BitterX: a tool for understanding bitter taste in humans

Ultra-high-performance liquid chromatography-mass spectrometry combined with molecular docking and molecular dynamics simulation reveals the source of bitterness in the traditional Chinese medicine formula Runchang-Tongbian

The Runchang-Tongbian (RCTB) formula is a traditional Chinese medicine (TCM) formula consisting of four herbs, namely Cannabis Fructus (Huomaren), Rehmanniae Radix (Dihuang), Atractylodis Macrocephalae Rhizoma (Baizhu), and Aurantii Fructus (Zhiqiao). It is widely used clinically because of its beneficial effect on constipation. However, its strong bitter taste leads to poor patient compliance. The bitter components of TCM compounds are complex and numerous, and inhibiting the bitter taste of TCM has become a major clinical challenge. Here, we use ultra-high-performance liquid chromatography coupled with mass spectrometry (UPLC-MS) and high-resolution mass spectrometry to identify 59 chemical components in the TCM compound RCTB formula. Next, four bitter taste receptors, TAS2R39, TAS2R14, TAS2R7, and TAS2R5, which are tightly bound to the compounds in RCTB, were screened as molecular docking receptors using the BitterX database. The top-three-scoring receptor-small-molecule complexes for each of the four receptors were selected for molecular dynamics simulation. Finally, seven bitter components were identified, namely six flavonoids (rhoifolin, naringin, poncirin, diosmin, didymin, and narirutin) and one phenylpropanoid (purpureaside C). Thus, we proposed a new method for identifying the bitter components in TCM compounds, which provides a theoretical reference for bitter taste inhibition in TCM compounds.

Machine learning approaches to predict TAS2R receptors for bitterants

Bitter taste involves the detection of diverse chemical compounds by a family of G protein-coupled receptors, known as taste receptor type 2 (TAS2R). It is often linked to toxins and harmful compounds and in particular bitter taste receptors participate in the regulation of glucose homeostasis, modulation of immune and inflammatory responses, and may have implications for various diseases. Human TAS2Rs are characterized by their polymorphism and differ in localization and function. Different receptors can activate various signaling pathways depending on the tissue and the ligand. However, in vitro screening of possible TAS2R ligands is costly and time-consuming. For this reason, in silico methods to predict bitterant-TAS2R interactions could be powerful tools to help in the selection of ligands and targets for experimental studies and improve our knowledge of bitter receptor roles. Machine learning (ML) is a branch of artificial intelligence that applies algorithms to large datasets to learn from patterns and make predictions. In recent years, there has been a record of numerous taste classifiers in literature, especially on bitter/non-bitter or bitter/sweet classification. However, only a few of them exploit ML to predict which TAS2R receptors could be targeted by bitter molecules. Indeed, the shortage and incompleteness of data on receptor-ligand associations in literature make this task non-trivial. In this work, we provide an overview of the state of the art dealing with this specific investigation, focusing on three ML-based models, namely BitterX (2016), BitterSweet (2019) and BitterMatch (2022). This review aims to establish the foundation for future research endeavours focused on addressing the limitations and drawbacks of existing models.

Exploring Bitter and Sweet: The Application of Large Language Models in Molecular Taste Prediction

The perception of bitter and sweet tastes is a crucial aspect of human sensory experience. Concerns over the long-term use of aspartame, a widely used sweetener suspected of carcinogenic risks, highlight the importance of developing new taste modifiers. This study utilizes Large Language Models (LLMs) such as GPT-3.5 and GPT-4 for predicting molecular taste characteristics, with a focus on the bitter-sweet dichotomy. Employing random and scaffold data splitting strategies, GPT-4 demonstrated superior performance, achieving an impressive 86% accuracy under scaffold partitioning. Additionally, ChatGPT was employed to extract specific molecular features associated with bitter and sweet tastes. Utilizing these insights, novel molecular compounds with distinct taste profiles were successfully generated. These compounds were validated for their bitter and sweet properties through molecular docking and molecular dynamics simulation, and their practicality was further confirmed by ADMET toxicity testing and DeepSA synthesis feasibility. This research highlights the potential of LLMs in predicting molecular properties and their implications in health and chemical science.

Integrating Computational and Experimental Methods to Identify Novel Sweet Peptides from Egg and Soy Proteins

Sweetness in food delivers a delightful sensory experience, underscoring the crucial role of sweeteners in the food industry. However, the widespread use of sweeteners has sparked health concerns. This underscores the importance of developing and screening natural, health-conscious sweeteners. Our study represents a groundbreaking venture into the discovery of such sweeteners derived from egg and soy proteins. Employing virtual hydrolysis as a novel technique, our research entailed a comprehensive screening process that evaluated biological activity, solubility, and toxicity of the derived compounds. We harnessed cutting-edge machine learning methodologies, specifically the latest graph neural network models, for predicting the sweetness of molecules. Subsequent refinements were made through molecular docking screenings and molecular dynamics simulations. This meticulous research approach culminated in the identification of three promising sweet peptides: DCY(Asp-Cys-Tyr), GGR(Gly-Gly-Arg), and IGR(Ile-Gly-Arg). Their binding affinity with T1R2/T1R3 was lower than -15 kcal/mol. Using an electronic tongue, we verified the taste profiles of these peptides, with IGR emerging as the most favorable in terms of taste with a sweetness value of 19.29 and bitterness value of 1.71. This study not only reveals the potential of these natural peptides as healthier alternatives to traditional sweeteners in food applications but also demonstrates the successful synergy of computational predictions and experimental validations in the realm of flavor science.

