Predicting olfactory receptor neuron responses from odorant structure

iORbase: A database for the prediction of the structures and functions of insect olfactory receptors

Insect olfactory receptors (iORs) with atypical 7-transmembrane domains, unlike Chordata olfactory receptors, are not in the GPCR protein family. iORs selectively bind to volatile ligands in the environment and affect essential insect behaviors. In this study, we constructed a new platform (iORbase, https://www.iorbase.com) for the structural and functional analysis of iORs based on a combined algorithm for gene annotation and protein structure prediction. Moreover, it provides the option to calculate the binding affinities and binding residues between iORs and pheromone molecules by virtual screening of docking. Furthermore, iORbase supports the automatic structural and functional prediction of user-submitted iORs or pheromones. iORbase contains the well-analyzed results of approximately 6 000 iORs and their 3D protein structures identified from 59 insect species and 2 077 insect pheromones from the literature, as well as approximately 12 million pairs of simulated interactions between functional iORs and pheromones. We also built 4 online modules, iORPDB, iInteraction, iModelTM, and iOdorTool to easily retrieve and visualize the 3D structures and interactions. iORbase can help greatly improve the experimental efficiency and success rate, identify new insecticide targets, or develop electronic nose technology. This study will shed light on the olfactory recognition mechanism and evolutionary characteristics from the perspectives of omics and macroevolution.

Higher-order olfactory neurons in the lateral horn support odor valence and odor identity coding in Drosophila

Understanding neuronal representations of odor-evoked activities and their progressive transformation from the sensory level to higher brain centers features one of the major aims in olfactory neuroscience. Here, we investigated how odor information is transformed and represented in higher-order neurons of the lateral horn, one of the higher olfactory centers implicated in determining innate behavior, using Drosophila melanogaster. We focused on a subset of third-order glutamatergic lateral horn neurons (LHNs) and characterized their odor coding properties in relation to their presynaptic partner neurons, the projection neurons (PNs) by two-photon functional imaging. We show that odors evoke reproducible, stereotypic, and odor-specific response patterns in LHNs. Notably, odor-evoked responses in these neurons are valence-specific in a way that their response amplitude is positively correlated with innate odor preferences. We postulate that this valence-specific activity is the result of integrating inputs from multiple olfactory channels through second-order neurons. GRASP and micro-lesioning experiments provide evidence that glutamatergic LHNs obtain their major excitatory input from uniglomerular PNs, while they receive an odor-specific inhibition through inhibitory multiglomerular PNs. In summary, our study indicates that odor representations in glutamatergic LHNs encode hedonic valence and odor identity and primarily retain the odor coding properties of second-order neurons.

Smell compounds classification using UMAP to increase knowledge of odors and molecular structures linkages

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.

Olfactory processing in the lateral horn of Drosophila

Sensing olfactory signals in the environment represents a crucial and significant task of sensory systems in almost all organisms to facilitate survival and reproduction. Notably, the olfactory system of diverse animal phyla shares astonishingly many fundamental principles with regard to anatomical and functional properties. Binding of odor ligands by chemosensory receptors present in the olfactory peripheral organs leads to a neuronal activity that is conveyed to first and higher-order brain centers leading to a subsequent odor-guided behavioral decision. One of the key centers for integrating and processing innate olfactory behavior is the lateral horn (LH) of the protocerebrum in insects. In recent years the LH of Drosophila has garnered increasing attention and many studies have been dedicated to elucidate its circuitry. In this review we will summarize the recent advances in mapping and characterizing LH-specific cell types, their functional properties with respect to odor tuning, their neurotransmitter profiles, their connectivity to pre-synaptic and post-synaptic partner neurons as well as their impact for olfactory behavior as known so far.

