Accelerating Ligand Discovery for Insect Odorant Receptors

Odorant receptors (ORs) are main actors of the insects peripheral olfactory system, making them prime targets for pest control through olfactory disruption. Traditional methods employed in the context of chemical ecology for identifying OR ligands rely on analyzing compounds present in the insect's environment or screening molecules with structures similar to known ligands. However, these approaches can be time-consuming and constrained by the limited chemical space they explore. Recent advances in OR structural understanding, coupled with scientific breakthroughs in protein structure prediction, have facilitated the application of Structure-Based Virtual Screening (SBVS) techniques for accelerated ligand discovery. Here, we report the first successful application of SBVS to insect ORs. We developed a unique workflow that combines molecular docking predictions, <i>in vivo</i> validation and behavioral assays to identify new behaviorally active volatiles for non-pheromonal receptors. This work serves as a proof of concept, laying the groundwork for future studies and highlighting the need for improved computational approaches. Finally, we propose a simple model for predicting receptor response spectra based on the hypothesis that the binding pocket properties partially encode this information, as suggested by our results on <i>Spodoptera littoralis</i> ORs.

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Publication Details

Journal
International Journal of Biological Sciences
Published
2025-02-18
DOI
https://doi.org/10.7150/ijbs.105648
Citations
7
Primary Topic
Computational Drug Discovery Methods
Type
article
Field-Weighted Citation Impact
10.53

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article

Accelerating Ligand Discovery for Insect Odorant Receptors

Sébastien Fiorucci, Nicolas Montagné, Emmanuelle Jacquin‐Joly, Arthur Comte et al.
7 citations
International Journal of Biological Sciences
Computational Drug Discovery Methods
10.53
article

Accelerating Ligand Discovery for Insect Odorant Receptors

Sébastien Fiorucci, Nicolas Montagné, Emmanuelle Jacquin‐Joly, Arthur Comte, Maxence Lalis, Jérémie Topin, Ludvine Brajon, Riccardo Moracci
article en
7 citations

Abstract

Odorant receptors (ORs) are main actors of the insects peripheral olfactory system, making them prime targets for pest control through olfactory disruption. Traditional methods employed in the context of chemical ecology for identifying OR ligands rely on analyzing compounds present in the insect's environment or screening molecules with structures similar to known ligands. However, these approaches can be time-consuming and constrained by the limited chemical space they explore. Recent advances in OR structural understanding, coupled with scientific breakthroughs in protein structure prediction, have facilitated the application of Structure-Based Virtual Screening (SBVS) techniques for accelerated ligand discovery. Here, we report the first successful application of SBVS to insect ORs. We developed a unique workflow that combines molecular docking predictions, <i>in vivo</i> validation and behavioral assays to identify new behaviorally active volatiles for non-pheromonal receptors. This work serves as a proof of concept, laying the groundwork for future studies and highlighting the need for improved computational approaches. Finally, we propose a simple model for predicting receptor response spectra based on the hypothesis that the binding pocket properties partially encode this information, as suggested by our results on <i>Spodoptera littoralis</i> ORs.

International Journal of Biological SciencesVol. 21(5)
Centre National de la Recherche Scientifique (FR), Institut Universitaire de France (FR), Université Paris-Est Créteil (FR), Université Paris Cité (FR), Sorbonne Université (FR), Institut de Chimie de Nice (FR), Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement (FR), Institut d'écologie et des sciences de l'environnement de Paris (FR), Institut de Recherche pour le Développement (FR)
Agence Nationale de la Recherche, Centre National de la Recherche Scientifique
Zero hunger
Openalex Percentile: Top 2%
Computational Drug Discovery Methods
10.53
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