Bitter flavors and bitter compounds in foods: identification, perception, and reduction techniques

Bitterness is one of the five basic tastes generally considered undesirable. The widespread presence of bitter compounds can negatively affect the palatability of foods. The classification and sensory evaluation of bitter compounds have been the focus in recent research. However, the rigorous identification of bitter tastes and further studies to effectively mask or remove them have not been thoroughly evaluated. The present paper focuses on identification of bitter compounds in foods, structural-based activation of bitter receptors, and strategies to reduce bitter compounds in foods. It also discusses the roles of metabolomics and virtual screening analysis in bitter taste. The identification of bitter compounds has seen greater success through metabolomics with multivariate statistical analysis compared to conventional chromatography, HPLC, LC-MS, and NMR techniques. However, to avoid false positives, sensory recognition should be combined. Bitter perception involves the structural activation of bitter taste receptors (TAS2Rs). Only 25 human TAS2Rs have been identified as responsible for recognizing numerous bitter compounds, showcasing their high structural diversity to bitter agonists. Thus, reducing bitterness can be achieved through several methods. Traditionally, the removal or degradation of bitter substances has been used for debittering, while the masking of bitterness presents a new effective approach to improving food flavor. Future research in food bitterness should focus on identifying unknown bitter compounds in food, elucidating the mechanisms of activation of different receptors, and developing debittering techniques based on the entire food matrix.

Prediction of bitterness based on modular designed graph neural network

Motivation

Bitterness plays a pivotal role in our ability to identify and evade harmful substances in food. As one of the five tastes, it constitutes a critical component of our sensory experiences. However, the reliance on human tasting for discerning flavors presents cost challenges, rendering in silico prediction of bitterness a more practical alternative.

Results

In this study, we introduce the use of Graph Neural Networks (GNNs) in bitterness prediction, superseding traditional machine learning techniques. We developed an advanced model, a Hybrid Graph Neural Network (HGNN), surpassing conventional GNNs according to tests on public datasets. Using HGNN and three other GNNs, we designed BitterGNNs, a bitterness predictor that achieved an AUC value of 0.87 in both external bitter/non-bitter and bitter/sweet evaluations, outperforming the acclaimed RDKFP-MLP predictor with AUC values of 0.86 and 0.85. We further created a bitterness prediction website and database, TastePD (https://www.tastepd.com/). The BitterGNNs predictor, built on GNNs, offers accurate bitterness predictions, enhancing the efficacy of bitterness prediction, aiding advanced food testing methodology development, and deepening our understanding of bitterness origins.

Availability and implementation

TastePD can be available at https://www.tastepd.com, all codes are at https://github.com/heyigacu/BitterGNN.

BitterDB database analysis plus cell stiffness screening identify flufenamic acid as the most potent TAS2R14-based relaxant of airway smooth muscle cells for therapeutic bronchodilation

Rationale: Bitter taste receptors (TAS2Rs) are abundantly expressed in airway smooth muscle cells (ASMCs), which have been recognized as promising targets for bitter agonists to initiate relaxation and thereby prevent excessive airway constriction as the main characteristic of asthma. However, due to the current lack of tested safe and potent agonists functioning at low effective concentrations, there has been no clinically approved TAS2R-based drug for bronchodilation in asthma therapy. This study thus aimed at exploring TAS2R agonists with bronchodilator potential by BitterDB database analysis and cell stiffness screening. Methods: Bitter compounds in the BitterDB database were retrieved and analyzed for their working subtype of TAS2R and effective concentration. Compounds activating TAS2R5, 10, and 14 at < 100 μM effective concentration were identified and subsequently screened by cell stiffness assay using optical magnetic twisting cytometry (OMTC) to identify the most potent to relax ASMCs. Then the compound identified was further characterized for efficacy on various aspects related to relaxation of ASMCs, incl. but not limited to traction force by Fourier transform traction force microscopy (FTTFM), [Ca2+]i signaling by Fluo-4/AM intensity, cell migration by scratch wound healing, mRNA expression by qPCR, and protein expressing by ELISA. The compound identified was also compared to conventional β-agonist (isoproterenol and salbutamol) for efficacy in reducing cell stiffness of cultured ASMCs and airway resistance of ovalbumin-treated mice. Results: BitterDB analysis found 18 compounds activating TAS2R5, 10, and 14 at < 100 μM effective concentration. Cell stiffness screening of these compounds eventually identified flufenamic acid (FFA) as the most potent compound to rapidly reduce cell stiffness at 1 μM. The efficacy of FFA to relax ASMCs in vitro and abrogate airway resistance in vivo was equivalent to that of conventional β-agonists. The FFA-induced effect on ASMCs was mediated by TAS2R14 activation, endoplasmic reticulum Ca2+ release, and large-conductance Ca2+-activated K+ (BKCa) channel opening. FFA also attenuated lipopolysaccharide-induced inflammatory response in cultured ASMCs. Conclusions: FFA as a potent TAS2R14 agonist to relax ASMCs while suppressing cytokine release might be a favorite drug agent for further development of TAS2R-based novel dual functional medication for bronchodilation and anti-inflammation in asthma therapy.

Bitter Phytochemicals as Novel Candidates for Skin Disease Treatment

Skin diseases represent a global healthcare challenge due to their rising incidence and substantial socio-economic burden. While biological, immunological, and targeted therapies have brought a revolution in improving quality of life and survival rates for certain dermatological conditions, there remains a stringent demand for new remedies. Nature has long served as an inspiration for drug development. Recent studies have identified bitter taste receptors (TAS2Rs) in both skin cell lines and human skin. Additionally, bitter natural compounds have shown promising benefits in addressing skin aging, wound healing, inflammatory skin conditions, and even skin cancer. Thus, TAS2Rs may represent a promising target in all these processes. In this review, we summarize evidence supporting the presence of TAS2Rs in the skin and emphasize their potential as drug targets for addressing skin aging, wound healing, inflammatory skin conditions, and skin carcinogenesis. To our knowledge, this is a pioneering work in connecting information on TAS2Rs expression in skin and skin cells with the impact of bitter phytochemicals on various beneficial effects related to skin disorders.

