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

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.

New 3-(1H-benzo[d]imidazol-2-yl)quinolin-2(1H)-one-based triazole derivatives: Design, synthesis, and biological evaluation as antiproliferative and apoptosis-inducing agents

A series of 1,2,3-triazole derivatives based on the quinoline-benzimidazole hybrid scaffold was designed, synthesized, and screened against a panel of NCI-60 humanoid cancer cell lines for in vitro cytotoxicity evaluation, which revealed that compound Q6 was the most potent cytotoxic agent with excellent GI₅₀ , TGI, and LC₅₀ values on multiple cancer cell lines. Q6 was tested further on the BT-474 breast cancer line to evaluate the mechanism of action. Preliminary screening studies based on the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl tetrazolium bromide assay revealed that compound Q6 had an excellent antiproliferative effect against human breast cancer cells, BT-474, with IC₅₀ values of 0.59 ± 0.01 μM. The detailed study based on the acridine orange/ethidium bromide staining (AO/EB) and the 4',6-diamidino-2-phenylindole (DAPI) assay suggested that the antiproliferative activity shown was due to the induction of apoptosis on exposure to Q6. Further, DCFDA staining showed the generation of reactive oxygen species, altering the mitochondrial potential and leading to the initiation of apoptosis. This was further supported by JC-1 staining, indicating that this scaffold can contribute to the development of more potent derivatives.

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/.

VirtualTaste: a web server for the prediction of organoleptic properties of chemical compounds

Taste is one of the crucial organoleptic properties involved in the perception of food by humans. Taste of a chemical compound present in food stimulates us to take in food and avoid poisons. Bitter taste of drugs presents compliance problems and early flagging of potential bitterness of a drug candidate may help with its further development. Similarly, the taste of chemicals present in food is important for evaluation of food quality in the industry. In this work, we have implemented machine learning models to predict three different taste endpoints-sweet, bitter and sour. The VirtualTaste models achieved an overall accuracy of 90% and an AUC of 0.98 in 10-fold cross-validation and in an independent test set. The web server takes a two-dimensional chemical structure as input and reports the chemical's taste profile for three tastes-using molecular fingerprints along with confidence scores, including information on similar compounds with known activity from the training set and an overall radar chart. Additionally, insights into 25 bitter receptors are also provided via target prediction for the predicted bitter compounds. VirtualTaste, to the best of our knowledge, is the first freely available web-based platform for the prediction of three different tastes of compounds. It is accessible via http://virtualtaste.charite.de/VirtualTaste/without any login requirements and is free to use.

Bitter Taste and Olfactory Receptors: Beyond Chemical Sensing in the Tongue and the Nose

Abstract

The Up-and-Coming-Scientist section of the current issue of the Journal of Membrane Biology features the invited essay by Dr. Mercedes Alfonso-Prieto, Assistant Professor at the Forschungszentrum Jülich (FZJ), Germany, and the Heinrich-Heine University Düsseldorf, Vogt Institute for Brain Research. Dr. Alfonso-Prieto completed her doctoral degree in chemistry at the Barcelona Science Park, Spain, in 2009, pursued post-doctoral research in computational molecular sciences at Temple University, USA, and then, as a Marie Curie post-doctoral fellow at the University of Barcelona, worked on computations of enzyme reactions and modeling of photoswitchable ligands targeting neuronal receptors. In 2016, she joined the Institute for Advanced Science and the Institute for Computational Biomedicine at the FZJ, where she pursues research on modeling and simulation of chemical senses. The invited essay by Dr. Alfonso-Prieto discusses state-of-the-art modeling of molecular receptors involved in chemical sensing – the senses of taste and smell. These receptors, and computational methods to study them, are the focus of Dr. Alfonso-Prieto’s research. Recently, Dr. Alfonso-Prieto and colleagues have presented a new methodology to predict ligand binding poses for GPCRs, and extensive computations that deciphered the ligand selectivity determinants of bitter taste receptors. These developments inform our current understanding of how taste occurs at the molecular level.

