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

Title
Machine learning decodes chemical features to identify novel agonists of a moth odorant receptor
Authors
Caballero-Vidal, GabrielaBouysset, CédricGrunig, HubertFiorucci, SébastienMontagné, NicolasGolebiowski, JeromeJacquin-Joly, Emmanuelle
DGIST Authors
Caballero-Vidal, Gabriela; Bouysset, Cédric; Grunig, Hubert; Fiorucci, Sébastien; Montagné, Nicolas; Golebiowski, Jerome; Jacquin-Joly, Emmanuelle
Issue Date
2020-02
Citation
Scientific Reports, 10(1), 1655
Type
Article
Article Type
Article
Keywords
7-TRANSMEMBRANE PROTEINSMOLECULAR-BASISREPELLENTSDIVERSITYBIOASSAYFAMILY
ISSN
2045-2322
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. © 2020, The Author(s).
URI
http://hdl.handle.net/20.500.11750/12614
DOI
10.1038/s41598-020-58564-9
Publisher
Nature Publishing Group
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