OlfactionBase: a repository to explore odors, odorants, olfactory receptors and odorant-receptor interactions

A deep position-encoding model for predicting olfactory perception from molecular structures and electrostatics

Predicting olfactory perceptions from odorant molecules is challenging due to the complex and potentially discontinuous nature of the perceptual space for smells. In this study, we introduce a deep learning model, Mol-PECO (Molecular Representation by Positional Encoding of Coulomb Matrix), designed to predict olfactory perceptions based on molecular structures and electrostatics. Mol-PECO learns the efficient embedding of molecules by utilizing the Coulomb matrix, which encodes atomic coordinates and charges, as an alternative of the adjacency matrix and its Laplacian eigenfunctions as positional encoding of atoms. With a comprehensive dataset of odor molecules and descriptors, Mol-PECO outperforms traditional machine learning methods using molecular fingerprints and graph neural networks based on adjacency matrices. The learned embeddings by Mol-PECO effectively capture the odor space, enabling global clustering of descriptors and local retrieval of similar odorants. This work contributes to a deeper understanding of the olfactory sense and its mechanisms.

Pred-O3, a web server to predict molecules, olfactory receptors and odor relationships

The sense of smell is a biological process involving volatile molecules that interact with proteins called olfactory receptors to transmit a nervous message that allows the recognition of a perceived odor. However, the relationships between odorant molecules, olfactory receptors and odors (O3) are far from being well understood due to the combinatorial olfactory codes and large family of olfactory receptors. This is the reason why, based on 5802 odorant molecules and their annotations to 863 olfactory receptors (human) and 7029 odors and flavors annotations, a web server called Pred-O3 has been designed to provide insights into olfaction. Predictive models based on Artificial Intelligence have been developed allowing to suggest olfactory receptors and odors associated with a new molecule. In addition, based on the encoding of the odorant molecule's structure, physicochemical features related to odors and/or olfactory receptors are proposed. Finally, based on the structural models of the 98 olfactory receptors a systematic docking protocol can be applied and suggest if a molecule can bind or not to an olfactory receptor. Therefore, Pred-O3 is well suited to aid in the design of new odorant molecules and assist in fragrance research and sensory neuroscience. Pred-O3 is accessible at ' https://odor.rpbs.univ-paris-diderot.fr/'.

A high-throughput behavioral screening platform for measuring chemotaxis by C. elegans

Throughout history, humans have relied on plants as a source of medication, flavoring, and food. Plants synthesize large chemical libraries and release many of these compounds into the rhizosphere and atmosphere where they affect animal and microbe behavior. To survive, nematodes must have evolved the sensory capacity to distinguish plant-made small molecules (SMs) that are harmful and must be avoided from those that are beneficial and should be sought. This ability to classify chemical cues as a function of their value is fundamental to olfaction and represents a capacity shared by many animals, including humans. Here, we present an efficient platform based on multiwell plates, liquid handling instrumentation, inexpensive optical scanners, and bespoke software that can efficiently determine the valence (attraction or repulsion) of single SMs in the model nematode, Caenorhabditis elegans. Using this integrated hardware-wetware-software platform, we screened 90 plant SMs and identified 37 that attracted or repelled wild-type animals but had no effect on mutants defective in chemosensory transduction. Genetic dissection indicates that for at least 10 of these SMs, response valence emerges from the integration of opposing signals, arguing that olfactory valence is often determined by integrating chemosensory signals over multiple lines of information. This study establishes that C. elegans is an effective discovery engine for determining chemotaxis valence and for identifying natural products detected by the chemosensory nervous system.

An efficient behavioral screening platform classifies natural products and other chemical cues according to their chemosensory valence in C. elegans

Throughout history, humans have relied on plants as a source of medication, flavoring, and food. Plants synthesize large chemical libraries and release many of these compounds into the rhizosphere and atmosphere where they affect animal and microbe behavior. To survive, nematodes must have evolved the sensory capacity to distinguish plant-made small molecules (SMs) that are harmful and must be avoided from those that are beneficial and should be sought. This ability to classify chemical cues as a function of their value is fundamental to olfaction, and represents a capacity shared by many animals, including humans. Here, we present an efficient platform based on multi-well plates, liquid handling instrumentation, inexpensive optical scanners, and bespoke software that can efficiently determine the valence (attraction or repulsion) of single SMs in the model nematode, Caenorhabditis elegans. Using this integrated hardware-wetware-software platform, we screened 90 plant SMs and identified 37 that attracted or repelled wild-type animals, but had no effect on mutants defective in chemosensory transduction. Genetic dissection indicates that for at least 10 of these SMs, response valence emerges from the integration of opposing signals, arguing that olfactory valence is often determined by integrating chemosensory signals over multiple lines of information. This study establishes that C. elegans is an effective discovery engine for determining chemotaxis valence and for identifying natural products detected by the chemosensory nervous system.