Graphic Abstract

Sweet taste of heavy water

Hydrogen to deuterium isotopic substitution has only a minor effect on physical and chemical properties of water and, as such, is not supposed to influence its neutral taste. Here we conclusively demonstrate that humans are, nevertheless, able to distinguish D₂O from H₂O by taste. Indeed, highly purified heavy water has a distinctly sweeter taste than same-purity normal water and can add to perceived sweetness of sweeteners. In contrast, mice do not prefer D₂O over H₂O, indicating that they are not likely to perceive heavy water as sweet. HEK 293T cells transfected with the TAS1R2/TAS1R3 heterodimer and chimeric G-proteins are activated by D₂O but not by H₂O. Lactisole, which is a known sweetness inhibitor acting via the TAS1R3 monomer of the TAS1R2/TAS1R3, suppresses the sweetness of D₂O in human sensory tests, as well as the calcium release elicited by D₂O in sweet taste receptor-expressing cells. The present multifaceted experimental study, complemented by homology modelling and molecular dynamics simulations, resolves a long-standing controversy about the taste of heavy water, shows that its sweet taste is mediated by the human TAS1R2/TAS1R3 taste receptor, and opens way to future studies of the detailed mechanism of action.

Ben Abu, Mason and colleagues use molecular dynamics, cell-based experiments, mouse models, and human subjects to determine that, unlike ordinary water, heavy water tastes sweet to humans, but not mice. Mechanistically, this effect is mediated by the human TAS1R/TAS1R3 sweet taste receptor.

Sweet taste of heavy water

Hydrogen to deuterium isotopic substitution has only a minor effect on physical and chemical properties of water and, as such, is not supposed to influence its neutral taste. Here we conclusively demonstrate that humans are, nevertheless, able to distinguish D₂O from H₂O by taste. Indeed, highly purified heavy water has a distinctly sweeter taste than same-purity normal water and can add to perceived sweetness of sweeteners. In contrast, mice do not prefer D₂O over H₂O, indicating that they are not likely to perceive heavy water as sweet. HEK 293T cells transfected with the TAS1R2/TAS1R3 heterodimer and chimeric G-proteins are activated by D₂O but not by H₂O. Lactisole, which is a known sweetness inhibitor acting via the TAS1R3 monomer of the TAS1R2/TAS1R3, suppresses the sweetness of D₂O in human sensory tests, as well as the calcium release elicited by D₂O in sweet taste receptor-expressing cells. The present multifaceted experimental study, complemented by homology modelling and molecular dynamics simulations, resolves a long-standing controversy about the taste of heavy water, shows that its sweet taste is mediated by the human TAS1R2/TAS1R3 taste receptor, and opens way to future studies of the detailed mechanism of action.

Bitter taste in silico: A review on virtual ligand screening and characterization methods for TAS2R-bitterant interactions

The growing pharmaceutical interest in the human bitter taste receptors (hTAS2Rs) has two dimensions; i) evaluation of the bitterness of active pharmaceutical compounds, in order to develop strategies for improving patients' adherence to medication, and ii) application of ligands for extra-cellular hTAS2Rs for potential preventive therapeutic achievements. The result is an increasing demand on robust tools for bitterness assessment and screening the receptor-ligand affinity. In silico tools are useful for aiding experimental-screening, as well as to elucide ligand-receptor interactions. In this review, the ligand-based and structure-based approaches are described as the two main in silico tools for bitter taste analysis. The strengths and weaknesses of each approach are discussed. Both approaches provide key tools for understanding and exploiting bitter taste for human health applications.

Computational exploration and anti-mycobacterial activity of potential inhibitors of Mycobacterium tuberculosis acetyl coenzyme A carboxylase as anti-tubercular agents

Acetyl Coenzyme A Carboxylase (AccD6) is a homodimeric protein which is involved in the carboxylation of acetyl coenzyme A to produce malonyl coenzyme A, which plays an important role in the biosynthesis of fatty acid chain. However, studies suggest that AccD6 in combination with AccA3 produces malonyl co-A. Certain herbicides are known to inhibit plant ACC. Among these herbicides, haloxyfop was found to inhibit AccD6 at IC50 of 21.1 ± 1 µM. In this study, we have performed molecular docking of the Maybridge database consisting of ~55,000 compounds in the active site of the protein with haloxyfop as a reference molecule, followed by molecular dynamics study and biological activity determination of prioritized compounds. Out of the nine compounds selected for biological evaluation, three compounds - CD07230, HTS08529 and KM08871 - were found to exhibit anti-mycobacterial activity.

