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

• Published in: eLife 2013-10, Vol.2, p.e01120
• Main Authors: Boyle, Sean Michael, McInally, Shane, Ray, Anandasankar
• Format: Article
• Language: English
• Subjects: Animals; antenna; Arthropod Antennae - cytology; Arthropod Antennae - drug effects; Arthropod Antennae - metabolism; Binding sites; cheminformatics; Computer applications; Computer Simulation; Databases, Chemical; Drosophila; Drosophila melanogaster - drug effects; Drosophila melanogaster - physiology; Drosophila Proteins - chemistry; Drosophila Proteins - genetics; Drosophila Proteins - metabolism; electrophysiology; Gene Expression Regulation; Informatics; Insects; Ligands; Models, Molecular; Neural coding; Neuroscience; odorant receptor; Odorant receptors; Odorants - analysis; Odors; Olfactory Receptor Neurons - cytology; Olfactory Receptor Neurons - drug effects; Olfactory Receptor Neurons - metabolism; Olfactory system; Pharmaceuticals; Protein Binding; Receptors, Odorant - chemistry; Receptors, Odorant - genetics; Receptors, Odorant - metabolism; Signal Transduction; Small Molecule Libraries - chemistry; Small Molecule Libraries - pharmacology; Structure-Activity Relationship; Volatiles
• ISSN: 2050-084X; 2050-084X
• DOI: 10.7554/eLife.01120

Coding of information in the peripheral olfactory system depends on two fundamental : 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 (∼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 (∼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. DOI:http://dx.doi.org/10.7554/eLife.01120.001.