Novel scaffold of natural compound eliciting sweet taste revealed by machine learning

Predicting multiple taste sensations with a multiobjective machine learning method

Taste perception plays a pivotal role in guiding nutrient intake and aiding in the avoidance of potentially harmful substances through five basic tastes - sweet, bitter, umami, salty, and sour. Taste perception originates from molecular interactions in the oral cavity between taste receptors and chemical tastants. Hence, the recognition of taste receptors and the subsequent perception of taste heavily rely on the physicochemical properties of food ingredients. In recent years, several advances have been made towards the development of machine learning-based algorithms to classify chemical compounds' tastes using their molecular structures. Despite the great efforts, there remains significant room for improvement in developing multi-class models to predict the entire spectrum of basic tastes. Here, we present a multi-class predictor aimed at distinguishing bitter, sweet, and umami, from other taste sensations. The development of a multi-class taste predictor paves the way for a comprehensive understanding of the chemical attributes associated with each fundamental taste. It also opens the potential for integration into the evolving realm of multi-sensory perception, which encompasses visual, tactile, and olfactory sensations to holistically characterize flavour perception. This concept holds promise for introducing innovative methodologies in the rational design of foods, including pre-determining specific tastes and engineering complementary diets to augment traditional pharmacological treatments.

The taste for health: the role of taste receptors and their ligands in the complex food/health relationship

Taste, food, and health are terms that have since always accompanied the act of eating, but the association was simple: taste serves to classify a food as good or bad and therefore influences food choices, which determine the nutritional status and therefore health. The identification of taste receptors, particularly, the G protein-coupled receptors that mediate sweet, umami, and bitter tastes, in the gastrointestinal tract has assigned them much more relevant tasks, from nutrient sensing and hormone release to microbiota composition and immune response and finally to a rationale for the gut-brain axis. Particularly interesting are bitter taste receptors since most of the times they do not mediate macronutrients (energy). The relevant roles of bitter taste receptors in the gut indicate that they could become new drug targets and their ligands new medications or components in nutraceutical formulations. Traditional knowledge from different cultures reported that bitterness intensity was an indicator for distinguishing plants used as food from those used as medicine, and many non-cultivated plants were used to control glucose level and treat diabetes, modulate hunger, and heal gastrointestinal disorders caused by pathogens and parasites. This concept represents a means for the scientific integration of ancient wisdom with advanced medicine, constituting a possible boost for more sustainable food and functional food innovation and design.

Bioinformatics and bioactive peptides from foods: Do they work together?

We live in the Big Data Era which affects many aspects of science, including research on bioactive peptides derived from foods, which during the last few decades have been a focus of interest for scientists. These two issues, i.e., the development of computer technologies and progress in the discovery of novel peptides with health-beneficial properties, are closely interrelated. This Chapter presents the example applications of bioinformatics for studying biopeptides, focusing on main aspects of peptide analysis as the starting point, including: (i) the role of peptide databases; (ii) aspects of bioactivity prediction; (iii) simulation of peptide release from proteins. Bioinformatics can also be used for predicting other features of peptides, including ADMET, QSAR, structure, and taste. To answer the question asked "bioinformatics and bioactive peptides from foods: do they work together?", currently it is almost impossible to find examples of peptide research with no bioinformatics involved. However, theoretical predictions are not equivalent to experimental work and always require critical scrutiny. The aspects of compatibility of in silico and in vitro results are also summarized herein.

Insights into Chemical Structure-Based Modeling for New Sweetener Discovery

The search for novel, natural, high-sweetness, low-calorie sweeteners remains open and challenging. In the present study, the structure-based machine learning modeling and sweetness recognition mechanism were investigated to assist this process. It was found that whether or not a compound was sweet was closely related to molecular connectivity and composition (the number of hydrogen bond acceptors and donors), tpsaEfficiency, structural complexity, and shape (nAtomP and Fsp3). While the relative sweetness of sweet compounds was more determined by the molecular properties (tpsaEfficiency and Log P), structural complexity and composition (nAtomP and ATSm 1). The built machine learning models exhibited very good performance for classifying the sweet/non-sweet compounds and predicting the relative sweetness of the compounds. Moreover, a specific binding pocket was found for sweet compounds, and the sweet compounds mainly interacted with the VFT domain of the T1R2-T1R3 through hydrogen bonds. In addition, the results indicated that among the sweet compounds, those that were sweeter bound to the VFT domain stronger than those that had low sweetness. This study provides very useful information for developing new sweeteners.