M2OR: a database of olfactory receptor-odorant pairs for understanding the molecular mechanisms of olfaction

Mammalian sense of smell is triggered by interaction between odorant molecules and a class of proteins, called olfactory receptors (ORs). These receptors, expressed at the surface of olfactory sensory neurons, encode myriad of distinct odors via a sophisticated activation pattern. However, determining the molecular recognition spectrum of ORs remains a major challenge. The Molecule to Olfactory Receptor database (M2OR, https://m2or.chemsensim.fr/) provides curated data that allows an easy exploration of the current state of the research on OR-molecule interaction. We have gathered a database of 75,050 bioassay experiments for 51 395 distinct OR-molecule pairs. Drawn from published literature and public databases, M2OR contains information about OR responses to molecules and their mixtures, receptor sequences and experimental details. Users can obtain information on the activity of a chosen molecule or a group of molecules, or search for agonists for a specific OR or a group of ORs. Advanced search allows for fine-grained queries using various metadata such as species or experimental assay system, and the database can be queried by multiple inputs via a batch search. Finally, for a given search query, users can access and download a curated aggregation of the experimental data into a binarized combinatorial code of olfaction.

Developing human olfactory network and exploring olfactory receptor-odorant interaction

The Olfactory receptor (OR)-odorant interactions are perplexed. ORs can bind to structurally diverse odorants associated with one or more odor percepts. Various attempts have been made to understand the intricacies of OR-odorant interaction. In this study, experimentally documented OR-odorant interactions are investigated comprehensively to; (a) suggest potential odor percepts for ORs based on the OR-OR network; (b) determine how odorants interacting with specific ORs differ in terms of inherent pharmacophoric features and molecular properties, (c) identify molecular interactions that explained OR-odorant interactions of selective ORs; and (d) predict the probable role of ORs other than olfaction. Human olfactory receptor network (hORnet) is developed to study possible odor percepts for ORs. We identified six molecular properties which showed variation and significant patterns to differentiate odorants binding with five ORs. The pharmacophore analysis revealed that odorants subset of five ORs follow similar pharmacophore hypothesis, (one hydrogen acceptor and two hydrophobic regions) but differ in terms of distance and orientation of pharmacophoric features. To ascertain the binding site residues and key interactions between the selected ORs and their interacting odorants, 3D-structure modelling, docking and molecular dynamics studies were carried out. Lastly, the potential role of ORs beyond olfaction is explored. A human OR-OR network was developed to suggest possible odor percepts for ORs using empirically proven OR-odorant interactions. We sought to find out significant characteristics, molecular properties, and molecular interactions that could explain OR-odorant interactions and add to the understanding of the complex issue of odor perception.Communicated by Ramaswamy H. Sarma.

XGBoost odor prediction model: finding the structure-odor relationship of odorant molecules using the extreme gradient boosting algorithm

Determining the structure-odor relationship has always been a very challenging task. The main challenge in investigating the correlation between the molecular structure and its associated odor is the ambiguous and obscure nature of verbally defined odor descriptors, particularly when the odorant molecules are from different sources. With the recent developments in machine learning (ML) technology, ML and data analytic techniques are significantly being used for quantitative structure-activity relationship (QSAR) in the chemistry domain toward knowledge discovery where the traditional Edisonian methods have not been useful. The smell perception of odorant molecules is one of the aforementioned tasks, as olfaction is one of the least understood senses as compared to other senses. In this study, the XGBoost odor prediction model was generated to classify smells of odorant molecules from their SMILES strings. We first collected the dataset of 1278 odorant molecules with seven basic odor descriptors, and then 1875 physicochemical properties of odorant molecules were calculated. To obtain relevant physicochemical features, a feature reduction algorithm called PCA was also employed. The ML model developed in this study was able to predict all seven basic smells with high precision (>99%) and high sensitivity (>99%) when tested on an independent test dataset. The results of the proposed study were also compared with three recently conducted studies. The results indicate that the XGBoost-PCA model performed better than the other models for predicting common odor descriptors. The methodology and ML model developed in this study may be helpful in understanding the structure-odor relationship.Communicated by Ramaswamy H. Sarma.

Human Olfactory Receptor Sensor for Odor Reconstitution

Among the five human senses, light, sound, and force perceived by the eye, ear, and skin, respectively are physical phenomena, and therefore can be easily measured and expressed as objective, univocal, and simple digital data with physical quantity. However, as taste and odor molecules perceived by the tongue and nose are chemical phenomena, it has been difficult to express them as objective and univocal digital data, since no reference chemicals can be defined. Therefore, while the recording, saving, transmitting to remote locations, and replaying of human visual, auditory, and tactile information as digital data in digital devices have been realized (this series of data flow is defined as DX (digital transformation) in this review), the DX of human taste and odor information is not yet in the realization stage. Particularly, since there are at least 400,000 types of odor molecules and an infinite number of complex odors that are mixtures of these molecules, it has been considered extremely difficult to realize "human olfactory DX" by converting all odors perceived by human olfaction into digital data. In this review, we discuss the current status and future prospects of the development of "human olfactory DX", which we believe can be realized by utilizing odor sensors that employ the olfactory receptors (ORs) that support human olfaction as sensing molecules (i.e., human OR sensor).

