This article has been reviewed according to Science X's editorial process and policies. Editors have highlighted the following attributes while ensuring the content's credibility: A team of INRAE scientists, in collaboration with Université Côte d'Azur and Nanjing Agricultural University in China, has used an innovative AI-based method to identify the sex pheromone of the lily moth1 and the olfactory receptors associated with it. The insect pest, whose caterpillars feed on these plants, is found in Asia and Oceania.
This approach to identifying previously unknown molecules opens up new possibilities for identifying pheromones in other species. These molecules play a key role in certain biocontrol strategies currently used to protect crops, particularly in France. The results were obtained as part of the EXPLOR'AE program and published in BMC Biology.
Although insects are essential to ecosystems, they can also damage crops. Some approaches known as "biocontrol" use insect sexual communication, which relies on odorant molecules called pheromones, to address this problem. Knowing a species' sex pheromone can help control its population, for example, by releasing large quantities of the pheromone into the air to disrupt mating or by using insect traps to estimate or control population density.
However, identifying pheromones is not simple: It requires access to live insects, knowledge of exactly when they produce pheromones, and analysis of numerous chemical compounds to assess their attractiveness. The scientists developed a method to identify the sex pheromone of the lily moth (Spodoptera picta), whose sexual communication was previously unknown. Their approach works in reverse: Instead of starting with the insect that emits the signal, as is traditionally the case, they started with the receiver and its olfactory receptors, identified by analyzing the insect's genes.
They modeled the receptors using computer software and tested them with different odorant molecules to predict which ones were likely to interact with them. This analysis identified a potential pheromone and its associated receptors. The scientists then confirmed the findings through a series of electrophysiological, analytical and behavioral tests.
To identify both the pheromone and its associated olfactory receptors, the research team combined several complementary approaches. Based on the species' genes and comparisons with related species, they first selected two candidate receptors. The researchers then used artificial intelligence to predict their three-dimensional structures and molecular docking, a computational method used to predict how two molecules can bind and interact, to test volatile molecules and identify those likely to interact with the receptors.
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