Classification of tastants: A deep learning based approach

Predicting the taste of molecules is of critical importance in the food and beverages, flavor, and pharmaceutical industries for the design and screening of new tastants. In this work, we have built deep learning models to classify sweet, bitter, and umami molecules- the three basic tastes whose sensation is mediated by G protein-coupled receptors. An extensive dataset containing 1466 bitter, 1764 sweet, and 238 umami tastants was curated from existing literature. We analyzed the chemical characteristics of the molecules, with special focus on the presence of different functional groups. A deep neural network model based on molecular descriptors and a graph neural network model were trained for taste prediction. The class imbalance due to fewer umami molecules was tackled using special sampling techniques. Both models show comparable performance during evaluation, but the graph-based model can learn task-specific representations from the molecular structure without requiring handcrafted features. We further explain the deep neural network predictions using Shapley additive explanations. Finally, we demonstrated the applicability of the models by screening bitter, sweet, and umami molecules from a large food database. This study develops an in-silico approach to classify molecules based on their taste by leveraging the recent progress in deep learning, which can serve as a powerful tool for tastant design.

Classification-based machine learning approaches to predict the taste of molecules: A review

The capacity to discriminate safe from dangerous compounds has played an important role in the evolution of species, including human beings. Highly evolved senses such as taste receptors allow humans to navigate and survive in the environment through information that arrives to the brain through electrical pulses. Specifically, taste receptors provide multiple bits of information about the substances that are introduced orally. These substances could be pleasant or not according to the taste responses that they trigger. Tastes have been classified into basic (sweet, bitter, umami, sour and salty) or non-basic (astringent, chilling, cooling, heating, pungent), while some compounds are considered as multitastes, taste modifiers or tasteless. Classification-based machine learning approaches are useful tools to develop predictive mathematical relationships in such a way as to predict the taste class of new molecules based on their chemical structure. This work reviews the history of multicriteria quantitative structure-taste relationship modelling, starting from the first ligand-based (LB) classifier proposed in 1980 by Lemont B. Kier and concluding with the most recent studies published in 2022.

Conserved Sites and Recognition Mechanisms of T1R1 and T2R14 Receptors Revealed by Ensemble Docking and Molecular Descriptors and Fingerprints Combined with Machine Learning

Taste peptides, as an important component of protein-rich foodstuffs, potentiate the nutrition and taste of food. Thereinto, umami- and bitter-taste peptides have been ex tensively reported, while their taste mechanisms remain unclear. Meanwhile, the identification of taste peptides is still a time-consuming and costly task. In this study, 489 peptides with umami/bitter taste from TPDB (http://tastepeptides-meta.com/) were collected and used to train the classification models based on docking analysis, molecular descriptors (MDs), and molecular fingerprints (FPs). A consensus model, taste peptide docking machine (TPDM), was generated based on five learning algorithms (linear regression, random forest, gaussian naive bayes, gradient boosting tree, and stochastic gradient descent) and four molecular representation schemes. Model interpretive analysis showed that MDs (VSA_EState, MinEstateIndex, MolLogP) and FPs (598, 322, 952) had the greatest impact on the umami/bitter prediction of peptides. Based on the consensus docking results, we obtained the key recognition modes of umami/bitter receptors (T1Rs/T2Rs): (1) residues 107S-109S, 148S-154T, 247F-249A mainly form hydrogen bonding contacts and (2) residues 153A-158L, 163L, 181Q, 218D, 247F-249A in T1R1 and 56D, 106P, 107V, 152V-156F, 173K-180F in T2R14 constituted their hydrogen bond pockets. The model is available at http://www.tastepeptides-meta.com/yyds.

An overview of bitter compounds in foodstuffs: Classifications, evaluation methods for sensory contribution, separation and identification techniques, and mechanism of bitter taste transduction

The bitter taste is generally considered an undesirable sensory attribute. However, bitter-tasting compounds can significantly affect the overall flavor of many foods and beverages and endow them with various beneficial effects on human health. To better understand the relationship between chemical structure and bitterness, this paper has summarized the bitter compounds in foodstuffs and classified them based on the basic skeletons. Only those bitter compounds that are confirmed by human sensory evaluation have been included in this paper. To develop food products that satisfy consumer preferences, correctly ranking the key bitter compounds in foodstuffs according to their contributions to the overall bitterness intensity is the precondition. Generally, three methods were applied to screen out the key bitter compounds in foods and beverages and evaluate their sensory contributions, including dose-over-threshold factors, taste dilution analysis, and spectrum descriptive analysis method. This paper has discussed in detail the mechanisms and applications of these three methods. Typical procedures for separating and identifying the main bitter compounds in foodstuffs have also been summarized. Additionally, the activation of human bitter taste receptors (TAS2Rs) and the mechanisms of bitter taste transduction are outlined. Ultimately, a conclusion has been drawn to highlight the current problems and propose potential directions for further 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.

