The traditional route to identifying a sex pheromone has always been laborious. Researchers must typically maintain live colonies of the target insect, collect and extract the chemicals emitted by females, and then sift through a complex mixture of dozens or even hundreds of volatile compounds to determine which ones actually attract males. The timing of pheromone production matters enormously, since many species release their signals only during narrow windows of the day or night, and missing that window can mean missing the signal entirely. Once candidate compounds are isolated, each must be tested for behavioural activity, often through wind-tunnel experiments or field trials, before the true pheromone can be confirmed. For rare species, invasive pests that have just appeared in a new region, or insects that are difficult to rear in captivity, this process can stall for years, leaving pest managers without the species-specific tools that pheromone-based control depends on.

The INRAE team, working within the EXPLOR’AE Programme funded through the project known as Invoria, chose to invert this logic entirely. Rather than starting with the emitter of the chemical signal, they started with the receiver: the male moth, and more specifically, the olfactory receptors housed in his antennae that are tuned to detect the female’s scent. Their study species was the lily moth, Spodoptera picta, a strikingly coloured insect whose sexual communication had never been described. The lily moth is what biologists call a non-model organism: it is not a standard laboratory species with a rich history of prior research, and this was precisely the point. If the method could work on a poorly studied insect with no established toolkit, the researchers reasoned, it could likely be extended to many other species that currently resist conventional analysis.

The first step of the reverse approach relied on genomics. By analysing the genes of the lily moth and comparing them with those of related species, the researchers narrowed down a catalogue of candidate olfactory receptors to two promising candidates. This comparative step matters because receptor genes evolve alongside the chemicals they detect; receptors in closely related species that respond to similar pheromones often share detectable similarities in their sequences. From this genetic starting material, the team turned to artificial intelligence. Machine-learning tools were used to predict the three-dimensional structures of the two candidate receptors, generating models of the protein shapes that sit in the membranes of sensory neurons and cradle odorant molecules as they arrive from the air.

With structural models in hand, the researchers applied molecular docking, a computational technique widely used in drug discovery, to predict which volatile molecules would fit and bind within the receptors’ binding pockets. Docking simulations evaluate how two molecules can interact spatially and energetically, scoring the likelihood that a given compound will make stable contact with a target protein. By screening a library of volatile compounds against the modelled receptors, the computational pipeline identified a specific molecule predicted to interact strongly with both candidate receptors: (Z,E)-9,11-tetradecadienyl acetate. In effect, the algorithm had proposed an answer to a question the insects had never been asked in the laboratory: what chemical does the female of this species use to signal her availability?

Prediction alone, however, is never sufficient in biology, and the team followed the computational phase with a rigorous sequence of experimental validation. First, the selected olfactory receptors were tested directly against the predicted volatile molecules using electrophysiological methods, which measure the electrical responses of sensory cells or expressed receptor proteins when exposed to chemical stimuli. The receptors responded, confirming computationally predicted interactions with real physiological activity. Next, the researchers combined physicochemical analysis with electrophysiology to verify two crucial facts: that female lily moths actually secrete the predicted compound as their sex pheromone, and that male moths can detect the volatile molecule in the air. These analytical steps confirmed that the AI-guided prediction matched what the insects themselves were doing.

The final and most demanding test was behavioural. A molecule may be detected by an antenna, but detection does not guarantee attraction; only a behavioural response demonstrates that the compound functions as a genuine pheromone in the life of the animal. In behavioural assays, the identified compound attracted male lily moths and elicited sexual behaviour, completing the chain of evidence from gene to receptor to molecule to mating behaviour. Taken together, the results show that an unknown pheromone of a non-model species could be identified through a pipeline that begins with sequencing, passes through AI-predicted receptor structures and molecular docking, and ends with classical electrophysiology and behavioural experiments. Each stage filtered and focused the search, dramatically reducing the number of candidate chemicals that needed laboratory testing.

The significance of this proof of concept extends well beyond a single moth species. Current, emerging and invasive crop pests are precisely the organisms for which pheromone identification is most urgently needed and most often missing, because invasive populations can spread faster than conventional chemical ecology can characterise them. Once a species’ sex pheromone is known, two well-established biocontrol strategies become available. Mating disruption involves flooding an area, such as a crop field or orchard, with synthetic pheromone so that males become unable to locate females, crashing reproduction without any toxic chemical. Alternatively, pheromone-baited traps can be used to estimate population density, monitor invasions, or suppress populations through mass trapping. Both approaches are highly species-specific, sparing beneficial insects such as pollinators and natural enemies of pests, and both are compatible with the increasingly strict regulations governing pesticide use in gardens, parks and botanical collections.

