Dataset Information

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.

SUBMITTER: Kepchia D

PROVIDER: S-EPMC6411751 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

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

Kepchia Devin D Xu Pingxi P Terryn Raymond R Castro Ana A Schürer Stephan C SC Leal Walter S WS Luetje Charles W CW

Scientific reports 20190311 1

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 ...[more]

PMID: 30858563

Similar Datasets

Project description:Insects 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.We 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.A 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.

Dataset Information

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

Publications

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

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure