SuperScent--a database of flavors and scents

Navigating the Fragrance Space Using Graph Generative Models and Predicting Odors

We explore a suite of generative modeling techniques to efficiently navigate and explore the complex landscapes of odor and the broader chemical space. Unlike traditional approaches, we not only generate molecules but also predict the odor likeliness with a ROC AUC score of 0.97 and assign probable odor labels. We correlate odor likeliness with physicochemical features of molecules using machine learning techniques and leverage SHAP (SHapley Additive exPlanations) to demonstrate the interpretability of the function. The whole process involves four key stages: molecule generation, stringent sanitization checks for molecular validity, fragrance likeliness screening, and odor prediction of the generated molecules. By making our code and trained models publicly accessible, we aim to facilitate the broader adoption of our research across applications in fragrance discovery and olfactory research.

sCentInDB: a database of essential oil chemical profiles of Indian medicinal plants

Essential oils are complex mixtures of volatile compounds produced by aromatic plants and widely used in personal care, food flavoring, and pharmaceutical industry due to their odor and therapeutic properties. As a high-value and low-volume organic product, optimizing plant yield and modifying composition by leveraging knowledge on chemical profiles of essential oils can lead to enhanced bioproducts. Additionally, overharvesting of wild medicinal plants, especially in India, threatens biodiversity. Essential oil profiles of such plants can help regulate their exploitation. Here, we present sCentInDB, a manually curated FAIR-compliant DataBase of Essential oil Chemical profiles of Medicinal plants of India, compiled from published literature. sCentInDB contains data on 554 Indian medicinal plants at the plant part level, encompassing 2170 essential oil profiles, 3420 chemicals, 471 plant-part-therapeutic use associations, 120 plant-part-odor associations, and 218 plant-part-color associations. sCentInDB also compiles metadata such as sample location, isolation, and analysis methods. Subsequently, an extensive analysis of the chemical space in sCentInDB was performed. By constructing a chemical similarity network, terpenoids were found to be distributed across the network, indicating greater structural diversity. Moreover, a comparison of the scaffold diversity of chemicals in sCentInDB was performed against three other aroma libraries using cyclic system retrieval curves. Altogether, sCentInDB will serve as a valuable resource for researchers working on plant volatiles and employing genetic engineering to enhance oil yield and composition. Further, sCentInDB will aid in the establishment of quality standards for essential oils and provide vital insights for therapeutic and perfumery applications. sCentInDB is accessible at https://cb.imsc.res.in/scentindb/ .

MORE-Q, a dataset for molecular olfactorial receptor engineering by quantum mechanics

We introduce the MORE-Q dataset, a quantum-mechanical (QM) dataset encompassing the structural and electronic data of non-covalent molecular sensors formed by combining 18 mucin-derived olfactorial receptors with 102 body odor volatilome (BOV) molecules. To have a better understanding of their intra- and inter-molecular interactions, we have performed accurate QM calculations in different stages of the sensor design and, accordingly, MORE-Q splits into three subsets: i) MORE-Q-G1: QM data of 18 receptors and 102 BOV molecules, ii) MORE-Q-G2: QM data of 23,838 BOV-receptor configurations, and iii) MORE-Q-G3: QM data of 1,836 BOV-receptor-graphene systems. Each subset involves geometries optimized using GFN2-xTB with D4 dispersion correction and up to 39 physicochemical properties, including global and local properties as well as binding features, all computed at the tightly converged PBE+D3 level of theory. By addressing BOV-receptor-graphene systems from a QM perspective, MORE-Q can serve as a benchmark dataset for state-of-the-art machine learning methods developed to predict binding features. This, in turn, can provide valuable insights for developing the next-generation mucin-derived olfactory receptor sensing devices.

An overview on olfaction in the biological, analytical, computational, and machine learning fields

Recently, the comprehension of odor perception has advanced, unveiling the mysteries of the molecular receptors within the nasal passages and the intricate mechanisms governing signal transmission between these receptors, the olfactory bulb, and the brain. This review provides a comprehensive panorama of odors, encompassing various topics ranging from the structural and molecular underpinnings of odorous substances to the physiological intricacies of olfactory perception. It extends to elucidate the analytical methods used for their identification and explores the frontiers of computational methodologies.

Exploring natural products potential: A similarity-based target prediction tool for natural products

Natural products are invaluable resources in drug discovery due to their substantial structural diversity. However, predicting their interactions with druggable protein targets remains a challenge, primarily due to the limited availability of bioactivity data. This study introduces CTAPred (Compound-Target Activity Prediction), an open-source command-line tool designed to predict potential protein targets for natural products. CTAPred employs a two-stage approach, combining fingerprinting and similarity-based search techniques to identify likely drug targets for these bioactive compounds. Despite its simplicity, the tool's performance is comparable to that of more complex methods, demonstrating proficiency in target retrieval for natural product compounds. Furthermore, this study explores the optimal number of reference compounds most similar to the query compound, aiming to refine target prediction accuracy. The findings demonstrated the superior performance of considering only the most similar reference compounds for target prediction. CTAPred is freely available at https://github.com/Alhasbary/CTAPred, offering a valuable resource for deciphering natural product-target associations and advancing drug discovery.

Effective detection of indoor fungal contamination through the identification of volatile organic compounds using mass spectrometry and machine learning

Indoor fungal contamination poses significant challenges to human health and indoor air quality. This study addresses an effective approach using mass spectrometry and machine learning to identify microbial volatile organic compounds (MVOCs) originated from indoor fungi. Three common indoor fungi, including Penicillium Chrysogenum, Cladosporium cladosporioides, and Aspergillus niger, were cultivated on various substrates, namely potato dextrose agar, wallpaper, and silicone. Solid-phase microextraction-gas chromatography-mass spectrometry (SPME-GC-MS) was used to analyze MVOCs together, along with the VOCs (namely, non-MVOCs) emitted by various indoor materials (wallpaper adhesives, diffusers, particle board, oriented strand board, medium-density fiberboard, bleach, print cartridges, and cosmetic creams). This study demonstrates the significant effectiveness of machine learning, particularly the random forest model, in accurately distinguishing MVOCs from non-MVOCs. Furthermore, specific VOCs such as benzocyclobutane, styrene, ethanol, benzene, and 2-butanone emerged as consistent indicators of fungal presence across different fungal species and substrates. A simplified random forest model incorporating these key VOCs achieved a high accuracy of 96.2%, highlighting their practical significance in fungal contamination detection. This integrated approach combining analytical chemistry and machine learning offers a promising strategy for the comprehensive and reliable assessment of indoor fungal contamination.