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.

Evolutionary shifts in taste coding in the fruit pest Drosophila suzukii

Although most Drosophila species lay eggs in overripe fruit, the agricultural pest Drosophila suzukii lays eggs in ripe fruit. We found that changes in bitter taste perception have accompanied this adaptation. We show that bitter-sensing mutants of Drosophila melanogaster undergo a shift in egg laying preference toward ripe fruit. D. suzukii has lost 20% of the bitter-sensing sensilla from the labellum, the major taste organ of the head. Physiological responses to various bitter compounds are lost. Responses to strawberry purées are lost from two classes of taste sensilla. Egg laying is not deterred by bitter compounds that deter other species. Profiling of labellar transcriptomes reveals reduced expression of several bitter Gr genes (gustatory receptors). These findings support a model in which bitter compounds in early ripening stages deter egg laying in most Drosophila species, but a loss of bitter response contributes to the adaptation of D. suzukii to ripe fruit.

A new agricultural pest has recently emerged in the United States and Northern Europe. The invasive species is a type of fruit fly that normally lives in Southeast Asia called Drosophila suzukii (also known as the spotted wing Drosophila). This fly poses a threat to fruit crops – including strawberries, blueberries, cherries, peaches and grapes – because, while other fruit flies lay eggs in overripe fruit, D. suzukii lays eggs in ripe fruit, leading to agricultural losses.

This shift in where fruit flies prefer to lay their eggs is related to changes in the senses of smell and touch, and taste could also play a role. Insects have evolved mechanisms that dissuade them from eating or laying eggs in plants with high levels of toxins, which taste bitter. If D. suzukii is less sensitive to bitter tastes than other flies, this could help explain why it lays eggs in just-ripe fruit, since the levels of certain bitter compounds are higher in the early stages of ripening than later on.

To figure out if this is the case, Dweck et al. studied different species of fruit fly. Compared to Drosophila melanogaster (a fruit fly common in America and Europe that is regularly used in scientific studies), D. suzukii had fewer bitter taste receptor neurons on the major taste organ of the fly head. These receptor neurons were also less responsive to a variety of bitter compounds.

Next, Dweck et al. tested whether D. melanogaster and D. suzukii showed different preferences for where to lay their eggs by offering them strawberry purées made from fruit at different ripening stages. In this experiment, D. suzukii preferred to lay its eggs on purées made from unripe or just-ripe strawberries, while D. melanogaster showed a preference for fermented (overripe) purée. Furthermore, when D. melanogaster flies were genetically modified so that they became less sensitive to bitter taste, they preferred to lay their eggs in ripe (rather than overripe) fruit, similar to D. suzukii. These results suggest that taste has a major role in the egg laying preferences of D. suzukii.

Further research is needed to determine which bitter compounds influence egg-laying decisions in each species of fruit fly, and what receptors respond to these compounds. However, Dweck et al.’s results lay the groundwork for new approaches to reducing D. suzukii’s impact on agriculture.

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.

Roasting-induced changes in cocoa beans with respect to the mood pyramid

The mood pyramid of cocoa, which was previously proposed as a new concept, consists of four levels (flavan-3-ols, methylxanthines, minor compounds and orosensory properties). Roasting is a crucial process for flavor development in cocoa but is likely to have a negative impact on the phytochemicals. We investigated the effect of roasting time (10-50 min) and temperature (110-160 °C) on the potential mood-enhancing compounds corresponding to the distinct mood pyramid levels. Phytochemicals were analyzed using UPLC-HRMS, while the flavor was mapped via aroma (HS-SPME-GC-MS) and generic descriptive analysis (trained panel). Results revealed that roasting at 130 °C for 30 min did not significantly affect the levels of epicatechin, procyanidin B2 and theobromine, while salsolinol significantly increased. Moreover, bitterness and astringency were reduced and the desired cocoa flavor was developed. Thus, through selection of appropriate roasting time and temperature conditions phytochemicals of interest could be retained without comprising the flavor.

