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 cheminformat...
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doaj-ad0a34b133fa436091fb08726af74f022021-05-04T22:32:28ZengeLife Sciences Publications LtdeLife2050-084X2013-10-01210.7554/eLife.01120Expanding the olfactory code by in silico decoding of odor-receptor chemical spaceSean Michael Boyle0Shane McInally1Anandasankar Ray2Genetics, Genomics, and Bioinformatics Program, University of California, Riverside, Riverside, United StatesDepartment of Entomology, University of California, Riverside, Riverside, United StatesGenetics, Genomics, and Bioinformatics Program, University of California, Riverside, Riverside, United States; Department of Entomology, University of California, Riverside, Riverside, United States; Institute of Integrative Genome Biology, University of California, Riverside, Riverside, United StatesCoding 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 (∼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.https://elifesciences.org/articles/01120odorant receptorantennaelectrophysiologycheminformatics |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Sean Michael Boyle Shane McInally Anandasankar Ray |
spellingShingle |
Sean Michael Boyle Shane McInally Anandasankar Ray Expanding the olfactory code by in silico decoding of odor-receptor chemical space eLife odorant receptor antenna electrophysiology cheminformatics |
author_facet |
Sean Michael Boyle Shane McInally Anandasankar Ray |
author_sort |
Sean Michael Boyle |
title |
Expanding the olfactory code by in silico decoding of odor-receptor chemical space |
title_short |
Expanding the olfactory code by in silico decoding of odor-receptor chemical space |
title_full |
Expanding the olfactory code by in silico decoding of odor-receptor chemical space |
title_fullStr |
Expanding the olfactory code by in silico decoding of odor-receptor chemical space |
title_full_unstemmed |
Expanding the olfactory code by in silico decoding of odor-receptor chemical space |
title_sort |
expanding the olfactory code by in silico decoding of odor-receptor chemical space |
publisher |
eLife Sciences Publications Ltd |
series |
eLife |
issn |
2050-084X |
publishDate |
2013-10-01 |
description |
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 (∼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. |
topic |
odorant receptor antenna electrophysiology cheminformatics |
url |
https://elifesciences.org/articles/01120 |
work_keys_str_mv |
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