Identification of floral aroma components and molecular regulation mechanism of floral aroma formation in Phalaenopsis

BACKGROUND

Most Phalaenopsis cultivars have almost no aroma, with a few exceptions. Phalaenopsis presents significant challenges in fragrance breeding due to its weak aroma and low fertility. It is therefore necessary to identify the aroma components and key regulatory genes in Phalaenopsis cultivars like 'Orange Beauty', 'Brother Sara Gold', 'Purple Martin', 'H026', 'SK16', 'SX098', and 'SH51', to improve the aroma of the common Phalaenopsis.

RESULTS

Floral aroma components were tested on nine Phalaenopsis species, using smell identification and headspace gas chromatography-mass spectrometry. The result showed that alcohols, esters, and alkenes were the key specific components in the different species and cultivar aromas and the aroma intensity and component content of cultivars with different colors were different. The main components of the floral aromas in Phalaenopsis were alcohols (including eucalyptol, linalool, citronellol, and 1-hexanol), esters (including hexyl acetate, leaf acetate, and dibutyl phthalate), alkenes (including pinene and sabinene) and arenes (like fluorene). The transcriptome of flowers in the bud stage and bloom stage of P. 'SH51' was sequenced and 5999 differentially expressed genes were obtained. The contributions of the phenylpropionic acid/phenyl ring compound and the terpene compound to the aroma were greater. Sixteen genes related to phalaenopsis aroma were found. TC4M, PAL, CAD6, and HR were related to phenylpropanoid synthesis pathway. SLS, TS10, and P450 were related to the synthesis pathway of terpenes. TS10 and YUCCA 10 were involved in tryptophan metabolism.

CONCLUSION

This is the first report on the floral aroma components and regulatory genes in Phalaenopsis. The proposed method and research data can provide technical support for Phalaenopsis breeding. © 2024 Society of Chemical Industry.

Floral Volatile Organic Compounds of Mitchella repens (Rubiaceae)

Mitchella repens (partridgeberry; family Rubiaceae) is a creeping, understory plant native to eastern North America. The twinned, tubular flowers of this distylous plant are bright white and produce volatile organic compounds (VOCs). Partridgeberry has intermorph incompatibility and thus requires pollinators to move pollen from one morph to the other. Despite partridgeberry being a common member of forest communities, little is known about its pollination syndrome. Using headspace solid-phase microextraction (HS-SPME) coupled with gas chromatography-mass spectrometry (GC-MS) analysis the floral VOCs were identified, with the four predominant molecules being α-pinene, camphene, D-limonene, and verbenone. The VOC profile contained 27 molecules consisting mostly of monoterpenes. Two independent sample t-tests confirmed that each morph produced statistically similar floral VOC profiles (p > 0.1). Additionally, two of the predominant VOC molecules, α-pinene and D-limonene, were measured throughout the 5-day flowering cycle. Simple linear regressions of these compound levels versus days after flowering (DAF) confirmed that α-pinene and D-limonene both decreased with flower age. Insect visits were observed to correlate with α-pinene and D-limonene concentrations, peaking at 1-2 DAF and then declining through 5 DAF.

Pyrfume: A window to the world's olfactory data

Advances in theoretical understanding are frequently unlocked by access to large, diverse experimental datasets. Our understanding of olfactory neuroscience and psychophysics remain years behind the other senses, in part because rich datasets linking olfactory stimuli with their corresponding percepts, behaviors, and neural pathways remain scarce. Here we present a concerted effort to unlock and unify dozens of stimulus-linked olfactory datasets across species and modalities under a unified framework called Pyrfume. We present examples of how researchers might use Pyrfume to conduct novel analyses uncovering new principles, introduce trainees to the field, or construct benchmarks for machine olfaction.

FlavorDB2: An updated database of flavor molecules

Flavor is expressed through the interaction of molecules via gustatory and olfactory mechanisms. Knowing the pivotal role of flavor molecules in food and fragrances, the existence of a comprehensive repository becomes valuable. Such a repository encompasses the information of flavor molecules characterizing their flavor profile, chemical properties, regulatory status, consumption statistics, taste/aroma threshold values, reported uses in food categories, and synthesis. FlavorDB2 (https://cosylab.iiitd.edu.in/flavordb2/) is an updated database of flavor molecules with a user-friendly interface consisting of 25,595 flavor molecules. Among these, 2254 flavor molecules are associated with 936 natural ingredients from 34 categories. This repository simplifies the search for flavor molecules and their associated attributes. This platform offers multifaceted applications, including food pairing. FlavorDB2 serves as a standard repository of flavor compounds for researchers, chefs, and fragrance industry.

Chitosan and inulin synergized with Lactiplantibacillus plantarum LPP95 to improve the quality characteristics of low-salt pickled tuber mustard

Low-salt pickled vegetables are in line with a healthier diet, yet ensuring consistent quality of such products is challenging. In this study, low-salt tuber mustard pickles fermented with Lactiplantibacillus plantarum LPP95 in the presence of chitosan and inulin were analyzed over a 30-day period, and quality changes were evaluated. Total acid productions along with high bacterial counts (106 CFU/mL) were observed in the initial 20 days during indoor storage temperature, in which the reduced fiber aperture was found significantly lead to an increase in crispness (16.94 ± 1.87 N) and the maintenance of a low nitrate content (1.23 ± 0.01 mg/kg). Moreover, the combined pickling treatment resulted in higher malic acid content, lower tartaric acid content, and a decrease in the content of bitter amino acids (e.g., isoleucine and leucine), thus leading to an increase in the proportion of sweet amino acids. Additionally, combined pickling led to the production of unique volatile flavor compounds, especially the distinct spicy flavor compounds isothiocyanates. Moreover, the combined pickling treatment resulted in an increase in the abundance of Lactiplantibacillus and promoted microbial diversity within the fermentation system. Thus, the synergistic effect among chitosan, inulin, and L. plantarum LPP95 significantly enhanced the quality of pickles. The study offers a promising strategy to standardize the quality of low-salt fermented vegetables.

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/'.

