their functional characterization is a long process and remains limited. To date, machine learning virtual screening has been used to predict new ligands for such receptors in both mammals and insects, using chemical features of known ligands. In insects, such approach is yet limited to Diptera, whereas insect odorant receptors are known to be highly divergent between orders. Here, we extend this strategy to a Lepidoptera receptor, SlitOR25, involved in the recognition of attractive odorants in the crop pest Spodoptera littoralis larvae. Virtual screening of 3 million molecules predicted 32 purchasable ones whose function has been systematically tested on SlitOR25, revealing 11 novel agonists with a success rate of 28%. Our results show that Support Vector Machine optimizes the discovery of novel agonists and expands the chemical space of a Lepidoptera OR. More, it opens up structure-function relationship analyses through a comparison of the agonist chemical structures. This proof-of-concept in a crop pest could ultimately enable the identification of OR agonists or antagonists, capable of modifying olfactory behaviors in a context of biocontrol.

摘要

表达在外周嗅觉器官的气味受体是动物挥发性感知的关键蛋白。尽管它们决定了给定物种的气味空间,但它们的功能表征是一个漫长的过程,仍然受到限制。迄今为止,机器学习虚拟筛选已被用于预测哺乳动物和昆虫中此类受体的新配体,使用已知配体的化学特征。在昆虫中,这种方法仅限于双翅目,而昆虫气味受体在目之间存在高度的差异。在这里,我们将这种策略扩展到鳞翅目受体 SlitOR25,该受体参与识别作物害虫斜纹夜蛾幼虫中的有吸引力的气味。对 300 万个分子进行虚拟筛选,预测了 32 种可购买的分子,这些分子的功能已在 SlitOR25 上进行了系统测试,发现了 11 种新型激动剂,成功率为 28%。我们的结果表明,支持向量机优化了新型激动剂的发现,并扩展了鳞翅目 OR 的化学空间。此外,它通过比较激动剂的化学结构,为结构-功能关系分析开辟了道路。这一在作物害虫中的概念验证最终可以识别 OR 激动剂或拮抗剂,从而能够在生物防治的背景下改变嗅觉行为。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/907e/6997167/926a44b60e96/41598_2020_58564_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/907e/6997167/ce8be52ec7a5/41598_2020_58564_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/907e/6997167/fc5bd8683760/41598_2020_58564_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/907e/6997167/926a44b60e96/41598_2020_58564_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/907e/6997167/ce8be52ec7a5/41598_2020_58564_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/907e/6997167/fc5bd8683760/41598_2020_58564_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/907e/6997167/926a44b60e96/41598_2020_58564_Fig3_HTML.jpg

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