New strategies for identifying and masking the bitter taste in traditional herbal medicines: The example of Huanglian Jiedu Decoction

Suppressing the bitter taste of traditional Chinese medicine (TCM) largely has been a major clinical challenge due to complex and diverse metabolites and high dispersion of bitter metabolites in liquid preparations. In this work, we developed a novel strategy for recognizing bitter substances, hiding their bitter taste, and elucidated the mechanism of flavor masking in TCM. Huanglian Jie-Du Decoction (HLJDD) with an intense bitter taste was studied as a typical case. UHPLC-MS/MS was used to analyze the chemical components in HLJDD, whereas the bitter substances were identified by pharmacophores. Additionally, the screening results of the pharmacophores were further validated by using experimental assays. The mask formula of HLJDD was effectively screened under the condition of clear bitter substances. Subsequently, computational chemistry, molecular docking, and infrared characterization (IR) techniques were then used to explicate the mechanism of flavor masking. Consequently, neotame, γ-CD, and mPEG₂₀₀₀-PLLA₂₀₀₀ significantly reduced the bitterness of HLJDD. Specifically, mPEG₂₀₀₀-PLLA₂₀₀₀ increased the colloid proportion in the decoction system and minimized the distribution of bitter components in the real solution. Sweetener neotame suppressed the perception of bitter taste and inhibited bitter taste receptor activation to eventually reduce the bitter taste. The γ-CD included in the decoction bound the hydrophobic groups of the bitter metabolites in real solution and “packed” all or part of the bitter metabolites into the “cavity”. We established a novel approach for screening bitter substances in TCM by integrating virtual screening and experimental assays. Based on this strategy, the bitter taste masking of TCM was performed from three different aspects, namely, changing the drug phase state, component distribution, and interfering with bitter taste signal transduction. Collectively, the methods achieved a significant effect on bitter taste suppression and taste masking. Our findings will provide a novel strategy for masking the taste of TCM liquid preparation/decoction, which will in return help in improving the clinical efficacy of TCM.

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.

Identify Bitter Peptides by Using Deep Representation Learning Features

A bitter taste often identifies hazardous compounds and it is generally avoided by most animals and humans. Bitterness of hydrolyzed proteins is caused by the presence of bitter peptides. To improve palatability, bitter peptides need to be identified experimentally in a time-consuming and expensive process, before they can be removed or degraded. Here, we report the development of a machine learning prediction method, iBitter-DRLF, which is based on a deep learning pre-trained neural network feature extraction method. It uses three sequence embedding techniques, soft symmetric alignment (SSA), unified representation (UniRep), and bidirectional long short-term memory (BiLSTM). These were initially combined into various machine learning algorithms to build several models. After optimization, the combined features of UniRep and BiLSTM were finally selected, and the model was built in combination with a light gradient boosting machine (LGBM). The results showed that the use of deep representation learning greatly improves the ability of the model to identify bitter peptides, achieving accurate prediction based on peptide sequence data alone. By helping to identify bitter peptides, iBitter-DRLF can help research into improving the palatability of peptide therapeutics and dietary supplements in the future. A webserver is available, too.

BitterMatch: recommendation systems for matching molecules with bitter taste receptors

Bitterness is an aversive cue elicited by thousands of chemically diverse compounds. Bitter taste may prevent consumption of foods and jeopardize drug compliance. The G protein-coupled receptors for bitter taste, TAS2Rs, have species-dependent number of subtypes and varying expression levels in extraoral tissues. Molecular recognition by TAS2R subtypes is physiologically important, and presents a challenging case study for ligand-receptor matchmaking. Inspired by hybrid recommendation systems, we developed a new set of similarity features, and created the BitterMatch algorithm that predicts associations of ligands to receptors with ~ 80% precision at ~ 50% recall. Associations for several compounds were tested in-vitro, resulting in 80% precision and 42% recall. The encouraging performance was achieved by including receptor properties and integrating experimentally determined ligand-receptor associations with chemical ligand-to-ligand similarities.

BitterMatch can predict off-targets for bitter drugs, identify novel ligands and guide flavor design. The novel features capture information regarding the molecules and their receptors, which could inform various chemoinformatic tasks. Inclusion of neighbor-informed similarities improves as experimental data mounts, and provides a generalizable framework for molecule-biotarget matching.

ChemTastesDB: A curated database of molecular tastants

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

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

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

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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.

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.

Piperine, as a TAS2R14 agonist, stimulates the secretion of glucagon-like peptide-1 in the human enteroendocrine cell line Caco-2

Piperine is reported to ameliorate common metabolic diseases, however, its molecular mechanism is still unclear. In the present study, we examined whether piperine could stimulate glucagon-like peptide-1 (GLP-1) secretion in a human enteroendocrine cell line, Caco-2, and explored the potential mechanisms from the activation of human bitter taste receptors (TAS2Rs). It was found that TAS2R14 was highly expressed in Caco-2 cells, far more than TAS2R4 and TAS2R10. Piperine and flufenamic acid (FA, a known TAS2R14 agonist) markedly increased intracellular calcium mobilization and significantly enhanced the GLP-1 secretion, accompanied by elevated levels of proglucagon mRNA in Caco-2 cells compared with the control. Moreover, piperine and FA activated TAS2R14 signaling as evidenced by the increased mRNA and protein levels of TAS2R14, and the protein expression of its downstream key molecules including phospholipase C β2 (PLCβ2) and a transient receptor potential channel melastatin 5 (TRPM5). On the other hand, a G protein βγ subunit inhibitor Gallein or a PLC inhibitor U73122 alleviated piperine-stimulated GLP-1 secretion in Caco-2 cells. In the meantime, a flavanone hesperetin significantly attenuated piperine and FA induced the intracellular calcium mobilization and GLP-1 secretion. Furthermore, TAS2R14 knockdown reversed the piperine-triggered up-regulation of PLCβ2 and TRPM5 as well as increased the GLP-1 secretion in Caco-2 cells by TAS2R14 shRNA transfection. In summary, our findings demonstrated that piperine promoted the GLP-1 secretion from enteroendocrine cells through the activation of TAS2R14 signaling. Moreover, TAS2R14 was likely a target of piperine in the alleviation of metabolic diseases.