Predicting Odor Sensory Attributes of Unidentified Chemicals in Water Using Fragmentation Mass Spectra with Machine Learning Models

Knowing odor sensory attributes of odorants lies at the core of odor tracking when addressing waterborne odor issues. However, experimental determination covering tens of thousands of odorants in authentic water is not pragmatic due to the complexity of odorant identification and odor evaluation. In this study, we propose the first machine learning (ML) model to predict odor perception/threshold aiming at odorants in water, which can use either molecular structure or MS2 spectra as input features. We demonstrate that model performance using MS2 spectra is nearly as good as that using unequivocal structures, both with outstanding accuracy. We particularly show the model's robustness in predicting odor sensory attributes of unidentified chemicals by using the experimentally obtained MS2 spectra from nontarget analysis on authentic water samples. Interpreting the developed models, we identify the intricate interaction of functional groups as the predominant influence factor on odor sensory attributes. We also highlight the important roles of carbon chain length, molecular weight, etc., in the inherent olfactory mechanisms. These findings streamline the odor sensory attribute prediction and are crucial advancements toward credible tracking and efficient control of off-odors in water.

A critical assessment of bioactive compounds databases

Compound databases (DBs) are essential tools for drug discovery. The number of DBs in public domain is increasing, so it is important to analyze these DBs. In this article, the main characteristics of 64 DBs will be presented. The methodological strategy used was a literature search. To analyze the characteristics obtained in the review, the DBs were categorized into two subsections: Open Access and Commercial DBs. Open access includes generalist DBs (containing compounds of diverse origins), DBs with specific applicability, DBs exclusive to natural products and those containing compounds with specific pharmacological action. The literature review showed that there are challenges to making these repositories available, such as standardizing information curation practices and funding to maintain and sustain them.

Standardizing Substrate Selection: A Strategy toward Unbiased Evaluation of Reaction Generality

With over 10,000 new reaction protocols arising every year, only a handful of these procedures transition from academia to application. A major reason for this gap stems from the lack of comprehensive knowledge about a reaction's scope, i.e., to which substrates the protocol can or cannot be applied. Even though chemists invest substantial effort to assess the scope of new protocols, the resulting scope tables involve significant biases, reducing their expressiveness. Herein we report a standardized substrate selection strategy designed to mitigate these biases and evaluate the applicability, as well as the limits, of any chemical reaction. Unsupervised learning is utilized to map the chemical space of industrially relevant molecules. Subsequently, potential substrate candidates are projected onto this universal map, enabling the selection of a structurally diverse set of substrates with optimal relevance and coverage. By testing our methodology on different chemical reactions, we were able to demonstrate its effectiveness in finding general reactivity trends by using a few highly representative examples. The developed methodology empowers chemists to showcase the unbiased applicability of novel methodologies, facilitating their practical applications. We hope that this work will trigger interdisciplinary discussions about biases in synthetic chemistry, leading to improved data quality.

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.

Transcriptome Analyses Reveal the Aroma Terpeniods Biosynthesis Pathways of Primula forbesii Franch. and the Functional Characterization of the PfDXS2 Gene

Primula forbesii Franch. is a unique biennial herb with a strong floral fragrance, making it an excellent material for studying the aroma characteristics of the genus Primula. The floral scent is an important ornamental trait that facilitates fertilization. However, the molecular mechanism regulating the floral scent in Primula is unknown. In order to better understand the biological mechanisms of floral scents in this species, this study used RNA sequencing analysis to discuss the first transcriptome sequence of four flowering stages of P. forbesii, which generated 12 P. forbesii cDNA libraries with 79.64 Gb of clean data that formed 51,849 unigenes. Moreover, 53.26% of the unigenes were annotated using public databases. P. forbesii contained 44 candidate genes covering all known enzymatic steps for the biosynthesis of volatile terpenes, the major contributor to the flower's scent. Finally, 1-deoxy-d-xylulose 5-phosphate synthase gene of P. forbesii (PfDXS2, MK370094), the first key enzyme gene in the 2-c-methyl-d-erythritol 4-phosphate (MEP) pathway of terpenoids, was cloned and functionally verified using virus-induced gene silencing (VIGs). The results showed that PfDXS2-silencing significantly reduced the relative concentrations of main volatile terpenes. This report is the first to present molecular data related to aroma metabolites biosynthesis pathways and the functional characterization of any P. forbesii gene. The data on RNA sequencing provide comprehensive information for further analysis of other plants of the genus Primula.

Encapsulation of Fragrances in Micro- and Nano-Capsules, Polymeric Micelles, and Polymersomes

Fragrances are ubiquitously and extensively used in everyday life and several industrial applications, including perfumes, textiles, laundry formulations, hygiene household products, and food products. However, the intrinsic volatility of these small organic molecules leaves them particularly susceptible to fast depletion from a product or from the surface they have been applied to. Encapsulation is a very effective method to limit the loss of fragrance during their use and to sustain their release. This review gives an overview of the different materials and techniques used for the encapsulation of fragrances, scents, and aromas, as well as the methods used to characterize the resulting encapsulation systems, with a particular focus on cyclodextrins, polymer microcapsules, inorganic microcapsules, block copolymer micelles, and polymersomes for fragrance encapsulation, sustained release, and controlled release.

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.

Volatile secondary metabolome and transcriptome analysis reveals distinct regulation mechanism of aroma biosynthesis in Syringa oblata and S. vulgaris

Lilacs have high ornamental value due to their strong aroma. However, the molecular regulatory mechanisms of aroma biosynthesis and metabolism in lilac were largely unclear. In this study, two varieties with distinct aroma, Syringa oblata 'Zi Kui' (faint aroma) and Syringa vulgaris 'Li Fei' (strong aroma), were used for exploring the regulation mechanism of aroma difference. Via GC-MS analysis, a total of 43 volatile components were identified. Terpene volatiles was the most abundant volatiles constituting the aroma of two varieties. Notably, 3 volatile secondary metabolites were unique in 'Zi Kui' and 30 volatile secondary metabolites were unique in 'Li Fei'. Then, a transcriptome analysis was performed to clarify the regulation mechanism of aroma metabolism difference between these two varieties, and identified 6411 differentially expressed genes (DEGs). Interestingly, ubiquinone and other terpenoid-quinone biosynthesis genes were significantly enriched in DEGs. We further conducted a correlation analysis between the volatile metabolome and transcriptome and found that TPS, GGPPS, and HMGS genes might be the key contributors to the differences in floral fragrance composition between the two lilac varieties. Our study improves the understanding in the regulation mechanism of Lilac aroma and would help improve the aroma of ornamental crops by metabolic engineering.