Computational exploration of molecular receptive fields in the olfactory bulb reveals a glomerulus-centric chemical map

Progress in olfactory research is currently hampered by incomplete knowledge about chemical receptive ranges of primary receptors. Moreover, the chemical logic underlying the arrangement of computational units in the olfactory bulb has still not been resolved. We undertook a large-scale approach at characterising molecular receptive ranges (MRRs) of glomeruli in the dorsal olfactory bulb (dOB) innervated by the MOR18-2 olfactory receptor, also known as Olfr78, with human ortholog OR51E2. Guided by an iterative approach that combined biological screening and machine learning, we selected 214 odorants to characterise the response of MOR18-2 and its neighbouring glomeruli. We found that a combination of conventional physico-chemical and vibrational molecular descriptors performed best in predicting glomerular responses using nonlinear Support-Vector Regression. We also discovered several previously unknown odorants activating MOR18-2 glomeruli, and obtained detailed MRRs of MOR18-2 glomeruli and their neighbours. Our results confirm earlier findings that demonstrated tunotopy, that is, glomeruli with similar tuning curves tend to be located in spatial proximity in the dOB. In addition, our results indicate chemotopy, that is, a preference for glomeruli with similar physico-chemical MRR descriptions being located in spatial proximity. Together, these findings suggest the existence of a partial chemical map underlying glomerular arrangement in the dOB. Our methodology that combines machine learning and physiological measurements lights the way towards future high-throughput studies to deorphanise and characterise structure-activity relationships in olfaction.

Sequence-Based Prediction of Olfactory Receptor Responses

Computational prediction of how strongly an olfactory receptor (OR) responds to various odors can help in bridging the widening gap between the large number of receptors that have been sequenced and the small number of experiments measuring their responses. Previous efforts in this area have predicted the responses of a receptor to some odors, using the known responses of the same receptor to other odors. Here, we present a method to predict the responses of a receptor without any known responses by using available data about the responses of other conspecific receptors and their sequences. We applied this method to ORs in insects Drosophila melanogaster (both adult and larva) and Anopheles gambiae and to mouse and human ORs. We found the predictions to be in significant agreement with the experimental measurements. The method also provides clues about the response-determining positions within the receptor sequences.

Agonists of G-Protein-Coupled Odorant Receptors Are Predicted from Chemical Features

Predicting the activity of chemicals for a given odorant receptor is a longstanding challenge. Here the activity of 258 chemicals on the human G-protein-coupled odorant receptor (OR)51E1, also known as prostate-specific G-protein-coupled receptor 2 (PSGR2), was virtually screened by machine learning using 4884 chemical descriptors as input. A systematic control by functional in vitro assays revealed that a support vector machine algorithm accurately predicted the activity of a screened library. It allowed us to identify two novel agonists in vitro for OR51E1. The transferability of the protocol was assessed on OR1A1, OR2W1, and MOR256-3 odorant receptors, and, in each case, novel agonists were identified with a hit rate of 39-50%. We further show how ligands' efficacy is encoded into residues within OR51E1 cavity using a molecular modeling protocol. Our approach allows widening the chemical spaces associated with odorant receptors. This machine-learning protocol based on chemical features thus represents an efficient tool for screening ligands for G-protein-coupled odorant receptors that modulate non-olfactory functions or, upon combinatorial activation, give rise to our sense of smell.

Odorant receptors of Drosophila are sensitive to the molecular volume of odorants

Which properties of a molecule define its odor? This is a basic yet unanswered question regarding the olfactory system. The olfactory system of Drosophila has a repertoire of approximately 60 odorant receptors. Molecules bind to odorant receptors with different affinities and activate them with different efficacies, thus providing a combinatorial code that identifies odorants. We hypothesized that the binding affinity of an odorant-receptor pair is affected by their relative sizes. The maximum affinity can be attained when the molecular volume of an odorant matches the volume of the binding pocket. The affinity drops to zero when the sizes are too different, thus obscuring the effects of other molecular properties. We developed a mathematical formulation of this hypothesis and verified it using Drosophila data. We also predicted the volume and structural flexibility of the binding site of each odorant receptor; these features significantly differ between odorant receptors. The differences in the volumes and structural flexibilities of different odorant receptor binding sites may explain the difference in the scents of similar molecules with different sizes.