iBitter-Fuse: A Novel Sequence-Based Bitter Peptide Predictor by Fusing Multi-View Features

Accurate identification of bitter peptides is of great importance for better understanding their biochemical and biophysical properties. To date, machine learning-based methods have become effective approaches for providing a good avenue for identifying potential bitter peptides from large-scale protein datasets. Although few machine learning-based predictors have been developed for identifying the bitterness of peptides, their prediction performances could be improved. In this study, we developed a new predictor (named iBitter-Fuse) for achieving more accurate identification of bitter peptides. In the proposed iBitter-Fuse, we have integrated a variety of feature encoding schemes for providing sufficient information from different aspects, namely consisting of compositional information and physicochemical properties. To enhance the predictive performance, the customized genetic algorithm utilizing self-assessment-report (GA-SAR) was employed for identifying informative features followed by inputting optimal ones into a support vector machine (SVM)-based classifier for developing the final model (iBitter-Fuse). Benchmarking experiments based on both 10-fold cross-validation and independent tests indicated that the iBitter-Fuse was able to achieve more accurate performance as compared to state-of-the-art methods. To facilitate the high-throughput identification of bitter peptides, the iBitter-Fuse web server was established and made freely available online. It is anticipated that the iBitter-Fuse will be a useful tool for aiding the discovery and de novo design of bitter peptides.

OdoriFy: A conglomerate of artificial intelligence-driven prediction engines for olfactory decoding

The molecular mechanisms of olfaction, or the sense of smell, are relatively underexplored compared with other sensory systems, primarily because of its underlying molecular complexity and the limited availability of dedicated predictive computational tools. Odorant receptors (ORs) allow the detection and discrimination of a myriad of odorant molecules and therefore mediate the first step of the olfactory signaling cascade. To date, odorant (or agonist) information for the majority of these receptors is still unknown, limiting our understanding of their functional relevance in odor-induced behavioral responses. In this study, we introduce OdoriFy, a Web server featuring powerful deep neural network-based prediction engines. OdoriFy enables (1) identification of odorant molecules for wildtype or mutant human ORs (Odor Finder); (2) classification of user-provided chemicals as odorants/nonodorants (Odorant Predictor); (3) identification of responsive ORs for a query odorant (OR Finder); and (4) interaction validation using Odorant-OR Pair Analysis. In addition, OdoriFy provides the rationale behind every prediction it makes by leveraging explainable artificial intelligence. This module highlights the basis of the prediction of odorants/nonodorants at atomic resolution and for the ORs at amino acid levels. A key distinguishing feature of OdoriFy is that it is built on a comprehensive repertoire of manually curated information of human ORs with their known agonists and nonagonists, making it a highly interactive and resource-enriched Web server. Moreover, comparative analysis of OdoriFy predictions with an alternative structure-based ligand interaction method revealed comparable results. OdoriFy is available freely as a web service at https://odorify.ahujalab.iiitd.edu.in/olfy/.

BERT4Bitter: a bidirectional encoder representations from transformers (BERT)-based model for improving the prediction of bitter peptides

MOTIVATION

The identification of bitter peptides through experimental approaches is an expensive and time-consuming endeavor. Due to the huge number of newly available peptide sequences in the post-genomic era, the development of automated computational models for the identification of novel bitter peptides is highly desira-ble.

RESULTS

In this work, we present BERT4Bitter, a bidirectional encoder representation from transformers (BERT)-based model for predicting bitter peptides directly from their amino acid sequence without using any structural information. To the best of our knowledge, this is the first time a BERT-based model has been employed to identify bitter peptides. Compared to widely used machine learning models, BERT4Bitter achieved the best performance with accuracy of 0.861 and 0.922 for cross-validation and independent tests, respectively. Furthermore, extensive empirical benchmarking experiments on the independent dataset demonstrated that BERT4Bitter clearly outperformed the existing method with improvements of > 8% accuracy and >16% Matthews coefficient correlation, highlighting the effectiveness and robustness of BERT4Bitter. We believe that the BERT4Bitter method proposed herein will be a useful tool for rapidly screening and identifying novel bitter peptides for drug development and nutritional research.

AVAILABILITY

The user-friendly web server of the proposed BERT4Bitter is freely accessible at: http://pmlab.pythonanywhere.com/BERT4Bitter.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Machine-OlF-Action: A unified framework for developing and interpreting machine-learning models for chemosensory research

 

Machine Learning-based techniques are emerging as state-of-the-art methods in chemoinformatics to selectively, effectively, and speedily identify biologically-relevant molecules from large databases. So far, a multitude of such techniques have been proposed, but unfortunately due to their sparse availability, and the dependency on high-end computational literacy, their wider adaptation faces challenges, at least in the context of G-Protein Coupled Receptors (GPCRs)-associated chemosensory research. Here we report Machine-OlF-Action (MOA), a user-friendly, open-source computational framework, that utilizes user-supplied SMILES (simplified molecular-input line-entry system) of the chemicals, along with their activation status, to synthesize classification models. MOA integrates a number of popular chemical databases collectively harboring ∼103 million chemical moieties. MOA also facilitates customized screening of user-supplied chemical datasets. A key feature of MOA is its ability to embed molecules based on the similarity of their local neighborhood, by utilizing a state of the art model interpretability framework LIME. We demonstrate the utility of MOA in identifying previously unreported agonists for human and mouse olfactory receptors OR1A1 and MOR174-9 by leveraging the chemical features of their known agonists and non-agonists. In summary, here we develop an ML-powered software playground for performing supervisory learning tasks involving chemical compounds.