Volatile metabolome and floral transcriptome analyses reveal the volatile components of strongly fragrant progeny of Malus × robusta

Floral fragrance is an important trait that contributes to the ornamental properties and pollination of crabapple. However, research on the physiological and molecular biology of the floral volatile compounds of crabapple is rarely reported. In this study, metabolomic and transcriptomic analyses of the floral volatile compounds of standard Malus robusta flowers (Mr), and progeny with strongly and weakly fragrant flowers (SF and WF, respectively), were conducted. Fifty-six floral volatile compounds were detected in the plant materials, mainly comprising phenylpropane/benzene ring-type compounds, fatty acid derivatives, and terpene compounds. The volatile contents were significantly increased before the early flowering stage (ES), and the contents of SF flowers were twice those of WF and Mr flowers. Odor activity values were determined for known fragrant volatiles and 10-11 key fragrant volatiles were identified at the ES. The predominant fragrant volatiles were methyl benzoate, linalool, leaf acetate, and methyl anthranilate. In the petals, stamens, pistil, and calyx of SF flowers, 26 volatiles were detected at the ES, among which phenylpropane/benzene ring-type compounds were the main components accounting for more than 75% of the total volatile content. Functional analysis of transcriptome data revealed that the phenylpropanoid biosynthesis pathway was significantly enriched in SF flowers. By conducting combined analyses between volatiles and differentially expressed genes, transcripts of six floral scent-related genes were identified and were associated with the contents of the key fragrant volatiles, and other 23 genes were potentially correlated with the key volatile compounds. The results reveal possible mechanisms for the emission of strong fragrance by SF flowers, and provide a foundation for improvement of the floral fragrance and development of new crabapple cultivars.

Efficient processing of natural scenes in visual cortex

Neural circuits in the periphery of the visual, auditory, and olfactory systems are believed to use limited resources efficiently to represent sensory information by adapting to the statistical structure of the natural environment. This "efficient coding" principle has been used to explain many aspects of early visual circuits including the distribution of photoreceptors, the mosaic geometry and center-surround structure of retinal receptive fields, the excess OFF pathways relative to ON pathways, saccade statistics, and the structure of simple cell receptive fields in V1. We know less about the extent to which such adaptations may occur in deeper areas of cortex beyond V1. We thus review recent developments showing that the perception of visual textures, which depends on processing in V2 and beyond in mammals, is adapted in rats and humans to the multi-point statistics of luminance in natural scenes. These results suggest that central circuits in the visual brain are adapted for seeing key aspects of natural scenes. We conclude by discussing how adaptation to natural temporal statistics may aid in learning and representing visual objects, and propose two challenges for the future: (1) explaining the distribution of shape sensitivity in the ventral visual stream from the statistics of object shape in natural images, and (2) explaining cell types of the vertebrate retina in terms of feature detectors that are adapted to the spatio-temporal structures of natural stimuli. We also discuss how new methods based on machine learning may complement the normative, principles-based approach to theoretical neuroscience.

An analytical pipeline to support robust research on the ecology, evolution, and function of floral volatiles

Research on floral volatiles has grown substantially in the last 20 years, which has generated insights into their diversity and prevalence. These studies have paved the way for new research that explores the evolutionary origins and ecological consequences of different types of variation in floral scent, including community-level, functional, and environmentally induced variation. However, to address these types of questions, novel approaches are needed that can handle large sample sizes, provide quality control measures, and make volatile research more transparent and accessible, particularly for scientists without prior experience in this field. Drawing upon a literature review and our own experiences, we present a set of best practices for next-generation research in floral scent. We outline methods for data collection (experimental designs, methods for conducting field collections, analytical chemistry, compound identification) and data analysis (statistical analysis, database integration) that will facilitate the generation and interpretation of quality data. For the intermediate step of data processing, we created the R package bouquet, which provides a data analysis pipeline. The package contains functions that enable users to convert chromatographic peak integrations to a filtered data table that can be used in subsequent statistical analyses. This package includes default settings for filtering out non-floral compounds, including background contamination, based on our best-practice guidelines, but functions and workflows can be easily customized as necessary. Next-generation research into the ecology and evolution of floral scent has the potential to generate broadly relevant insights into how complex traits evolve, their genomic architecture, and their consequences for ecological interactions. In order to fulfill this potential, the methodology of floral scent studies needs to become more transparent and reproducible. By outlining best practices throughout the lifecycle of a project, from experimental design to statistical analysis, and providing an R package that standardizes the data processing pipeline, we provide a resource for new and seasoned researchers in this field and in adjacent fields, where high-throughput and multi-dimensional datasets are common.

Terpenoids and their gene regulatory networks in Opisthopappus taihangensis 'Taihang Mingzhu' as detected by transcriptome and metabolome analyses

'Taihang Mingzhu' is the hybrid offspring of Opisthopappus taihangensis, and it has an excellent characteristic of whole-plant fragrance. At present, the genes and metabolites involved in the synthesis of its aromatic compounds are unknown because of the paucity of molecular biology studies on flowering in the Opisthopappus Shih genus. To elucidate the biosynthetic pathways of terpenoids, the main aromatic compounds in 'Taihang Mingzhu', we conducted transcriptome and metabolite analyses on its leaves and bud, inflorescences at the color-development, flowering, and full-bloom stages. A total of 82,685 unigenes were obtained, of which 43,901 were annotated on the basis of information at the protein databases Nr, SwissProt, KEGG, and COG/KOG (e-value<0.00001). Using gas headspace solid-phase microextraction chromatography - mass spectrometry (HS-SPME-GC/MS), 1350 metabolites were identified, the most abundant of which were terpenoids (302 metabolites). Analyses of the gene regulatory network of terpenoids in 'Taihang Mingzhu' identified 52 genes potentially involved in the regulation of terpenoid synthesis. The correlations between genes related to terpenoid metabolism/regulation and metabolite abundance were analyzed. We also extracted the essential oil from the leaves of 'Taihang Mingzhu' by hydrodistillation, and obtained 270 aromatic compounds. Again, the most abundant class was terpenoids. These results provide guidance for the extraction of essential oil from 'Taihang Mingzhu' leaves and flowers. In addition, our analyses provide valuable genetic resources to identify genetic targets to manipulate the aromatic profiles of this plant and other members the Opisthopappus Shih genus by molecular breeding.