Applying medicinal chemistry strategies to understand odorant discrimination

Associating an odorant's chemical structure with its percept is a long-standing challenge. One hindrance may come from the adoption of the organic chemistry scheme of molecular description and classification. Chemists classify molecules according to characteristics that are useful in synthesis or isolation, but which may be of little importance to a biological sensory system. Accordingly, we look to medicinal chemistry, which emphasizes biological function over chemical form, in an attempt to discern which among the many molecular features are most important for odour discrimination. Here we use medicinal chemistry concepts to assemble a panel of molecules to test how heteroaromatic ring substitution of the benzene ring will change the odour percept of acetophenone. This work allows us to describe an extensive rule in odorant detection by mammalian olfactory receptors. Whereas organic chemistry would have predicted the ring size and composition to be key features, our work reveals that the topological polar surface area is the key feature for the discrimination of these odorants.

Understanding the basis of odour perception and discrimination is a challenging task, due to the inherent complexity of the olfactory system. Here, the authors use a medicinal chemistry approach to derive biologically relevant rules for odorant classification.

Direct nuclear magnetic resonance observation of odorant binding to mouse odorant receptor MOR244-3

Mammals are able to perceive and differentiate a great number of structurally diverse odorants through the odorant's interaction with odorant receptors (ORs), proteins found within the cell membrane of olfactory sensory neurons. The natural gas industry has used human olfactory sensitivity to sulfur compounds (thiols, sulfides, etc.) to increase the safety of fuel gas transport, storage, and use through the odorization of this product. In the United States, mixtures of sulfur compounds are used, but the major constituent of odorant packages is 2-methylpropane-2-thiol, also known as tert-butyl mercaptan. It has been fundamentally challenging to understand olfaction and odorization due to the low affinity of odorous ligands to the ORs and the difficulty in expressing a sufficient number of OR proteins. Here, we directly observed the binding of tert-butyl mercaptan and another odiferous compound, cis-cyclooctene, to mouse OR MOR244-3 on living cells by saturation transfer difference (STD) nuclear magnetic resonance (NMR) spectroscopy. This effort lays the groundwork for resolving molecular mechanisms responsible for ligand binding and resulting signaling, which in turn will lead to a clearer understanding of odorant recognition and competition.

Hybrid Network Model for "Deep Learning" of Chemical Data: Application to Antimicrobial Peptides

We present a "deep" network architecture for chemical data analysis and classification together with a prospective proof-of-concept application. The model features a self-organizing map (SOM) as the input layer of a feedforward neural network. The SOM converts molecular descriptors to a two-dimensional image for further processing. We implemented lateral neuron inhibition for contrast enhancement. The model achieved improved classification accuracy and predictive robustness compared to feedforward network classifiers lacking the SOM layer. By nonlinear dimensionality reduction the networks extracted meaningful chemical features from the data and outperformed linear principal component analysis (PCA). The learning machine was trained on the sequence-length independent recognition of antibacterial peptides and correctly predicted the killing activity of a synthetic test peptide against Staphylococcus aureus in an in vitro experiment.

Understanding the Odour Spaces: A Step towards Solving Olfactory Stimulus-Percept Problem

Odours are highly complex, relying on hundreds of receptors, and people are known to disagree in their linguistic descriptions of smells. It is partly due to these facts that, it is very hard to map the domain of odour molecules or their structure to that of perceptual representations, a problem that has been referred to as the Structure-Odour-Relationship. We collected a number of diverse open domain databases of odour molecules having unorganised perceptual descriptors, and developed a graphical method to find the similarity between perceptual descriptors; which is intuitive and can be used to identify perceptual classes. We then separately projected the physico-chemical and perceptual features of these molecules in a non-linear dimension and clustered the similar molecules. We found a significant overlap between the spatial positioning of the clustered molecules in the physico-chemical and perceptual spaces. We also developed a statistical method of predicting the perceptual qualities of a novel molecule using its physico-chemical properties with high receiver operating characteristics(ROC).

