Predicting olfactory receptor neuron responses from odorant structure

  • 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. Conclusions 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.

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Author:Michael SchmukerORCiDGND, Marien De Bruyne, Melanie Hähnel, Gisbert SchneiderORCiDGND
URN:urn:nbn:de:hebis:30-44204
URL:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1994056
DOI:https://doi.org/10.1186/1752-153X-1-11
ISSN:1752-153X
Pubmed Id:https://pubmed.ncbi.nlm.nih.gov/17880742
Parent Title (English):Chemistry central journal
Publisher:BioMed Central
Place of publication:London
Document Type:Article
Language:English
Date of Publication (online):2007/05/29
Date of first Publication:2007/05/04
Publishing Institution:Universitätsbibliothek Johann Christian Senckenberg
Release Date:2007/05/29
Volume:1
Issue:11
Page Number:10
First Page:1
Last Page:10
Note:
© 2007 Schmuker et al. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Source:Chemistry Central Journal 2007, 1:11. - doi:10.1186/1752-153X-1-11. - http://journal.chemistrycentral.com/content/1/1/11
HeBIS-PPN:188077111
Institutes:Biochemie, Chemie und Pharmazie / Biochemie und Chemie
Dewey Decimal Classification:5 Naturwissenschaften und Mathematik / 57 Biowissenschaften; Biologie / 570 Biowissenschaften; Biologie
Sammlungen:Sammlung Biologie / Sondersammelgebiets-Volltexte
Licence (German):License LogoCreative Commons - Namensnennung 2.0