Perception of thousands of odors by a few hundreds of olfactory receptors (ORs) results from a combinatorial coding, in which one OR recognizes multiple odorants and an odorant is recognized by a specific group of ORs. Moreover, odorants could act both as agonists or antagonists depending on the OR. This dual agonist-antagonist combinatorial coding is in good agreement with behavioral and psychophysical observations of mixture perception. We previously described the odorant repertoire of a human OR, OR1G1, identifying both agonists and antagonists. In this paper, we performed a 3D-quantitative structure-activity relationship (3D-QSAR) study of these ligands. We obtained a double-alignment model explaining previously reported experimental activities and permitting to predict novel agonists and antagonists for OR1G1. These model predictions were experimentally validated. Thereafter, we evaluated the statistical link between OR1G1 response to odorants, 3D-QSAR categorization of OR1G1 ligands, and their olfactory description. We demonstrated that OR1G1 recognizes a group of odorants that share both 3D structural and perceptual qualities. We hypothesized that OR1G1 contributes to the coding of waxy, fatty, and rose odors in humans.

The human olfactory system recognizes a broad spectrum of odorants using approximately 400 different olfactory receptors (hORs). Although significant improvements of heterologous expression systems used to study interactions between ORs and odorant molecules have been made, screening the olfactory repertoire of hORs remains a tremendous challenge. We therefore developed a chemical systems level approach based on protein-protein association network to investigate novel hOR-odorant relationships. Using this new approach, we proposed and validated new bioactivities for odorant molecules and OR2W1, OR51E1 and OR5P3. As it remains largely unknown how human perception of odorants influence or prevent diseases, we also developed an odorant-protein matrix to explore global relationships between chemicals, biological targets and disease susceptibilities. We successfully experimentally demonstrated interactions between odorants and the cannabinoid receptor 1 (CB1) and the peroxisome proliferator-activated receptor gamma (PPARγ). Overall, these results illustrate the potential of integrative systems chemical biology to explore the impact of odorant molecules on human health, i.e. human odorome.

Habituation is a filter that optimizes the processing of information by our brain in all sensory modalities. It results in an unconscious reduced responsiveness to continuous or repetitive stimulation. In olfaction, the main question is whether habituation works the same way for any odorant or whether we habituate differently to each odorant? In particular, whether chemical, physical or perceptual cues can limit or increase habituation. To test this, the odour intensity of 32 odorants differing in physicochemical characteristics was rated by 58 participants continuously during 120s. Each odorant was delivered at a constant concentration. Results showed odorants differed significantly in habituation, highlighting the multifactoriality of habituation. Additionally habituation was predicted from 15 physico-chemical and perceptual characteristics of the odorants. The analysis highlighted the importance of trigeminality which is highly correlated to intensity and pleasantness. The vapour pressure, the molecular weight, the Odor Activity Value (OAV) and the number of double bonds mostly contributed to the modulation of habituation. Moreover, length of the carbon chain, number of conformers and hydrophobicity contributed to a lesser extent to the modulation of habituation. These results highlight new principles involved in the fundamental process of habituation, notably trigeminality and the physicochemical characteristics associated.

Interactions between a well-characterized protein, beta-lactoglobulin, and two flavor compounds, beta-ionone and gamma-decalactone, were studied by 2D NMR spectroscopy. NMR spectra were recorded in aqueous solution (pH 2.0, 12 mM NaCl, 10% D(2)O) under conditions such that beta-lactoglobulin is present in a monomeric state. TOCSY and NOESY spectra were recorded on the protein and the complexes between protein and ligands. The spectra of the NH-CH(alpha) region showed the cross-signals due to the coupling between N- and C-bonded protons in the polypeptide backbone. The observed chemical shift variations in the presence of ligands can be assigned to changes in the protein conformation. It appears that the side chains of several amino acids are affected by binding of gamma-decalactone point into the central cavity (Leu46, Ile56, Met107, and Gln120), whereas binding of beta-ionone affects amino acids located in a groove near the outer surface of the protein (Leu104, Tyr120, and Asp129), as illustrated by molecular visualization. This NMR study provides precise information of the location of binding and confirms the existence of two different binding sites for aroma compounds on beta-lactoglobulin, which was suggested in previous competition studies by fluorometry or affinity chromatography and by structural information obtained from infrared spectroscopy.

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This study aims to highlight the relationships between the structure of smell compounds and their odors. For this purpose, heterogeneous data sources were screened, and 6038 odorant compounds and their known associated odors (162 odor notes) were compiled, each individual molecule being represented with a set of 1024 structural fingerprint. Several dimensional reduction techniques (PCA, MDS, t-SNE and UMAP) with two clustering methods (k-means and agglomerative hierarchical clustering AHC) were assessed based on the calculated fingerprints. The combination of UMAP with k-means and AHC methods allowed to obtain a good representativeness of odors by clusters, as well as the best visualization of the proximity of odorants on the basis of their molecular structures. The presence or absence of molecular substructures has been calculated on odorant in order to link chemical groups to odors. The results of this analysis bring out some associations for both the odor notes and the chemical structures of the molecules such as “woody” and “spicy” notes with allylic and bicyclic structures, “balsamic” notes with unsaturated rings, both “sulfurous” and “citrus” with aldehydes, alcohols, carboxylic acids, amines and sulfur compounds, and “oily”, “fatty” and “fruity” characterized by esters and with long carbon chains. Overall, the use of UMAP associated to clustering is a promising method to suggest hypotheses on the odorant structure-odor relationships.

This paper reports a 3D-QSAR study using Catalyst software to explain the nature of interactions between flavor compounds and beta-lactoglobulin. A set of 35 compounds, for which dissociation constants were previously determined by affinity chromatography, was chosen. The set was divided into three subsets. An automated hypothesis generation, using HypoGen software, produced a model that made a valuable estimation of affinity and provided an explanation for the lack of correlation previously observed between the hydrophobicity of terpenes and the affinity for the protein. On the basis of these results, it appears that aroma binding to beta-lactoglobulin is caused by both hydrophobic interactions and hydrogen bonding, which plays a critical role. Catalyst appears to be a reliable tool for the application of 3D-QSAR study in aroma research.

The perception of odours is the result of the complex processing of a signal, which initiates at peripheral receptors and ends in the brain. Along this pathway, olfactory signal processing proceeds through several steps; each step possesses its own complexity, and all steps are also intricately connected. This review aims to describe the main intricate steps of olfactory processing in mammals, some of which remain unclear, and the close associations and overlapping nature of these steps. The causes of both the complexity and the variability of olfactory signals are examined: the nature of olfactory receptors, involving the diversity of the genome; the spatial organization of the olfactory epithelium (OE) and the olfactory bulb (OB); connections in the OB and from the OB to the brain; integration and processing in the brain, which leads to the final perception of odours; and odour recognition and odour identification, which is associated with the difficulty to verbalize a reliable description of the perception in humans. Finally, the last part of this review encompasses recent progress made to decipher and understand olfactory coding and focuses on computational approaches