Computational Approaches for Decoding Select Odorant-Olfactory Receptor Interactions Using Mini-Virtual Screening

Olfactory receptors (ORs) belong to the class A G-Protein Coupled Receptor superfamily of proteins. Unlike G-Protein Coupled Receptors, ORs exhibit a combinatorial response to odors/ligands. ORs display an affinity towards a range of odor molecules rather than binding to a specific set of ligands and conversely a single odorant molecule may bind to a number of olfactory receptors with varying affinities. The diversity in odor recognition is linked to the highly variable transmembrane domains of these receptors. The purpose of this study is to decode the odor-olfactory receptor interactions using in silico docking studies. In this study, a ligand (odor molecules) dataset of 125 molecules was used to carry out in silico docking using the GLIDE docking tool (SCHRODINGER Inc Pvt LTD). Previous studies, with smaller datasets of ligands, have shown that orthologous olfactory receptors respond to similarly-tuned ligands, but are dramatically different in their efficacy and potency. Ligand docking results were applied on homologous pairs (with varying sequence identity) of ORs from human and mouse genomes and ligand binding residues and the ligand profile differed among such related olfactory receptor sequences. This study revealed that homologous sequences with high sequence identity need not bind to the same/ similar ligand with a given affinity. A ligand profile has been obtained for each of the 20 receptors in this analysis which will be useful for expression and mutation studies on these receptors.

Nature's chemical signatures in human olfaction: a foodborne perspective for future biotechnology

The biocatalytic production of flavor naturals that determine chemosensory percepts of foods and beverages is an ever challenging target for academic and industrial research. Advances in chemical trace analysis and post-genomic progress at the chemistry-biology interface revealed odor qualities of nature's chemosensory entities to be defined by odorant-induced olfactory receptor activity patterns. Beyond traditional views, this review and meta-analysis now shows characteristic ratios of only about 3 to 40 genuine key odorants for each food, from a group of about 230 out of circa 10 000 food volatiles. This suggests the foodborn stimulus space has co-evolved with, and roughly match our circa 400 olfactory receptors as best natural agonists. This perspective gives insight into nature's chemical signatures of smell, provides the chemical odor codes of more than 220 food samples, and beyond addresses industrial implications for producing recombinants that fully reconstruct the natural odor signatures for use in flavors and fragrances, fully immersive interactive virtual environments, or humanoid bioelectronic noses.

Identification of odorant-receptor interactions by global mapping of the human odorome

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.

Sense of achievement

Computational techniques developed to predict if odorants will interact with receptors in the olfactory system have achieved a success rate of 70%.

Physicochemical vs. Vibrational Descriptors for Prediction of Odor Receptor Responses

Responses of olfactory receptors (ORs) can be predicted by applying machine learning methods on a multivariate encoding of an odorant's chemical structure. Physicochemical descriptors that encode features of the molecular graph are a popular choice for such an encoding. Here, we explore the EVA descriptor set, which encodes features derived from the vibrational spectrum of a molecule. We assessed the performance of Support Vector Regression (SVR) and Random Forest Regression (RFR) to predict the gradual response of Drosophila ORs. We compared a 27-dimensional variant of the EVA descriptor against a set of 1467 descriptors provided by the eDragon software package, and against a 32-dimensional subset thereof that has been proposed as the basis for an odor metric consisting of 32 descriptors (HADDAD). The best prediction performance was reproducibly achieved using SVR on the highest-dimensional feature set. The low-dimensional EVA and HADDAD feature sets predicted odor-OR interactions with similar accuracy. Adding charge and polarizability information to the EVA descriptor did not improve the results but rather decreased predictive power. Post-hoc in vivo measurements confirmed these results. Our findings indicate that EVA provides a meaningful low-dimensional representation of odor space, although EVA hardly outperformed "classical" descriptor sets.