The lily moth itself illustrates why such tools matter. The species is not found in mainland France and is therefore not considered a pest of concern there, which made it an ethically convenient and scientifically demanding test case. Yet elsewhere in the world, Spodoptera picta is a significant pest, particularly in private gardens, public parks and botanical gardens, settings where the use of conventional pest-control products is often tightly restricted and where ornamental plants suffer real damage. A validated pheromone-based method could give managers in those regions a targeted, low-toxicity option, and the study’s authors note that this work opens up the possibility of using pheromone-based methods to control the moth. In this sense, the research delivers both a general technique and a concrete practical outcome for a real agricultural problem.

There is also a deeper scientific payoff. Pheromones function as chemical signatures of species, and the molecules a species uses to communicate sexually contribute to reproductive isolation, helping to keep related species distinct. As a result, identifying the compounds that make up the pheromones of moths and other insects offers new insights into the evolutionary history of these lineages, revealing how chemical communication has diverged and shaped speciation. By making pheromone identification faster and less dependent on scarce live insects, the reverse chemical ecology approach demonstrated here could populate the field with many more of these chemical signatures, allowing biologists to reconstruct communication systems across entire groups of species. What began as a pest-management problem thus ends as a window onto evolution, powered by artificial intelligence, and a demonstration that sometimes the fastest way to understand a signal is to study the ear rather than the voice.

Subject of Research: AI-guided prediction of insect olfactory receptors to identify moth sex pheromones for crop protection

Article Title: Crop protection: a new method for understanding insect sexual communication

Article References: Crop protection: a new method for understanding insect sexual communication. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: pheromones, chemical ecology, olfactory receptors, Spodoptera picta, biocontrol, mating disruption, molecular docking, artificial intelligence, INRAE, BMC Biology, invasive pests, lily moth

Sex pheromones are the linchpin of reproduction in most moths. Females synthesize and release species-specific blends of volatile molecules, usually during a defined window of the night, and males detect these blends over remarkable distances at astonishingly low concentrations. The precise composition of the blend, both its constituent molecules and their ratios, determines species identity and helps maintain reproductive isolation between closely related species that share habitat. Yet despite their central importance, pheromone compositions are well characterized almost exclusively for agricultural pest species, where the commercial incentive is clear: synthetic pheromones underpin population monitoring traps, mass trapping programs, and mating disruption strategies. Non-pest species, including the lily moth, whose caterpillars feed on ornamental Amaryllidaceae plants in gardens, have been largely overlooked.

Traditional pheromone identification is laborious and constrained. It typically requires access to living insects for gland extraction or headspace collection, careful determination of the females’ calling period, and painstaking analytical chemistry to pick out the behaviorally relevant compounds from complex extracts. The team sidestepped these hurdles by starting from a different vantage point altogether: the moth’s genome. Taking advantage of a recently assembled chromosome-level genome of S. picta, they searched for orthologs of OR5 and OR75, two pheromone receptors previously characterized in the sister species S. litura and S. littoralis, where OR5 is narrowly tuned to the major pheromone component (Z,E)-9,11-tetradecadienyl acetate, abbreviated (Z,E)-9,11-14:OAc.

The genomic search succeeded on both counts. The annotated S. picta genes, SpicOR5 and SpicOR75, encode proteins of 397 amino acids each, and quantitative PCR showed that SpicOR5 is strongly over-expressed relative to SpicOR75 in adult male antennae, a pattern mirroring the sister species and consistent with OR5’s established role as the principal receptor mediating male attraction in S. littoralis and S. litura. Expression levels were stable between one-day-old and three-day-old males, suggesting the receptors are not regulated by early adult sexual maturation. The sequence identities between S. picta receptors and their orthologs in the two sister species exceeded 94 percent, in sharp contrast to the roughly 67 to 72 percent identity they share with more distantly related Spodoptera species.

High sequence identity alone, however, is not a guarantee of conserved function. Research on other moths has shown that a single amino acid substitution can dramatically shift a pheromone receptor’s tuning. To address this uncertainty, the researchers built three-dimensional models of the receptors using the AlphaFold 3 web server and interrogated the physicochemical properties of their predicted ligand-binding pockets, including cavity volume and hydrophobicity, two parameters previously shown to correlate directly with odorant receptor tuning. The models of S. picta, S. litura, and S. littoralis receptors were nearly superimposable, with mean root-mean-square deviations of about 0.3 angstroms across the full-length proteins. Molecular docking then placed the candidate ligand (Z,E)-9,11-14:OAc in virtually the same binding pose within SpicOR5 as in its orthologs, engaging conserved residues across all three species.