Disorder and the Neural Representation of Complex Odors

Animals smelling in the real world use a small number of receptors to sense a vast number of natural molecular mixtures, and proceed to learn arbitrary associations between odors and valences. Here, we propose how the architecture of olfactory circuits leverages disorder, diffuse sensing and redundancy in representation to meet these immense complementary challenges. First, the diffuse and disordered binding of receptors to many molecules compresses a vast but sparsely-structured odor space into a small receptor space, yielding an odor code that preserves similarity in a precise sense. Introducing any order/structure in the sensing degrades similarity preservation. Next, lateral interactions further reduce the correlation present in the low-dimensional receptor code. Finally, expansive disordered projections from the periphery to the central brain reconfigure the densely packed information into a high-dimensional representation, which contains multiple redundant subsets from which downstream neurons can learn flexible associations and valences. Moreover, introducing any order in the expansive projections degrades the ability to recall the learned associations in the presence of noise. We test our theory empirically using data from Drosophila. Our theory suggests that the neural processing of sparse but high-dimensional olfactory information differs from the other senses in its fundamental use of disorder.

OptNCMiner: a deep learning approach for the discovery of natural compounds modulating disease-specific multi-targets

Background

Due to their diverse bioactivity, natural product (NP)s have been developed as commercial products in the pharmaceutical, food and cosmetic sectors as natural compound (NC)s and in the form of extracts. Following administration, NCs typically interact with multiple target proteins to elicit their effects. Various machine learning models have been developed to predict multi-target modulating NCs with desired physiological effects. However, due to deficiencies with existing chemical-protein interaction datasets, which are mostly single-labeled and limited, the existing models struggle to predict new chemical-protein interactions. New techniques are needed to overcome these limitations.

Results

We propose a novel NC discovery model called OptNCMiner that offers various advantages. The model is trained via end-to-end learning with a feature extraction step implemented, and it predicts multi-target modulating NCs through multi-label learning. In addition, it offers a few-shot learning approach to predict NC-protein interactions using a small training dataset. OptNCMiner achieved better prediction performance in terms of recall than conventional classification models. It was tested for the prediction of NC-protein interactions using small datasets and for a use case scenario to identify multi-target modulating NCs for type 2 diabetes mellitus complications.

Conclusions

OptNCMiner identifies NCs that modulate multiple target proteins, which facilitates the discovery and the understanding of biological activity of novel NCs with desirable health benefits.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-022-04752-5.

Novel Antiviral Activity of Ethyl 3-Hydroxyhexanoate Against Coxsackievirus B Infection

Coxsackievirus group B (CVB) is a member of the genus Enterovirus in the family Picornaviridae. CVB infection has been implicated as a major etiologic agent of viral myocarditis, dilated cardiomyopathy, meningitis, and pancreatitis among children and young adults. Until date, no antiviral agent has been licensed for the treatment of Coxsackievirus infection. In an effort to identify antiviral agents against diseases caused by the CVB, we found that ethyl 3-hydroxyhexanoate (EHX), a volatile compound present in fruits and food additives, is a potent antiviral compound. In this study, we demonstrated that EHX treatment significantly inhibits CVB replication both in vivo and in vitro. Furthermore, EHX possesses antiviral activity at 50% effective concentration (EC₅₀) of 1.2 μM and 50% cytotoxicity (CC₅₀) of 25.6 μM, yielding a selective index (SI) value as high as 20.8. Insights into the mechanism of antiviral activity of EHX showed that it acts at the step of viral RNA replication. Since EHX has received approval as food additives, treatment of CVB-related infections with EHX might be a safe therapeutic option and may be a promising strategy for the development of semi-synthetic antiviral drugs for viral diseases.

Transcriptomic profiling of the floral fragrance biosynthesis pathway of Liriodendron and functional characterization of the LtuDXR gene

Floral fragrance, which has the function of attracting pollinators, is a class of volatile secondary metabolites mainly released by the secretory tissue of petals. Terpenoids are key components of floral volatile substances. Previous studies have shown that there are significant differences in the concentration and composition of volatile floral fragrances, especially terpenoids, between Liriodendron chinense and L. tulipifera. At present, the mechanism by which the synthesis of floral fragrance is regulated in Liriodendron remains unexplored. In this study, we analyzed the differentially expressed genes (DEGs) of L. chinense and L. tulipifera, and identified 130 DEGs related to terpenoid synthesis. A KEGG enrichment analysis of DEGs related to terpenoid biosynthesis revealed that the monoterpenoid biosynthesis pathway was the most significant. We cloned the LtuDXR gene from L. tulipifera using RACE technology. RT-qPCR results showed that the expression of the LtuDXR gene was the highest in the early florescence petals, indicating that the LtuDXR gene may play a role in the synthesis of volatile terpenoids. Subcellular localization showed that the LtuDXR protein is mainly localized in the chloroplast. Overexpression of LtuDXR in Arabidopsis thaliana significantly increased the plant height, DXR enzyme activity, and carotenoid content. In this study, we identified and functionally characterized LtuDXR, which is involved in terpenoid synthesis in Liriodendron. Our work lays the foundation for further exploration of the molecular mechanism by which terpenoid biosynthesis is regulated in Liriodendron.

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

Olfaction is a multi-stage process that initiates with the odorants entering the nose and terminates with the brain recognizing the odor associated with the odorant. In a very intricate way, the process incorporates various components functioning together and in synchronization. OlfactionBase is a free, open-access web server that aims to bring together knowledge about many aspects of the olfaction mechanism in one place. OlfactionBase contains detailed information of components like odors, odorants, and odorless compounds with physicochemical and ADMET properties, olfactory receptors (ORs), odorant- and pheromone binding proteins, OR-odorant interactions in Human and Mus musculus. The dynamic, user-friendly interface of the resource facilitates exploration of different entities: finding chemical compounds having desired odor, finding odorants associated with OR, associating chemical features with odor and OR, finding sequence information of ORs and related proteins. Finally, the data in OlfactionBase on odors, odorants, olfactory receptors, human and mouse OR-odorant pairs, and other associated proteins could aid in the inference and improved understanding of odor perception, which might provide new insights into the mechanism underlying olfaction. The OlfactionBase is available at https://bioserver.iiita.ac.in/olfactionbase/.

