TY  - JOUR
A1  - Schmuker, Michael
A1  - De Bruyne, Marien
A1  - Hähnel, Melanie
A1  - Schneider, Gisbert
T1  - Predicting olfactory receptor neuron responses from odorant structure
T2  - Chemistry central journal
N2  - 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.
Y1  - 2007
UR  - http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/1039
UR  - https://nbn-resolving.org/urn:nbn:de:hebis:30-44204
UR  - https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1994056
SN  - 1752-153X
N1  - © 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.
VL  - 1
IS  - 11
SP  - 1
EP  - 10
PB  - BioMed Central
CY  - London
ER  -