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

[Lyq322]

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

I haven't finished reading the paper yet, but it seems like the authors found a way to predict receptor-odorant interactions using molecular descriptors. They used known ligands of a particular receptor to find the optimal set of molecular descriptors, which they used to screen new candidate ligands from databases. Functional experiments tested on Drosophila for 9 odorant receptors had a high success rate (∼71%) from the screen.

This spreadsheet contains their set of molecular descriptors optimized for each odorant receptor of Drosophila: elife-01120-supp1-v1.xlsx

Another interesting thing is they found both agonists (>50 spikes/s above the spontaneous activity of neuron) and inverse agonists (>50% reduction in spontaneous activity of neuron) from searching for structually similar compounds. Perhaps inverse agonists or antagonists can be used to mask the scent of another bad-smelling chemical? I just read the abstract of Antagonistic interactions between odorants alter human odor perception but it seems like this paper supports this idea. Another paper I found was Olfactory receptor antagonism between odorants which talks about how an odorant can be an agonist to some receptors and an antagonist to others, leading to the perceived intensity or quality of an odorant mixture to be not equal to the sum of its components.

Additionally, Olfactionbase has a list of odorants and the corresponding receptors they interact with, but I'm not sure whether the odorants are agonists or antagonists.