What the odor is not: Estimation by elimination

Olfactory systems use a small number of broadly sensitive receptors to combinatorially encode a vast number of odors. We propose a method of decoding such distributed representations by exploiting a statistical fact: Receptors that do not respond to an odor carry more information than receptors that do because they signal the absence of all odorants that bind to them. Thus, it is easier to identify what the odor is not rather than what the odor is. For realistic numbers of receptors, response functions, and odor complexity, this method of elimination turns an underconstrained decoding problem into a solvable one, allowing accurate determination of odorants in a mixture and their concentrations. We construct a neural network realization of our algorithm based on the structure of the olfactory pathway.

Fungal Volatile Organic Compounds: More Than Just a Funky Smell?

Many volatile organic compounds (VOCs) associated with industry cause adverse health effects, but less is known about the physiological effects of biologically produced volatiles. This review focuses on the VOCs emitted by fungi, which often have characteristic moldy or "mushroomy" odors. One of the most common fungal VOCs, 1-octen-3-ol, is a semiochemical for many arthropod species and also serves as a developmental hormone for several fungal groups. Other fungal VOCs are flavor components of foods and spirits or are assayed in indirect methods for detecting the presence of mold in stored agricultural produce and water-damaged buildings. Fungal VOCs function as antibiotics as well as defense and plant-growth-promoting agents and have been implicated in a controversial medical condition known as sick building syndrome. In this review, we draw attention to the ubiquity, diversity, and toxicological significance of fungal VOCs as well as some of their ecological roles.

Volatile organic compounds: from figurants to leading actors in fungal symbiosis

Symbiosis involving two (or more) prokaryotic and/or eukaryotic partners is extremely widespread in nature, and it has performed, and is still performing, a key role in the evolution of several biological systems. The interaction between symbiotic partners is based on the emission and perception of a plethora of molecules, including volatile organic compounds (VOCs), synthesized by both prokaryotic and eukaryotic (micro)organisms. VOCs acquire increasing importance since they spread above and below ground and act as infochemicals regulating a very complex network. In this work we review what is known about the VOCs synthesized by fungi prior to and during the interaction(s) with their partners (either prokaryotic or eukaryotic) and their possible role(s) in establishing and maintaining the symbiosis. Lastly, we also describe the potential applications of fungal VOCs from different biotechnological perspectives, including medicinal, pharmaceutical and agronomical.

Optimization of SPME-GC-MS and characterization of floral scents from Aquilegia japonica and A. amurensis flowers

Background

The floral scents of plants play a key role in plant reproduction through the communication between plants and pollinators. Aquilegia as a model species for studying evolution, however, there have been few studies on the floral scents and relationships between floral scents and pollination for Aquilegia taxa.

Methods

In this study, three types of solid-phase micro-extraction (SPME) fiber coatings (DVB/PDMS, CAR/PDMS, DVB/CAR/PDMS) were evaluated for their performance in extracting volatile organic compounds (VOCs) from flowers of Aquilegia amurensis, which can contribute to the future studies of elucidating the role of floral scents in the pollination process.

Results

In total, 55 VOCs were identified, and among them, 50, 47 and 45 VOCs were extracted by the DVB/CAR/PDMS fiber, CAR/PDMS fiber and DVB/PDMS fibers, respectively. Only 30 VOCs were detected in A. japonica taxa. Furthermore, the relative contents of 8 VOCs were significant different (VIP > 1 and p < 0.05) between the A. amurensis and A. japonica.

Conclusions

The results can be applied in new studies of the relationships between the chemical composition of floral scents and the processes of attraction of pollinator. It may provide new ideas for rapid evolution and frequent interspecific hybridization of Aquilegia.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13065-021-00754-1.

Optimal prediction with resource constraints using the information bottleneck

Responding to stimuli requires that organisms encode information about the external world. Not all parts of the input are important for behavior, and resource limitations demand that signals be compressed. Prediction of the future input is widely beneficial in many biological systems. We compute the trade-offs between representing the past faithfully and predicting the future using the information bottleneck approach, for input dynamics with different levels of complexity. For motion prediction, we show that, depending on the parameters in the input dynamics, velocity or position information is more useful for accurate prediction. We show which motion representations are easiest to re-use for accurate prediction in other motion contexts, and identify and quantify those with the highest transferability. For non-Markovian dynamics, we explore the role of long-term memory in shaping the internal representation. Lastly, we show that prediction in evolutionary population dynamics is linked to clustering allele frequencies into non-overlapping memories.

Genes and genome editing tools for breeding desirable phenotypes in ornamentals

We review the main genes underlying commercial traits in cut flower species and critically discuss the possibility to apply genome editing approaches to produce novel variation and phenotypes. Promoting flowering and flower longevity as well as creating novelty in flower structure, colour range and fragrances are major objectives of ornamental plant breeding. The novel genome editing techniques add new possibilities to study gene function and breed new varieties. The implementation of such techniques, however, relies on detailed information about structure and function of genomes and genes. Moreover, improved protocols for efficient delivery of editing reagents are required. Recent results of the application of genome editing techniques to elite ornamental crops are discussed in this review. Enabling technologies and genomic resources are reviewed in relation to the implementation of such approaches. Availability of the main gene sequences, underlying commercial traits and in vitro transformation protocols are provided for the world's best-selling cut flowers, namely rose, lily, chrysanthemum, lisianthus, tulip, gerbera, freesia, alstroemeria, carnation and hydrangea. Results obtained so far are described and their implications for the improvement of flowering, flower architecture, colour, scent and shelf-life are discussed.

Olfactory Cues in Infant Feeds: Volatile Profiles of Different Milks Fed to Preterm Infants

Background: Smell is determined by odor-active volatile compounds that bind to specific olfactory receptors, allowing us to discriminate different smells. Olfactory stimulation may assist with digestion and metabolism of feeds in the neonate by activation of the cephalic phase response of digestion. Infants' physiological responses to the smell of different milks suggest they can distinguish between breastmilk and infant formula. We aimed to describe the profile of volatile compounds in preterm breastmilk and investigate how this differed from that of other preterm infant feeding options including pasteurized donor breastmilk, breastmilk with bovine milk-based fortifier, human milk-based products and various infant formulas. Methods: Forty-seven milk samples (13 different infant formulas and 34 human milk-based samples) were analyzed. Volatile compounds were extracted using Solid Phase Micro Extraction. Identification and relative quantification were carried out by Gas Chromatography with Mass Spectrometry. Principal Component Analysis (PCA) and one-way Analysis of Variance (ANOVA) with Tukey's HSD (parametric data) or Conover's post-hoc test (non-parametric data) were used as appropriate to explore differences in volatile profiles among milk types. Results: In total, 122 compounds were identified. Breastmilk containing bovine milk-based fortifier presented the highest number of compounds (109) and liquid formula the lowest (70). The profile of volatile compounds varied with 51 compounds significantly different (adjusted p < 0.001) among milk types. PCA explained 47% of variability. Compared to preterm breastmilk, the profile of volatile compounds in breastmilk with added bovine milk-based fortifier was marked by presence of fatty acids and their esters, ketones and aldehydes; infant formulas were characterized by alkyls, aldehydes and furans, and human milk-based products presented high concentrations of aromatic hydrocarbons, terpenoids and specific fatty acids. Conclusions: Sensory-active products of fatty acid oxidation are the major contributors to olfactory cues in infant feeds. Analysis of volatile compounds might be useful for monitoring quality of milk and detection of oxidation products and environmental contaminants. Further research is needed to determine whether these different volatile compounds have biological or physiological effects in nutrition of preterm infants.

