Expanding the olfactory code by in silico decoding of odor-receptor chemical space

Coding of information in the peripheral olfactory system depends on two fundamental factors: interaction of individual odors with subsets of the odorant receptor repertoire and mode of signaling that an individual receptor-odor interaction elicits, activation or inhibition. We develop a cheminformatics pipeline that predicts receptor-odorant interactions from a large collection of chemical structures (>240,000) for receptors that have been tested to a smaller panel of odorants (similar to 100). Using a computational approach, we first identify shared structural features from known ligands of individual receptors. We then use these features to screen in silico new candidate ligands from >240,000 potential volatiles for several Odorant receptors (Ors) in the Drosophila antenna. Functional experiments from 9 Ors support a high success rate (similar to 71%) for the screen, resulting in identification of numerous new activators and inhibitors. Such computational prediction of receptor-odor interactions has the potential to enable systems level analysis of olfactory receptor repertoires in organisms.