Expanding the olfactory code by in silico decoding of odor-receptor chemical space

Coding of information in the peripheral olfactory system depends on two fundamental factors: interaction of individual odors with subsets of the odorant receptor repertoire and mode of signaling that an individual receptor-odor interaction elicits, activation or inhibition. We develop a cheminformatics pipeline that predicts receptor–odorant interactions from a large collection of chemical structures (>240,000) for receptors that have been tested to a smaller panel of odorants (∼100). Using a computational approach, we first identify shared structural features from known ligands of individual receptors. We then use these features to screen in silico new candidate ligands from >240,000 potential volatiles for several Odorant receptors (Ors) in the Drosophila antenna. Functional experiments from 9 Ors support a high success rate (∼71%) for the screen, resulting in identification of numerous new activators and inhibitors. Such computational prediction of receptor–odor interactions has the potential to enable systems level analysis of olfactory receptor repertoires in organisms.

DOI:http://dx.doi.org/10.7554/eLife.01120.001

Although our sense of smell is regarded as inferior to that of many other species, we can nevertheless distinguish between roughly 10,000 different odors. These are made up of molecules called odorants, each of which activates a specific subset of odorant receptors in the nose. However, much of what we know about this process has come from studying the fruit fly, Drosophila, which detects odors using receptors located mainly on its antennae.

The number of potential odorants in nature is vast, and only a tiny fraction of the interactions between odorants and receptors can be physically tested. To address this challenge, Boyle et al. have used a computational approach to study in depth the interactions between a subset of 24 odorant receptors in Drosophila antennae and 109 odorants.

After developing a method to identify structural features shared by the odorants that activate each receptor, Boyle et al. used this information to perform a computational (in silico) screen of more than 240,000 different odorant-like volatile compounds. For each receptor, they compiled a list of the 500 odorants predicted to interact most strongly with it. They then tested their predictions for a subset of the receptors by performing experiments in living flies, and found that roughly 71% of predicted compounds did indeed activate or inhibit their receptors, compared to only 10% of a control sample.

In addition to providing new insights into the nature of the interactions between odorants and their receptors, the computational screen devised by Boyle et al. could aid the development of novel insect repellents, or compounds that mask the odors used by disease-causing insects to identify their hosts. It could also be used in the future to develop novel flavors and fragrances.

DOI:http://dx.doi.org/10.7554/eLife.01120.002

Promising Aedes aegypti repellent chemotypes identified through integrated QSAR, virtual screening, synthesis, and bioassay

Molecular field topology analysis, scaffold hopping, and molecular docking were used as complementary computational tools for the design of repellents for Aedes aegypti, the insect vector for yellow fever, chikungunya, and dengue fever. A large number of analogues were evaluated by virtual screening with Glide molecular docking software. This produced several dozen hits that were either synthesized or procured from commercial sources. Analysis of these compounds by a repellent bioassay resulted in a few highly active chemicals (in terms of minimum effective dosage) as viable candidates for further hit-to-lead and lead optimization effort.

Long-term memory and response generalization in mushroom body extrinsic neurons in the honeybee Apis mellifera

Honeybees learn to associate an odor with sucrose reward under conditions that allow the monitoring of neural activity by imaging Ca(2+) transients in morphologically identified neurons. Here we report such recordings from mushroom body extrinsic neurons - which belong to a recurrent tract connecting the output of the mushroom body with its input, potentially providing inhibitory feedback - and other extrinsic neurons. The neurons' responses to the learned odor and two novel control odors were measured 24 h after learning. We found that calcium responses to the learned odor and an odor that was strongly generalized with it were enhanced compared with responses to a weakly generalized control. Thus, the physiological responses measured in these extrinsic neurons accurately reflect what is observed in behavior. We conclude that the recorded recurrent neurons feed information back to the mushroom body about the features of learned odor stimuli. Other extrinsic neurons may signal information about learned odors to different brain regions.