SMILES to Smell: Decoding the Structure-Odor Relationship of Chemical Compounds Using the Deep Neural Network Approach

Finding the relationship between the structure of an odorant molecule and its associated smell has always been an extremely challenging task. The major limitation in establishing the structure-odor relation is the vague and ambiguous nature of the descriptor-labeling, especially when the sources of odorant molecules are different. With the advent of deep networks, data-driven approaches have been substantiated to achieve more accurate linkages between the chemical structure and its smell. In this study, the deep neural network (DNN) with physiochemical properties and molecular fingerprints (PPMF) and the convolution neural network (CNN) with chemical-structure images (IMG) are developed to predict the smells of chemicals using their SMILES notations. A data set of 5185 chemical compounds with 104 smell percepts was used to develop the multilabel prediction models. The accuracies of smell prediction from DNN + PPMF and CNN + IMG (Xception based) were found to be 97.3 and 98.3%, respectively, when applied on an independent test set of chemicals. The deep learning architecture combining both DNN + PPMF and CNN + IMG prediction models is proposed, which classifies smells and may help understand the generic mechanism underlying the relationship between chemical structure and smell perception.

FlavorGraph: a large-scale food-chemical graph for generating food representations and recommending food pairings

Food pairing has not yet been fully pioneered, despite our everyday experience with food and the large amount of food data available on the web. The complementary food pairings discovered thus far created by the intuition of talented chefs, not by scientific knowledge or statistical learning. We introduce FlavorGraph which is a large-scale food graph by relations extracted from million food recipes and information of 1,561 flavor molecules from food databases. We analyze the chemical and statistical relations of FlavorGraph and apply our graph embedding method to better represent foods in dense vectors. Our graph embedding method is a modification of metapath2vec with an additional chemical property learning layer and quantitatively outperforms other baseline methods in food clustering. Food pairing suggestions made based on the food representations of FlavorGraph help achieve better results than previous works, and the suggestions can also be used to predict relations between compounds and foods. Our research offers a new perspective on not only food pairing techniques but also food science in general.

RecipeDB: a resource for exploring recipes

Cooking is the act of turning nature into the culture, which has enabled the advent of the omnivorous human diet. The cultural wisdom of processing raw ingredients into delicious dishes is embodied in their cuisines. Recipes thus are the cultural capsules that encode elaborate cooking protocols for evoking sensory satiation as well as providing nourishment. As we stand on the verge of an epidemic of diet-linked disorders, it is eminently important to investigate the culinary correlates of recipes to probe their association with sensory responses as well as consequences for nutrition and health. RecipeDB (https://cosylab.iiitd.edu.in/recipedb) is a structured compilation of recipes, ingredients and nutrition profiles interlinked with flavor profiles and health associations. The repertoire comprises of meticulous integration of 118 171 recipes from cuisines across the globe (6 continents, 26 geocultural regions and 74 countries), cooked using 268 processes (heat, cook, boil, simmer, bake, etc.), by blending over 20 262 diverse ingredients, which are further linked to their flavor molecules (FlavorDB), nutritional profiles (US Department of Agriculture) and empirical records of disease associations obtained from MEDLINE (DietRx). This resource is aimed at facilitating scientific explorations of the culinary space (recipe, ingredient, cooking processes/techniques, dietary styles, etc.) linked to taste (flavor profile) and health (nutrition and disease associations) attributes seeking for divergent applications. Database URL:  https://cosylab.iiitd.edu.in/recipedb.

Identification of the Volatile Compounds and Observation of the Glandular Trichomes in Opisthopappus taihangensis and Four Species of Chrysanthemum

Opisthopappus taihangensis (Ling) Shih, a wild relative germplasm of chrysanthemum, releases a completely different fragrance from chrysanthemum species. We aimed to identify the volatile compounds of the leaves of O. taihangensis and four other Chrysanthemum species using headspace solid-phase micro-extraction combined with gas chromatography-mass spectrometry (HS-SPME-GC/MS). In total, 70 compounds were detected, and terpenoids accounted for the largest percentage in these five species. Many specific compounds were only emitted from O. taihangensis and not from the other four species. In particular, 1,8-cineole could be responsible for the special leaf fragrance of O. taihangensis as it accounted for the largest proportion of the compounds in O. taihangensis but a small or no proportion at all in other species. The glandular trichomes (GTs) in the leaves are the main organs responsible for the emission of volatiles. To explore the relationship between the emissions and the density of the GTs on the leaf epidermis, the shape and density of the GTs were observed and calculated, respectively. The results showed that the trichomes have two shapes in these leaves: T-shaped non-glandular trichomes and capitate trichomes. Histochemical staining analyses indicated that terpenoids are mainly emitted from capitate glandular trichomes. Correlation analysis showed that the volatile amount of terpenoids is highly related to the density of capitate trichomes. In O. taihangensis, the terpenoids content and density of capitate trichomes are the highest. We identified the diversity of leaf volatiles from O. taihangensis and four other Chrysanthemum species and found a possible relationship between the content of volatile compounds and the density of capitate trichomes, which explained the cause of the fragrance of O. taihangensis leaves.