AVAILABILITY AND IMPLEMENTATION

MOA is available for Windows, Mac, and Linux operating systems. It's accessible at (https://ahuja-lab.in/). Source code, user manual, step-and-step guide, and support is available at GitHub (https://github.com/the-ahuja-lab/Machine-Olf-Action). For results, reproducibility and hyperparameters, refer to Supplementary Notes.

Intense bitterness of molecules: Machine learning for expediting drug discovery

Drug development is a long, expensive and multistage process geared to achieving safe drugs with high efficacy. A crucial prerequisite for completing the medication regimen for oral drugs, particularly for pediatric and geriatric populations, is achieving taste that does not hinder compliance. Currently, the aversive taste of drugs is tested in late stages of clinical trials. This can result in the need to reformulate, potentially resulting in the use of more animals for additional toxicity trials, increased financial costs and a delay in release to the market. Here we present BitterIntense, a machine learning tool that classifies molecules into "very bitter" or "not very bitter", based on their chemical structure. The model, trained on chemically diverse compounds, has above 80% accuracy on several test sets. Our results suggest that about 25% of drugs are predicted to be very bitter, with even higher prevalence (~40%) in COVID19 drug candidates and in microbial natural products. Only ~10% of toxic molecules are predicted to be intensely bitter, and it is also suggested that intense bitterness does not correlate with hepatotoxicity of drugs. However, very bitter compounds may be more cardiotoxic than not very bitter compounds, possessing significantly lower QPlogHERG values. BitterIntense allows quick and easy prediction of strong bitterness of compounds of interest for food, pharma and biotechnology industries. We estimate that implementation of BitterIntense or similar tools early in drug discovery process may lead to reduction in delays, in animal use and in overall financial burden.

Identification and Comparison of Peptides from Chickpea Protein Hydrolysates Using Either Bromelain or Gastrointestinal Enzymes and Their Relationship with Markers of Type 2 Diabetes and Bitterness

The chickpea (Cicer arietinum L.) is one of the most important pulses worldwide. The objective was to identify, compare and evaluate peptides from chickpea hydrolysates produced by two enzymatic treatments. The antidiabetic potential and bitterness of the peptides and induction of bitter receptors were identified in silico. Proteins were isolated from the Kabuli variety. Peptides were produced from the proteins using a simulated digestive system (pepsin/pancreatin, 1:50 Enzyme/Protein, E/P), and these peptides were compared with those produced via bromelain hydrolysis (1:50 E/P). The protein profiles, sequences and characteristics of the peptides were evaluated. The biochemical inhibition and molecular docking of dipeptidyl peptidase-IV (DPP-IV), α-amylase and α-glucosidase were also studied. The molecular docking identified peptides from enzymatic hydrolysis as inhibitors of DPP-IV. The high hydrophobicity of the peptides indicated the potential for bitterness. There was no correlation between peptide length and DPP-IV binding. Peptides sequenced from the pepsin/pancreatin hydrolysates, PHPATSGGGL and YVDGSGTPLT, had greater affinity for the DPP-IV catalytic site than the peptides from the bromelain hydrolysates. These results are in agreement with their biochemical inhibition, when considering the inhibition of sitagliptin (54.3 µg/mL) as a standard. The bitter receptors hTAS2R38, hTAS2R5, hTAS2R7 and hTAS2R14 were stimulated by most sequences, which could be beneficial in the treatment of type 2 diabetes. Chickpea hydrolysates could be utilized as functional ingredients to be included in the diet for the prevention of diabetes.

BitterDB: taste ligands and receptors database in 2019

BitterDB (http://bitterdb.agri.huji.ac.il) was introduced in 2012 as a central resource for information on bitter-tasting molecules and their receptors. The information in BitterDB is frequently used for choosing suitable ligands for experimental studies, for developing bitterness predictors, for analysis of receptors promiscuity and more. Here, we describe a major upgrade of the database, including significant increase in content as well as new features. BitterDB now holds over 1000 bitter molecules, up from the initial 550. When available, quantitative sensory data on bitterness intensity as well as toxicity information were added. For 270 molecules, at least one associated bitter taste receptor (T2R) is reported. The overall number of ligand-T2R associations is now close to 800. BitterDB was extended to several species: in addition to human, it now holds information on mouse, cat and chicken T2Rs, and the compounds that activate them. BitterDB now provides a unique platform for structure-based studies with high-quality homology models, known ligands, and for the human receptors also data from mutagenesis experiments, information on frequently occurring single nucleotide polymorphisms and links to expression levels in different tissues.

Mechanistic role of plant-based bitter principles and bitterness prediction for natural product studies II: prediction tools and case studies

The first part of this chapter provides an overview of computer-based tools (algorithms, web servers, and software) for the prediction of bitterness in compounds. These tools all implement machine learning (ML) methods and are all freely accessible. For each tool, a brief description of the implemented method is provided, along with the training sets and the benchmarking results. In the second part, an attempt has been made to explain at the mechanistic level why some medicinal plants are bitter and how plants use bitter natural compounds, obtained through the biosynthetic process as important ingredients for adapting to the environment. A further exploration is made on the role of bitter natural products in the defense mechanism of plants against insect pest, herbivores, and other invaders. Case studies have focused on alkaloids, terpenoids, cyanogenic glucosides and phenolic derivatives.