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.

Toward a general and interpretable umami taste predictor using a multi-objective machine learning approach

The umami taste is one of the five basic taste modalities normally linked to the protein content in food. The implementation of fast and cost-effective tools for the prediction of the umami taste of a molecule remains extremely interesting to understand the molecular basis of this taste and to effectively rationalise the production and consumption of specific foods and ingredients. However, the only examples of umami predictors available in the literature rely on the amino acid sequence of the analysed peptides, limiting the applicability of the models. In the present study, we developed a novel ML-based algorithm, named VirtuousUmami, able to predict the umami taste of a query compound starting from its SMILES representation, thus opening up the possibility of potentially using such a model on any database through a standard and more general molecular description. Herein, we have tested our model on five databases related to foods or natural compounds. The proposed tool will pave the way toward the rationalisation of the molecular features underlying the umami taste and toward the design of specific peptide-inspired compounds with specific taste properties.

Taste GPCRs and their ligands

Taste GPCRs are expressed in taste buds on the tongue and play a key role in food choice and consumption. They are also expressed extra-orally, with various physiological roles that are currently under study. Unraveling the roles of these receptors relies on the knowledge of their ligands. Combining sensory, cell-based and computational approaches enabled the discovery of numerous agonists and several antagonists. Here we provide a short overview of taste receptor families, main recent methods for ligands discovery, and current sources of information about known ligands. The future directions that are likely to impact the taste GPCR field include focus on ligand interactions with naturally occurring polymorphisms, as well as harnessing the power of CryoEM and of multiple signaling readout techniques.

ChemTastesDB: A curated database of molecular tastants

•

A chemical database called ChemTastesDB was created, which consists of 2944 organic and inorganic tastants.

•

Tastants have been curated and classified into 5 basic and 4 non-basic taste categories.

•

The chemical space of the ChemTastesDB has been analyzed with unsupervised machine learning.

•

ChemTastesDB is freely available on line and provides support for decision-making for designing new tastants.

The purpose of this work is the creation of a chemical database named ChemTastesDB that includes both organic and inorganic tastants. The creation, curation pipeline and the main features of the database are described in detail. The database includes 2944 verified and curated compounds divided into nine classes, which comprise the five basic tastes (sweet, bitter, umami sour and salty) along with four additional categories: tasteless, non-sweet, multitaste and miscellaneous. ChemTastesDB provides the following information for each tastant: name, PubChem CID, CAS registry number, canonical SMILES, class taste and references to the scientific sources from which data were retrieved. The molecular structure in the HyperChem (.hin) format of each chemical is also made available. In addition, molecular fingerprints were used for characterizing and analyzing the chemical space of tastants by means of unsupervised machine learning. ChemTastesDB constitutes a useful tool to the scientific community to expand the information of taste molecules and to assist in silico studies for the taste prediction of unevaluated and as yet unsynthetized compounds, as well as the analysis of the relationships between molecular structure and taste. The database is freely accessible at https://doi.org/10.5281/zenodo.5747393.

Evolution of Chemical Research in Nice, Côte d'Azur: From Early Laboratories to the 'Institut de Chimie de Nice'

The 'Institut de Chimie de Nice' (ICN), founded in 2012, celebrates its 10th anniversary in 2022. Today, the ICN is part of the University Côte d'Azur (UCA), one out of nine excellence universities in France. ICN is also affiliated to the CNRS. We use the institute's anniversary to reflect on the origins and the successful evolution of research in chemical sciences in Nice, France. We outline research topics and their development towards modern chemistry in Nice that are characterized by innovation and territorial anchoring. At present, four research axes, namely aroma and perfume chemistry, medicinal chemistry, radiochemistry, and material chemistry structure the institute. ICN has created five start-up companies and includes a technological platform. The ICN is central part of the university and contributes to the advancement in chemical sciences as evidenced by both fundamental research and active contributions to local partnerships.