Functional validation followed in a heterologous system. The S. picta receptors were expressed transgenically in Drosophila melanogaster olfactory neurons, replacing the fruit fly’s endogenous receptor in at1 sensilla, and tested against a panel of 26 moth pheromone compounds using single-sensillum recordings. As predicted, SpicOR5 responded to exactly one compound in the panel: (Z,E)-9,11-14:OAc, which elicited a median firing rate of 49 spikes per second and activated the receptor from doses as low as 10 micrograms. SpicOR75, true to its predicted broad tuning, responded significantly to seven of the 26 compounds, sharing most of its ligand repertoire with its orthologs in the sister species. The docking predictions and electrophysiology agreed almost perfectly.

With the receptor tuning established, the team flipped the logic of pheromone discovery. If SpicOR5, the dominant receptor in male antennae, is exquisitely tuned to (Z,E)-9,11-14:OAc, then this compound should be the major component of the female pheromone blend. Behavioral observations first pinpointed the optimal extraction window: female calling behavior peaked on the first two days after adult emergence, six and a half to nine hours into the dark phase, when up to 60 percent of females were calling. Gas chromatography coupled to electroantennographic detection of extracts from females collected during this window revealed a single compound that activated male antennae, and it was also the most prominent peak in the chromatogram. Subsequent mass spectrometry confirmed its identity as (Z,E)-9,11-14:OAc, alongside three minor components, (Z)-9-14:OAc, (E)-11-14:OAc, and (Z)-11-14:OAc, in a relative ratio of 100:45:34:3.

The behavioral assays sealed the conclusion. At 10 micrograms on filter paper, (Z,E)-9,11-14:OAc alone triggered wing-fanning in 56.3 percent of tested males, with additional males performing hair-pencil displays and contacting the odor source. A synthetic four-component blend matching the gland extract ratio elicited even more sexual behaviors, though still fewer than a natural female gland extract, which provoked the full behavioral sequence in every male tested. That shortfall echoes a pattern documented in the fall armyworm, S. frugiperda, where a trace pheromone component present at only about 0.2 percent of the major component’s abundance significantly enhances male attraction. The authors suggest that additional, still-unidentified trace compounds likely contribute to the complete S. picta pheromone signal.

Beyond its practical implications, the study illuminates a fascinating evolutionary story. OR75 exists only as a duplicated copy of OR5 in S. picta, S. litura, and S. littoralis; related species such as S. frugiperda and S. exigua, which do not produce (Z,E)-9,11-14:OAc, carry only a single, broadly tuned OR5 ortholog. The duplication apparently freed one copy to specialize. Multiple amino acid substitutions in the OR5 binding pocket shifted its specificity toward narrow recognition of the major pheromone component, while OR75 largely retained the ancestral broad tuning, though with modified specificity. Both copies thus represent cases of neofunctionalization relative to the ancestral receptor, and the functional divergence is mirrored by structural divergence in their binding sites, which cluster into two distinct groups that exclude the more distant Spodoptera orthologs.

The shared major pheromone component also raises ecological questions. S. picta and S. litura overlap geographically across large regions of South Asia and Oceania, and both call during the second half of the night, creating a potential risk of interspecific sexual signal interference. The two species’ minor components differ qualitatively, however, and these species-specific minor compounds may serve as critical discrimination cues for males, a mechanism documented in other Spodoptera species where heterospecific pheromone components act as behavioral antagonists. Host plant volatiles may further refine mate finding, since S. picta specializes on certain Amaryllidaceae plants, while its polyphagous sister species does not.

For applied entomology, the payoff is immediate. Identifying (Z,E)-9,11-14:OAc as a sufficient male attractant in S. picta opens the door to pheromone-based monitoring and trapping in gardens and nurseries where synthetic pesticides are restricted. More broadly, the study demonstrates a generalizable workflow: mine a genome for candidate pheromone receptors, model their structures, dock candidate ligands, verify with electrophysiology, and only then commit to chemical analysis. As genomic resources proliferate and AI-driven structure prediction matures, this receptor-guided strategy promises to bring chemical ecology within reach for the vast majority of insect species that will never enjoy the attention devoted to a handful of crop pests.

Subject of Research: Structure-based prediction of moth sex pheromone components from odorant receptor models in Spodoptera picta

Article Title: Odorant receptor structures predict the major female sex pheromone component in a moth

Article References: Comte, A., Wei, Z., Li, H., Xing, W.-Z., Moracci, R., Zhang, J., Fiorucci, S., & Jacquin-Joly, E. (2026). Odorant receptor structures predict the major female sex pheromone component in a moth. BMC Biology, 24(1), Article 193. https://doi.org/10.1186/s12915-026-02717-1

Image Credits: AI Generated

DOI: 10.1186/s12915-026-02717-1

Keywords: Spodoptera picta, sex pheromone, pheromone receptors, odorant receptors, AlphaFold 3, molecular docking, reverse chemical ecology, GC-MS, electrophysiology, gene duplication, reproductive isolation, pest management