Toward the Identification of Extra-Oral TAS2R Agonists as Drug Agents for Muscle Relaxation Therapies via Bioinformatics-Aided Screening of Bitter Compounds in Traditional Chinese Medicine

Significant advances have been made in the past decade in mapping the distributions and the physiological functions of extra-oral bitter taste receptors (TAS2Rs) in non-gustatory tissues. In particular, it has been found that TAS2Rs are expressed in various muscle tissues and activation of TAS2Rs can lead to muscle cell relaxation, which suggests that TAS2Rs may be important new targets in muscle relaxation therapy for various muscle-related diseases. So far, however, there is a lack of potent extra-oral TAS2R agonists that can be used as novel drug agents in muscle relaxation therapies. Interestingly, traditional Chinese medicine (TCM) often characterizes a drug’s property in terms of five distinct flavors (bitter, sweet, sour, salty, and pungent) according to its taste and function, and commonly regards “bitterness” as an intrinsic property of “good medicine.” In addition, many bitter flavored TCM are known in practice to cause muscle relaxation after long term use, and in lab experiments the compounds identified from some bitter flavored TCM do activate TAS2Rs and thus relax muscle cells. Therefore, it is highly possible to discover very useful extra-oral TAS2R agonists for muscle relaxation therapies among the abundant bitter compounds used in bitter flavored TCM. With this perspective, we reviewed in literature the distribution of TAS2Rs in different muscle systems with a focus on the map of bitter flavored TCM which can regulate muscle contractility and related functional chemical components. We also reviewed the recently established databases of TCM chemical components and the bioinformatics software which can be used for high-throughput screening and data mining of the chemical components associated with bitter flavored TCM. All together, we aim to present a knowledge-based approach and technological platform for identification or discovery of extra-oral TAS2R agonists that can be used as novel drug agents for muscle relaxation therapies through screening and evaluation of chemical compounds used in bitter flavored TCM.

BitterSweet: Building machine learning models for predicting the bitter and sweet taste of small molecules

The dichotomy of sweet and bitter tastes is a salient evolutionary feature of human gustatory system with an innate attraction to sweet taste and aversion to bitterness. A better understanding of molecular correlates of bitter-sweet taste gradient is crucial for identification of natural as well as synthetic compounds of desirable taste on this axis. While previous studies have advanced our understanding of the molecular basis of bitter-sweet taste and contributed models for their identification, there is ample scope to enhance these models by meticulous compilation of bitter-sweet molecules and utilization of a wide spectrum of molecular descriptors. Towards these goals, our study provides a structured compilation of bitter, sweet and tasteless molecules and state-of-the-art machine learning models for bitter-sweet taste prediction (BitterSweet). We compare different sets of molecular descriptors for their predictive performance and further identify important features as well as feature blocks. The utility of BitterSweet models is demonstrated by taste prediction on large specialized chemical sets such as FlavorDB, FooDB, SuperSweet, Super Natural II, DSSTox, and DrugBank. To facilitate future research in this direction, we make all datasets and BitterSweet models publicly available, and present an end-to-end software for bitter-sweet taste prediction based on freely available chemical descriptors.

Activation of airway epithelial bitter taste receptors by Pseudomonas aeruginosa quinolones modulates calcium, cyclic-AMP, and nitric oxide signaling

Bitter taste receptors (taste family 2 bitter receptor proteins; T2Rs), discovered in many tissues outside the tongue, have recently become potential therapeutic targets. We have shown previously that airway epithelial cells express several T2Rs that activate innate immune responses that may be important for treatment of airway diseases such as chronic rhinosinusitis. It is imperative to more clearly understand what compounds activate airway T2Rs as well as their full range of functions. T2R isoforms in airway motile cilia (T2R4, -14, -16, and -38) produce bactericidal levels of nitric oxide (NO) that also increase ciliary beating, promoting clearance of mucus and trapped pathogens. Bacterial quorum-sensing acyl-homoserine lactones activate T2Rs and stimulate these responses in primary airway cells. Quinolones are another type of quorum-sensing molecule used by Pseudomonas aeruginosa To elucidate whether bacterial quinolones activate airway T2Rs, we analyzed calcium, cAMP, and NO dynamics using a combination of fluorescent indicator dyes and FRET-based protein biosensors. T2R-transfected HEK293T cells, several lung epithelial cell lines, and primary sinonasal cells grown and differentiated at the air-liquid interface were tested with 2-heptyl-3-hydroxy-4-quinolone (known as Pseudomonas quinolone signal; PQS), 2,4-dihydroxyquinolone, and 4-hydroxy-2-heptylquinolone (HHQ). In HEK293T cells, PQS activated T2R4, -16, and -38, whereas HHQ activated T2R14. 2,4-Dihydroxyquinolone had no effect. PQS and HHQ increased calcium and decreased both baseline and stimulated cAMP levels in cultured and primary airway cells. In primary cells, PQS and HHQ activated levels of NO synthesis previously shown to be bactericidal. This study suggests that airway T2R-mediated immune responses are activated by bacterial quinolones as well as acyl-homoserine lactones.

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.