Cellulose as a Delivery System of Raspberry Juice Volatiles and Their Stability

Formulation of delivery systems for active ingredients is of increasing importance for the food industry. For that purpose, we selected cellulose as a carrier polymer of raspberry volatiles. Freeze-dried cellulose/raspberry complexes were prepared by complexation of raspberry juice (constant amount) and cellulose (2.5%, 5%, 7.5% and 10%). In our study, cellulose was shown as a good carrier of raspberry juice volatiles. Thirty-nine volatiles were detected in raspberry juice while 11 of them were lost during preparation of the complexes. Berry flavor note was the dominant one in raspberry juice (40% of overall flavor), followed by citrus and woody notes (each around 18% of overall flavor) and floral, fruity, and green (each around 8% of overall flavor). Cellulose/raspberry complexes had different flavor profiles, but a berry flavor note was still the dominant one in all complexes. These results suggest an efficient plant-based approach to produce value-added cellulose/volatile dry complexes with possible utility as food flavoring ingredients.

An enumeration of natural products from microbial, marine and terrestrial sources

The discovery of a new drug is a multidisciplinary and very costly task. One of the major steps is the identification of a lead compound, i.e. a compound with a certain degree of potency and that can be chemically modified to improve its activity, metabolic properties, and pharmacokinetics profiles. Terrestrial sources (plants and fungi), microbes and marine organisms are abundant resources for the discovery of new structurally diverse and biologically active compounds. In this chapter, an attempt has been made to quantify the numbers of known published chemical structures (available in chemical databases) from natural sources. Emphasis has been laid on the number of unique compounds, the most abundant compound classes and the distribution of compounds in terrestrial and marine habitats. It was observed, from the recent investigations, that ~500,000 known natural products (NPs) exist in the literature. About 70 % of all NPs come from plants, terpenoids being the most represented compound class (except in bacteria, where amino acids, peptides, and polyketides are the most abundant compound classes). About 2,000 NPs have been co-crystallized in PDB structures.

Review on natural products databases: where to find data in 2020

Natural products (NPs) have been the centre of attention of the scientific community in the last decencies and the interest around them continues to grow incessantly. As a consequence, in the last 20 years, there was a rapid multiplication of various databases and collections as generalistic or thematic resources for NP information. In this review, we establish a complete overview of these resources, and the numbers are overwhelming: over 120 different NP databases and collections were published and re-used since 2000. 98 of them are still somehow accessible and only 50 are open access. The latter include not only databases but also big collections of NPs published as supplementary material in scientific publications and collections that were backed up in the ZINC database for commercially-available compounds. Some databases, even published relatively recently are already not accessible anymore, which leads to a dramatic loss of data on NPs. The data sources are presented in this manuscript, together with the comparison of the content of open ones. With this review, we also compiled the open-access natural compounds in one single dataset a COlleCtion of Open NatUral producTs (COCONUT), which is available on Zenodo and contains structures and sparse annotations for over 400,000 non-redundant NPs, which makes it the biggest open collection of NPs available to this date.

FlavorDB: a database of flavor molecules

Flavor is an expression of olfactory and gustatory sensations experienced through a multitude of chemical processes triggered by molecules. Beyond their key role in defining taste and smell, flavor molecules also regulate metabolic processes with consequences to health. Such molecules present in natural sources have been an integral part of human history with limited success in attempts to create synthetic alternatives. Given their utility in various spheres of life such as food and fragrances, it is valuable to have a repository of flavor molecules, their natural sources, physicochemical properties, and sensory responses. FlavorDB (http://cosylab.iiitd.edu.in/flavordb) comprises of 25,595 flavor molecules representing an array of tastes and odors. Among these 2254 molecules are associated with 936 natural ingredients belonging to 34 categories. The dynamic, user-friendly interface of the resource facilitates exploration of flavor molecules for divergent applications: finding molecules matching a desired flavor or structure; exploring molecules of an ingredient; discovering novel food pairings; finding the molecular essence of food ingredients; associating chemical features with a flavor and more. Data-driven studies based on FlavorDB can pave the way for an improved understanding of flavor mechanisms.

Primacy coding facilitates effective odor discrimination when receptor sensitivities are tuned

The olfactory system faces the difficult task of identifying an enormous variety of odors independent of their intensity. Primacy coding, where the odor identity is encoded by the receptor types that respond earliest, might provide a compact and informative representation that can be interpreted efficiently by the brain. In this paper, we analyze the information transmitted by a simple model of primacy coding using numerical simulations and statistical descriptions. We show that the encoded information depends strongly on the number of receptor types included in the primacy representation, but only weakly on the size of the receptor repertoire. The representation is independent of the odor intensity and the transmitted information is useful to perform typical olfactory tasks with close to experimentally measured performance. Interestingly, we find situations in which a smaller receptor repertoire is advantageous for discriminating odors. The model also suggests that overly sensitive receptor types could dominate the entire response and make the whole array useless, which allows us to predict how receptor arrays need to adapt to stay useful during environmental changes. Taken together, we show that primacy coding is more useful than simple binary and normalized coding, essentially because the sparsity of the odor representations is independent of the odor statistics, in contrast to the alternatives. Primacy coding thus provides an efficient odor representation that is independent of the odor intensity and might thus help to identify odors in the olfactory cortex.

Humans can identify odors independent of their intensity. Experimental data suggest that this is accomplished by representing the odor identity by the earliest responding receptor types. Using theoretical modeling, we here show that such a primacy code outperforms alternative encodings and allows discriminating odors with close to experimentally measured performance. This performance depends strongly on the number of receptors considered in the primacy code, but the receptor repertoire size is less important. The model also suggests a strong evolutionary pressure on the receptor sensitivities, which could explain observed receptor copy number adaptations. By predicting psycho-physical experiments, the model will thus contribute to our understanding of the olfactory system.

Dynamics of volatile compounds in TSH 565 cocoa clone fermentation and their role on chocolate flavor in Southeast Brazil

The effects of indigenous fermentation on volatile compound profiles in a Theobroma cacao L, TSH565 clone, resistant to Moniliophtora perniciosa and Phytophthora spp. were evaluated in Southern Brazil. Sixty-three volatile flavor compounds in pulp and 36 in grains were identified by SPME-HS/GC-MS and classified as terpenes, alcohols, esters, ketones and aldehydes, among others. The relative amount of these compounds and their evolution until the end of the fermentation process were assessed in both fresh and fermented grains/pulp masses. β-myrcene and β-cis-ocimene, among terpenes, were detected in high amounts and are associated to a fine chocolate aroma. The sensory evaluation of chocolates manufactured from the fermented cocoa was performed by trained panelists, which defined 15 sensory descriptors. Chocolates from the TSH565 cultivar were characterized by a rich, fruity, intense cocoa flavor and bitterness, which are valuable sensorial and commercial attributes.