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

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.

SUBMITTER: Caballero-Vidal G

PROVIDER: S-EPMC6997167 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature

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

Caballero-Vidal Gabriela G Bouysset Cédric C Grunig Hubert H Fiorucci Sébastien S Montagné Nicolas N Golebiowski Jérôme J Jacquin-Joly Emmanuelle E

Scientific reports 20200203 1

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 ar ...[more]

PMID: 32015393

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Project description:BackgroundInsects detect attractive and aversive chemicals using several families of chemosensory receptors, including the OR family of olfactory receptors, making these receptors appealing targets for the control of insects. Insect ORs are odorant-gated ion channels, comprised of at least one common subunit (the odorant receptor co-receptor subunit, Orco) and at least one variable odorant specificity subunit. Each of the many ORs of an insect species is activated or inhibited by an unique set of odorants that interact with the variable odorant specificity subunits, making the development of OR directed insect control agents complex and laborious. However, several N-,2-substituted triazolothioacetamide compounds (VUAA1, VU0450667 and VU0183254) were recently shown to act directly on the highly conserved Orco subunit, suggesting that broadly active compounds can be developed. We have explored the chemical space around the VUAA1 structure in order to identify new Orco ligands.Principal findingsWe screened ORs from several insect species, using heterologous expression in Xenopus oocytes and an electrophysiological assay, with a panel of 22 compounds structurally related to VUAA1. By varying the nitrogen position in the pyridine ring and altering the moieties decorating the phenyl ring, we identified two new agonists and a series of competitive antagonists. Screening smaller compounds, similar to portions of the VUAA1 structure, also yielded competitive antagonists. Importantly, we show that Orco antagonists inhibit odorant activation of ORs from several insect species. Detailed examination of one antagonist demonstrated inhibition to be through a non-competitive mechanism.ConclusionsA similar pattern of agonist and antagonist sensitivity displayed by Orco subunits from different species suggests a highly conserved binding site structure. The susceptibility to inhibition of odorant activation by Orco antagonism is conserved across disparate insect species, suggesing the ligand binding site on Orco as a promising target for the development of novel, broadly active insect repellants.

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

Publications

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

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