Use of machine learning to identify novel, behaviorally active antagonists of the insect odorant receptor co-receptor (Orco) subunit

Abstract

Olfaction is a key component of the multimodal approach used by mosquitoes to target and feed on humans, spreading various diseases. Current repellents have drawbacks, necessitating development of more effective agents. In addition to variable odorant specificity subunits, all insect odorant receptors (ORs) contain a conserved odorant receptor co-receptor (Orco) subunit which is an attractive target for repellent development. Orco directed antagonists allosterically inhibit odorant activation of ORs and we previously showed that an airborne Orco antagonist could inhibit insect olfactory behavior. Here, we identify novel, volatile Orco antagonists. We functionally screened 83 structurally diverse compounds against Orco from Anopheles gambiae. Results were used for training machine learning models to rank probable activity of a library of 1280 odorant molecules. Functional testing of a representative subset of predicted active compounds revealed enrichment for Orco antagonists, many structurally distinct from previously known Orco antagonists. Novel Orco antagonist 2-tert-butyl-6-methylphenol (BMP) inhibited odorant responses in electroantennogram and single sensillum recordings in adult Drosophila melanogaster and inhibited OR-mediated olfactory behavior in D. melanogaster larvae. Structure-activity analysis of BMP analogs identified compounds with improved potency. Our results provide a new approach to the discovery of behaviorally active Orco antagonists for eventual use as insect repellents/confusants.

Antifeeding effects of azadirachtin on the fifth instar Spodoptera litura larvae and the analysis of azadirachtin on target sensilla around mouthparts

“…The head of the insect is the sensory and feeding center, and the head receptor is used to sense external chemical stimulation and mechanical actions (Javoiš, Davis, & Tammaru, 2019), so as to feed, gather, seek, mate, lay eggs, and avoid natural enemies (Kepchia et al, 2019). In the process of insect location, olfactory sensilla are usually used to identify trace amounts of specific compounds from many odors to locate host plants (Na, Liu, Nguyen, & Ryan, 2019).…”

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

“…www.nature.com/scientificreports www.nature.com/scientificreports/ Modeling has already been shown to provide accurate information and facilitate the selection of active molecules on odorant receptors. In insects, it has been applied only in two Diptera models, the fruit fly and the mosquito 24,25,28 . In this study, we reveal that a conventional machine learning approach is efficient for the identification of novel agonists for a moth receptor, whose amino acid sequence is unrelated to that of Diptera ORs.…”

“…An in silico screening of 0.5 million compounds identified agonists or antagonists targeting the mosquito CO 2 receptor, leading to the discovery of new attractants and repellents for those harmful disease vectors 24 . More recently, antagonists for the insect coreceptor Orco have been identified by screening a library of 1280 odorant molecules 28 . In mammals, a more modest virtual screening of 258 chemicals anyhow identified new agonists of four human ORs 26 .…”

“…For comparison, 30% of 138 in silico predicted odorants activated the mosquito CO 2 receptor in a first round 24 . Machine learning models based on ligand topology predicted 138 antagonists for mosquito Orco, out of which 45 were active (32%) 28 . In this last study, it has to be noticed that 58 active antagonists were used to feed the machine learning, a number that is much higher than the 13 ligands we used.…”

Volatile allosteric antagonists of mosquito odorant receptors inhibit normal odor-dependent behaviors

“…A recent study involving the functional screening of a small collection of commercially available natural compounds selected through machine learning methodologies in X. laevis oocytes expressing AgamOrco, identified two AgamOrco antagonists, which inhibited odorant responses in electroantennogram and single sensillum recordings of adult Drosophila melanogaster antennae and inhibited odorantdirected behaviors in larvae of the same species (49). Interestingly, this study, whose results are concordant with ours with respect to the cross genus bioactivities of AgamORco antagonists, identified linalyl formate, a compound with a structure almost identical to that of LA, as one of the two AgamORco antagonists that inhibited odorant-directed behaviors in Drosophila larvae.…”