A combined perceptual, physico-chemical, and imaging approach to 'odour-distances' suggests a categorizing function of the Drosophila antennal lobe

How do physico-chemical stimulus features, perception, and physiology relate? Given the multi-layered and parallel architecture of brains, the question specifically is where physiological activity patterns correspond to stimulus features and/or perception. Perceived distances between six odour pairs are defined behaviourally from four independent odour recognition tasks. We find that, in register with the physico-chemical distances of these odours, perceived distances for 3-octanol and n-amylacetate are consistently smallest in all four tasks, while the other five odour pairs are about equally distinct. Optical imaging in the antennal lobe, using a calcium sensor transgenically expressed in only first-order sensory or only second-order olfactory projection neurons, reveals that 3-octanol and n-amylacetate are distinctly represented in sensory neurons, but appear merged in projection neurons. These results may suggest that within-antennal lobe processing funnels sensory signals into behaviourally meaningful categories, in register with the physico-chemical relatedness of the odours.

Possible origin of modified EAG activity by point-fluorination of insect pheromones

Fluorine closely mimics the steric requirement of hydrogen at enzyme receptor sites, but its strong electronegativity significantly alters the reactivity of neighboring centers. Therefore, point-fluorination of biologically active molecules is an important technology with which to investigate the relationship between a biologically active compound with a receptor protein. We synthesized point-fluorinated pheromone analogues of eldanolide and measured their biological activity by electroantennography (EAG) to understand the importance of conformation in the specificity of ligand recognition by the olfactory receptor. By comparing EAG activities and conformational analysis of these molecules using density functional theory calculations, significant differences were found in the population of preferable conformers between EAG-active compounds and EAG-inactive compounds. Based on these results, we propose a working hypothesis for the possible origin of the diversity of relationship between enantiomer and activity in pheromone perception response. These results obtained in the investigation of the mechanism of chemical communication through pheromone molecules among insects should be useful to expand the horizons of medicinal chemists.

A behavioral odor similarity "space" in larval Drosophila

To provide a behavior-based estimate of odor similarity in larval Drosophila, we use 4 recognition-type experiments: 1) We train larvae to associate an odor with food and then test whether they would regard another odor as the same as the trained one. 2) We train larvae to associate an odor with food and test whether they prefer the trained odor against a novel nontrained one. 3) We train larvae differentially to associate one odor with food, but not the other one, and test whether they prefer the rewarded against the nonrewarded odor. 4) In an experiment like (3), we test the larvae after a 30-min break. This yields a combined task-independent estimate of perceived difference between odor pairs. Comparing these perceived differences to published measures of physicochemical difference reveals a weak correlation. A notable exception are 3-octanol and benzaldehyde, which are distinct in published accounts of chemical similarity and in terms of their published sensory representation but nevertheless are consistently regarded as the most similar of the 10 odor pairs employed. It thus appears as if at least some aspects of olfactory perception are “computed” in postreceptor circuits on the basis of sensory signals rather than being immediately given by them.

Integrating heterogeneous odor response data into a common response model: A DoOR to the complete olfactome

We have developed a new computational framework for merging odor response data sets from heterogeneous studies, creating a consensus metadatabase, the database of odor responses (DoOR). As a result, we obtained a functional atlas of all available odor responses in Drosophila melanogaster. Both the program and the data set are freely accessible and downloadable on the Internet (http://neuro.uni-konstanz.de/DoOR). The procedure can be adapted to other species, thus creating a family of “olfactomes” in the near future. Drosophila melanogaster was chosen because of all species this one is closest to having the complete olfactome characterized, with the highest number of deorphanized receptors available. The database guarantees long-term stability (by offering time-stamped, downloadable versions), up-to-date accuracy (by including new data sets as soon as they are published), and portability (for other species). We hope that this comprehensive repository of odor response profiles will be useful to the olfactory community and to computational neuroscientists alike.