BitterSweetForest: A Random Forest Based Binary Classifier to Predict Bitterness and Sweetness of Chemical Compounds

Taste of a chemical compound present in food stimulates us to take in nutrients and avoid poisons. However, the perception of taste greatly depends on the genetic as well as evolutionary perspectives. The aim of this work was the development and validation of a machine learning model based on molecular fingerprints to discriminate between sweet and bitter taste of molecules. BitterSweetForest is the first open access model based on KNIME workflow that provides platform for prediction of bitter and sweet taste of chemical compounds using molecular fingerprints and Random Forest based classifier. The constructed model yielded an accuracy of 95% and an AUC of 0.98 in cross-validation. In independent test set, BitterSweetForest achieved an accuracy of 96% and an AUC of 0.98 for bitter and sweet taste prediction. The constructed model was further applied to predict the bitter and sweet taste of natural compounds, approved drugs as well as on an acute toxicity compound data set. BitterSweetForest suggests 70% of the natural product space, as bitter and 10% of the natural product space as sweet with confidence score of 0.60 and above. 77% of the approved drug set was predicted as bitter and 2% as sweet with a confidence score of 0.75 and above. Similarly, 75% of the total compounds from acute oral toxicity class were predicted only as bitter with a minimum confidence score of 0.75, revealing toxic compounds are mostly bitter. Furthermore, we applied a Bayesian based feature analysis method to discriminate the most occurring chemical features between sweet and bitter compounds using the feature space of a circular fingerprint.

e-Bitter: Bitterant Prediction by the Consensus Voting From the Machine-Learning Methods

In-silico bitterant prediction received the considerable attention due to the expensive and laborious experimental-screening of the bitterant. In this work, we collect the fully experimental dataset containing 707 bitterants and 592 non-bitterants, which is distinct from the fully or partially hypothetical non-bitterant dataset used in the previous works. Based on this experimental dataset, we harness the consensus votes from the multiple machine-learning methods (e.g., deep learning etc.) combined with the molecular fingerprint to build the bitter/bitterless classification models with five-fold cross-validation, which are further inspected by the Y-randomization test and applicability domain analysis. One of the best consensus models affords the accuracy, precision, specificity, sensitivity, F1-score, and Matthews correlation coefficient (MCC) of 0.929, 0.918, 0.898, 0.954, 0.936, and 0.856 respectively on our test set. For the automatic prediction of bitterant, a graphic program “e-Bitter” is developed for the convenience of users via the simple mouse click. To our best knowledge, it is for the first time to adopt the consensus model for the bitterant prediction and develop the first free stand-alone software for the experimental food scientist.

Bitter or not? BitterPredict, a tool for predicting taste from chemical structure

Bitter taste is an innately aversive taste modality that is considered to protect animals from consuming toxic compounds. Yet, bitterness is not always noxious and some bitter compounds have beneficial effects on health. Hundreds of bitter compounds were reported (and are accessible via the BitterDB http://bitterdb.agri.huji.ac.il/dbbitter.php), but numerous additional bitter molecules are still unknown. The dramatic chemical diversity of bitterants makes bitterness prediction a difficult task. Here we present a machine learning classifier, BitterPredict, which predicts whether a compound is bitter or not, based on its chemical structure. BitterDB was used as the positive set, and non-bitter molecules were gathered from literature to create the negative set. Adaptive Boosting (AdaBoost), based on decision trees machine-learning algorithm was applied to molecules that were represented using physicochemical and ADME/Tox descriptors. BitterPredict correctly classifies over 80% of the compounds in the hold-out test set, and 70–90% of the compounds in three independent external sets and in sensory test validation, providing a quick and reliable tool for classifying large sets of compounds into bitter and non-bitter groups. BitterPredict suggests that about 40% of random molecules, and a large portion (66%) of clinical and experimental drugs, and of natural products (77%) are bitter.

Flavones modulate respiratory epithelial innate immunity: Anti-inflammatory effects and activation of the T2R14 receptor

Chronic rhinosinusitis has a significant impact on patient quality of life, creates billions of dollars of annual healthcare costs, and accounts for ∼20% of adult antibiotic prescriptions in the United States. Because of the rise of resistant microorganisms, there is a critical need to better understand how to stimulate and/or enhance innate immune responses as a therapeutic modality to treat respiratory infections. We recently identified bitter taste receptors (taste family type 2 receptors, or T2Rs) as important regulators of sinonasal immune responses and potentially important therapeutic targets. Here, we examined the immunomodulatory potential of flavones, a class of flavonoids previously demonstrated to have antibacterial and anti-inflammatory effects. Some flavones are also T2R agonists. We found that several flavones inhibit Muc5AC and inducible NOS up-regulation as well as cytokine release in primary and cultured airway cells in response to several inflammatory stimuli. This occurs at least partly through inhibition of protein kinase C and receptor tyrosine kinase activity. We also demonstrate that sinonasal ciliated epithelial cells express T2R14, which closely co-localizes (<7 nm) with the T2R38 isoform. Heterologously expressed T2R14 responds to multiple flavones. These flavones also activate T2R14-driven calcium signals in primary cells that activate nitric oxide production to increase ciliary beating and mucociliary clearance. TAS2R38 polymorphisms encode functional (PAV: proline, alanine, and valine at positions 49, 262, and 296, respectively) or non-functional (AVI: alanine, valine, isoleucine at positions 49, 262, and 296, respectively) T2R38. Our data demonstrate that T2R14 in sinonasal cilia is a potential therapeutic target for upper respiratory infections and that flavones may have clinical potential as topical therapeutics, particularly in T2R38 AVI/AVI individuals.

Bitterness prediction in-silico: A step towards better drugs

Bitter taste is innately aversive and thought to protect against consuming poisons. Bitter taste receptors (Tas2Rs) are G-protein coupled receptors, expressed both orally and extra-orally and proposed as novel targets for several indications, including asthma. Many clinical drugs elicit bitter taste, suggesting the possibility of drugs re-purposing. On the other hand, the bitter taste of medicine presents a major compliance problem for pediatric drugs. Thus, efficient tools for predicting, measuring and masking bitterness of active pharmaceutical ingredients (APIs) are required by the pharmaceutical industry. Here we highlight the BitterDB database of bitter compounds and survey the main computational approaches to prediction of bitter taste based on compound's chemical structure. Current in silico bitterness prediction methods provide encouraging results, can be constantly improved using growing experimental data, and present a reliable and efficient addition to the APIs development toolbox.