Achievement

The olfactory systems of plant and human insect pests are incredibly complex, containing tens to hundreds of unique odor receptors that ascertain the identity and concentration of chemicals in the environment. To understand odor receptor responses on a systems level, UC Riverside’s ChemGen IGERT trainee Sean Boyle and Professor Anandasankar Ray designed an in silico screening approach to predict receptor-odor interactions in Drosophila, as a model for other pests. The prediction pipeline identified common structural features of odorants that are highly correlated with odor activity for individual receptors. An untested odor space of ~480,000 putative volatiles was evaluated. Single unit electrophysiology assays of selected molecules resulted in a ~71% success rate of identification of new activators and inhibitors of specific receptors. This knowledge was used to predict natural compounds as candidate insect repellents, identifying several already approved as safe for human food use.