Data-Driven Elucidation of Flavor Chemistry

Flavor molecules are commonly used in the food industry to enhance product quality and consumer experiences but are associated with potential human health risks, highlighting the need for safer alternatives. To address these health-associated challenges and promote reasonable application, several databases for flavor molecules have been constructed. However, no existing studies have comprehensively summarized these data resources according to quality, focused fields, and potential gaps. Here, we systematically summarized 25 flavor molecule databases published within the last 20 years and revealed that data inaccessibility, untimely updates, and nonstandard flavor descriptions are the main limitations of current studies. We examined the development of computational approaches (e.g., machine learning and molecular simulation) for the identification of novel flavor molecules and discussed their major challenges regarding throughput, model interpretability, and the lack of gold-standard data sets for equitable model evaluation. Additionally, we discussed future strategies for the mining and designing of novel flavor molecules based on multi-omics and artificial intelligence to provide a new foundation for flavor science research.

Smelling the Disease: Diagnostic Potential of Breath Analysis

Breath analysis is a relatively recent field of research with much promise in scientific and clinical studies. Breath contains endogenously produced volatile organic components (VOCs) resulting from metabolites of ingested precursors, gut and air-passage bacteria, environmental contacts, etc. Numerous recent studies have suggested changes in breath composition during the course of many diseases, and breath analysis may lead to the diagnosis of such diseases. Therefore, it is important to identify the disease-specific variations in the concentration of breath to diagnose the diseases. In this review, we explore methods that are used to detect VOCs in laboratory settings, VOC constituents in exhaled air and other body fluids (e.g., sweat, saliva, skin, urine, blood, fecal matter, vaginal secretions, etc.), VOC identification in various diseases, and recently developed electronic (E)-nose-based sensors to detect VOCs. Identifying such VOCs and applying them as disease-specific biomarkers to obtain accurate, reproducible, and fast disease diagnosis could serve as an alternative to traditional invasive diagnosis methods. However, the success of VOC-based identification of diseases is limited to laboratory settings. Large-scale clinical data are warranted for establishing the robustness of disease diagnosis. Also, to identify specific VOCs associated with illness states, extensive clinical trials must be performed using both analytical instruments and electronic noses equipped with stable and precise sensors.

Do Synthetic Fragrances in Personal Care and Household Products Impact Indoor Air Quality and Pose Health Risks?

Fragrance compounds (synthetic fragrances or natural essential oils) comprise formulations of specific combinations of individual materials or mixtures. Natural or synthetic scents are core constituents of personal care and household products (PCHPs) that impart attractiveness to the olfactory perception and disguise the unpleasant odor of the formula components of PCHPs. Fragrance chemicals have beneficial properties that allow their use in aromatherapy. However, because fragrances and formula constituents of PCHPs are volatile organic compounds (VOCs), vulnerable populations are exposed daily to variable indoor concentrations of these chemicals. Fragrance molecules may trigger various acute and chronic pathological conditions because of repetitive human exposure to indoor environments at home and workplaces. The negative impact of fragrance chemicals on human health includes cutaneous, respiratory, and systemic effects (e.g., headaches, asthma attacks, breathing difficulties, cardiovascular and neurological problems) and distress in workplaces. Pathologies related to synthetic perfumes are associated with allergic reactions (e.g., cutaneous and pulmonary hypersensitivity) and potentially with the perturbation of the endocrine-immune-neural axis. The present review aims to critically call attention to odorant VOCs, particularly synthetic fragrances and associated formula components of PCHPs, potentially impacting indoor air quality and negatively affecting human health.

Genetic and functional odorant receptor variation in the Homo lineage

Humans, Neanderthals, and Denisovans independently adapted to a wide range of geographic environments and their associated food odors. Using ancient DNA sequences, we explored the in vitro function of thirty odorant receptor genes in the genus Homo. Our extinct relatives had highly conserved olfactory receptor sequence, but humans did not. Variations in odorant receptor protein sequence and structure may have produced variation in odor detection and perception. Variants led to minimal changes in specificity but had more influence on functional sensitivity. The few Neanderthal variants disturbed function, whereas Denisovan variants increased sensitivity to sweet and sulfur odors. Geographic adaptations may have produced greater functional variation in our lineage, increasing our olfactory repertoire and expanding our adaptive capacity. Our survey of olfactory genes and odorant receptors suggests that our genus has a shared repertoire with possible local ecological adaptations.

Synthesis of Cyclic Fragrances via Transformations of Alkenes, Alkynes and Enynes: Strategies and Recent Progress

With increasing demand for customized commodities and the greater insight and understanding of olfaction, the synthesis of fragrances with diverse structures and odor characters has become a core task. Recent progress in organic synthesis and catalysis enables the rapid construction of carbocycles and heterocycles from readily available unsaturated molecular building blocks, with increased selectivity, atom economy, sustainability and product diversity. In this review, synthetic methods for creating cyclic fragrances, including both natural and synthetic ones, will be discussed, with a focus on the key transformations of alkenes, alkynes, dienes and enynes. Several strategies will be discussed, including cycloaddition, catalytic cyclization, ring-closing metathesis, intramolecular addition, and rearrangement reactions. Representative examples and the featured olfactory investigations will be highlighted, along with some perspectives on future developments in this area.