Research article
Predicting olfactory receptor neuron responses from odorant structure
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* Corresponding author: Michael Schmuker schmuker@zedat.fu-berlin.de
1 Johann Wolfgang Goethe Universität, Beilstein Endowed Chair for Cheminformatics, Institute of Organic Chemistry and Chemical Biology, Siesmayerstr. 70, 60323 Frankfurt am Main, Germany
2 Freie Universität Berlin, Institute of Biology-Neurobiology, Königin-Luise-Str. 28–30, 14195 Berlin, Germany
3 Monash University, School of Biological Sciences, Wellington Road, Clayton VIC 3800, Australia
4 (present address) Freie Universität Berlin, Institute of Biology-Neurobiology, Königin-Luise-Str. 28–30, 14195 Berlin, Germany
Chemistry Central Journal 2007, 1:11 doi:10.1186/1752-153X-1-11
Published: 4 May 2007Abstract
Background
Olfactory receptors work at the interface between the chemical world of volatile molecules and the perception of scent in the brain. Their main purpose is to translate chemical space into information that can be processed by neural circuits. Assuming that these receptors have evolved to cope with this task, the analysis of their coding strategy promises to yield valuable insight in how to encode chemical information in an efficient way.
Results
We mimicked olfactory coding by modeling responses of primary olfactory neurons to small molecules using a large set of physicochemical molecular descriptors and artificial neural networks. We then tested these models by recording in vivo receptor neuron responses to a new set of odorants and successfully predicted the responses of five out of seven receptor neurons. Correlation coefficients ranged from 0.66 to 0.85, demonstrating the applicability of our approach for the analysis of olfactory receptor activation data. The molecular descriptors that are best-suited for response prediction vary for different receptor neurons, implying that each receptor neuron detects a different aspect of chemical space. Finally, we demonstrate that receptor responses themselves can be used as descriptors in a predictive model of neuron activation.
Conclusion
The chemical meaning of molecular descriptors helps understand structure-response relationships for olfactory receptors and their "receptive fields". Moreover, it is possible to predict receptor neuron activation from chemical structure using machine-learning techniques, although this is still complicated by a lack of training data.