Odor coding by a Mammalian receptor repertoire

Deciphering olfactory encoding requires a thorough description of the ligands that activate each odorant receptor (OR). In mammalian systems, however, ligands are known for fewer than 50 of more than 1400 human and mouse ORs, greatly limiting our understanding of olfactory coding. We performed high-throughput screening of 93 odorants against 464 ORs expressed in heterologous cells and identified agonists for 52 mouse and 10 human ORs. We used the resulting interaction profiles to develop a predictive model relating physicochemical odorant properties, OR sequences, and their interactions. Our results provide a basis for translating odorants into receptor neuron responses and for unraveling mammalian odor coding.

From nose to brain: understanding transport capacity and transport rate of drugs

The unique relationship between nasal cavity and cranial cavity tissues in anatomy and physiology makes intranasal delivery to the brain feasible. An intranasal delivery provides some drugs with short channels to bypass the blood-brain barrier (BBB), especially for those with fairly low brain concentrations after a routine delivery, thus greatly enhancing the therapeutic effect on brain diseases. In the past two decades, a good number of encouraging outcomes have been reported in the treatment of diseases of the brain or central nervous system (CNS) through nasal administration. In spite of the significant merit of bypassing the BBB, direct nose-to-brain delivery still bears the problems of low efficiency and volume for capacity due to the limited volume of the nasal cavity, the small area ratio of olfactory mucosa to nasal mucosa and the limitations of low dose and short retention time of drug absorption. It is crucial that selective distribution and retention time of drugs or preparations on olfactory mucosa should be enhanced so as to increase the direct delivery efficiency. In this article, we first briefly review the nose-to-brain transport pathways, before detailing the impacts on them, followed by a comprehensive summary of effective methods, including formulation modification, agglutinant-mediated transport and a brain-homing, peptide-mediated delivery based on phage display screening technique, with a view to providing a theoretic reference for elevating the therapeutic effects on brain diseases.

SuperScent--a database of flavors and scents

Volatiles are efficient mediators of chemical communication acting universally as attractant, repellent or warning signal in all kingdoms of life. Beside this broad impact volatiles have in nature, scents are also widely used in pharmaceutical, food and cosmetic industries, so the identification of new scents is of great industrial interest. Despite this importance as well as the vast number and diversity of volatile compounds, there is currently no comprehensive public database providing information on structure and chemical classification of volatiles. Therefore, the database SuperScent was established to supply users with detailed information on the variety of odor components. The version of the database presented here comprises the 2D/3D structures of approximately 2100 volatiles and around 9200 synonyms as well as physicochemical properties, commercial availability and references. The volatiles are classified according to their origin, functionality and odorant groups. The information was extracted from the literature and web resources. SuperScent offers several search options, e.g. name, Pubchem ID number, species, functional groups, or molecular weight. SuperScent is available online at: http://bioinformatics.charite.de/superscent.

Relationships between molecular structure and perceived odor quality of ligands for a human olfactory receptor

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.

Processing and classification of chemical data inspired by insect olfaction

The chemical sense of insects has evolved to encode and classify odorants. Thus, the neural circuits in their olfactory system are likely to implement an efficient method for coding, processing, and classifying chemical information. Here, we describe a computational method to process molecular representations and classify molecules. The three-step approach mimics neurocomputational principles observed in olfactory systems. In the first step, the original stimulus space is sampled by "virtual receptors," which are chemotopically arranged by a self-organizing map. In the second step, the signals from the virtual receptors are decorrelated via correlation-based lateral inhibition. Finally, in the third step, olfactory scent perception is modeled by a machine learning classifier. We found that signal decorrelation during the second stage significantly increases the accuracy of odorant classification. Moreover, our results suggest that the proposed signal transform is capable of dimensionality reduction and is more robust against overdetermined representations than principal component scores. Our olfaction-inspired method was successfully applied to predicting bioactivities of pharmaceutically active compounds with high accuracy. It represents a way to efficiently connect chemical structure with biological activity space.