A survey on computational taste predictors

Taste is a sensory modality crucial for nutrition and survival, since it allows the discrimination between healthy foods and toxic substances thanks to five tastes, i.e., sweet, bitter, umami, salty, and sour, associated with distinct nutritional or physiological needs. Today, taste prediction plays a key role in several fields, e.g., medical, industrial, or pharmaceutical, but the complexity of the taste perception process, its multidisciplinary nature, and the high number of potentially relevant players and features at the basis of the taste sensation make taste prediction a very complex task. In this context, the emerging capabilities of machine learning have provided fruitful insights in this field of research, allowing to consider and integrate a very large number of variables and identifying hidden correlations underlying the perception of a particular taste. This review aims at summarizing the latest advances in taste prediction, analyzing available food-related databases and taste prediction tools developed in recent years.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00217-022-04044-5.

A novel multi-layer prediction approach for sweetness evaluation based on systematic machine learning modeling

Nowadays, computational approaches have drawn more and more attention when exploring the relationship between sweetness and chemical structure instead of traditional experimental tests. In this work, we proposed a novel multi-layer sweetness evaluation system based on machine learning methods. It can be used to evaluate sweet properties of compounds with different chemical spaces and categories, including natural, artificial, carbohydrate, non-carbohydrate, nutritive and non-nutritive ones, suitable for different application scenarios. Furthermore, it provided quantitative predictions of sweetness. In addition, sweetness-related chemical basis and structure transforming rules were obtained by using molecular cloud and matched molecular pair analysis (MMPA) methods. This work systematically improved the data quality, explored the best machine learning algorithm and molecular characterizing strategy, and finally obtained robust models to establish a multi-layer prediction system (available at: https://github.com/ifyoungnet/ChemSweet). We hope that this study could facilitate food scientists with efficient screening and precise development of high-quality sweeteners.

Pharmacology of TAS1R2/TAS1R3 Receptors and Sweet Taste

The detection of energy-rich sweet food items has been important for our survival during evolution, however, in light of the changing lifestyles in industrialized and developing countries our natural sweet preference is causing considerable problems. Hence, it is even more important to understand how our sense of sweetness works, and perhaps even, how we may deceive it for our own benefit. This chapter summarizes current knowledge about sweet tastants and sweet taste modulators on the compound side as well as insights into the structure and function of the sweet taste receptor and the transduction of sweet signals. Moreover, methods to assess the activity of sweet substances in vivo and in vitro are compared and discussed.

Sugar Reduction in Dairy Food: An Overview with Flavoured Milk as an Example

Owing to the public health concern associated with the consumption of added sugar, the World Health Organization recommends cutting down sugar in processed foods. Furthermore, due to the growing concern of increased calorie intake from added sugar in sweetened dairy foods, the present review provides an overview of different types and functions of sugar, various sugar reduction strategies, and current trends in the use of sweeteners for sugar reduction in dairy food, taking flavoured milk as a central theme where possible to explore the aforementioned aspects. The strength and uniqueness of this review are that it brings together all the information on the available types of sugar and sugar reduction strategies and explores the current trends that could be applied for reducing sugar in dairy foods without much impact on consumer acceptance. Among different strategies for sugar reduction, the use of natural non-nutritive sweeteners (NNSs), has received much attention due to consumer demand for natural ingredients. Sweetness imparted by sugar can be replaced by natural NNSs, however, sugar provides more than just sweetness to flavoured milk. Sugar reduction involves multiple technical challenges to maintain the sensory properties of the product, as well as to maintain consumer acceptance. Because no single sugar has a sensory profile that matches sucrose, the use of two or more natural NNSs could be an option for food industries to reduce sugar using a holistic approach rather than a single sugar reduction strategy. Therefore, achieving even a small sugar reduction can significantly improve the diet and health of an individual.