In silico screening of known small molecules to bind ACE2 specific RBD on Spike glycoprotein of SARS-CoV-2 for repurposing against COVID-19

Background: Human coronavirus (SARS-CoV-2) is causing a pandemic with significant morbidity and mortality. As no effective novel drugs are available currently, drug repurposing is an alternative intervention strategy. Here we present an  in silico drug repurposing study that implements successful concepts of computer-aided drug design (CADD) technology for repurposing known drugs to interfere with viral cellular entry via the spike glycoprotein (SARS-CoV-2-S), which mediates host cell entry via the hACE2 receptor. Methods: A total of 4015 known and approved small molecules were screened for interaction with SARS-CoV-2-S through docking studies and 15 lead molecules were shortlisted. Additionally, streptomycin, ciprofloxacin, and glycyrrhizic acid (GA) were selected based on their reported anti-viral activity, safety, availability and affordability. The 18 molecules were subjected to molecular dynamics (MD) simulation. Results: The MD simulation results indicate that GA of plant origin may be repurposed for SARS-CoV-2 intervention, pending further studies. Conclusions: Repurposing is a beneficial strategy for treating COVID-19 with existing drugs. It is aimed at using docking studies to screen molecules for clinical application and investigating their efficacy in inhibiting SARS-CoV-2-S. SARS-CoV-2-S is a key pathogenic protein that mediates pathogen-host interaction. Hence, the molecules screened for inhibitory properties against SARS-CoV-2-S can be clinically used to treat COVID-19 since the safety profile is already known.

Quantitative prediction of the bitterness of atomoxetine hydrochloride and taste-masked using hydroxypropyl-β-cyclodextrin: A biosensor evaluation and interaction study

The bitterness of a drug is a major challenge for patient acceptability and compliance, especially for children. Due to the toxicity of medication, a human taste panel test has certain limitations. Atomoxetine hydrochloride (HCl), which is used for the treatment of attention deficit/hyperactivity disorder (ADHD), has an extremely bitter taste. The aim of this work is to quantitatively predict the bitterness of atomoxetine HCl by a biosensor system. Based on the mechanism of detection of the electronic tongue (E-tongue), the bitterness of atomoxetine HCl was evaluated, and it was found that its bitterness was similar to that of quinine HCl. The bitterness threshold of atomoxetine HCl was 8.61 µg/ml based on the Change of membrane Potential caused by Adsorption (CPA) value of the BT0 sensor. In this study, the taste-masking efficiency of 2-hydroxypropyl-β-cyclodextrin (HP-β-CyD) was assessed by Euclidean distances on a principle component analysis (PCA) map with the SA402B Taste Sensing System, and the host-guest interactions were investigated by differential scanning calorimetry (DSC), powder X-ray diffraction (XRD), nuclear magnetic resonance (NMR) spectroscopy and scanning electron microscopy (SEM). Biosensor evaluation and characterization of the inclusion complex indicated that atomoxetine HCl could actively react with 2-hydroxypropyl-β-cyclodextrin.

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.

Structure-based screening and validation of bioactive compounds as Zika virus methyltransferase (MTase) inhibitors through first-principle density functional theory, classical molecular simulation and QM/MM affinity estimation

Recent Zika virus (ZIKV) outbreak and association with human diseases such as neurological disorders have raised global health concerns. However, in the absence of an approved anti-ZIKV drug has generated urgency for the drug development against ZIKV infection. Here, structure-based virtual screening of 8589 bioactive compounds, screened at the substrate-binding site of ZIKV nonstructural 5 (NS5)-based structure N-terminal methyltransferase (MTase) domain followed by ADMET (absorption, distribution, metabolism, excretion and toxicity) profiling concluded the four potential lead inhibitors, i.e. (4-acetylamino-benzenesulfonylamino)-acetic acid (F3342-0450), 3-(5-methylfuran-2-yl)-N-(4-sulfamoylphenyl)propanamide (F1736-0142), 8-(2-hydroxy-ethylamino)-1,3-dimethyl-7-(3-methyl-benzyl)-3,7-dihydro-purine-2,6-dione (F0886-0080) and N-[4-(aminosulfonyl)phenyl]-2,3-dihydro-1,4-benzodioxine-2-carboxamide (F0451-2187). Collectively, extra precision docking and Density Functional Theory(DFT) calculations studies identified the F3342-0450 molecule, having strong interactions on the active site of MTase, further supported by molecular dynamics simulation, binding affinity and hybrid QM/MM calculations, suggest a new drug molecule for the antiviral drug development against ZIKV infection. Communicated by Ramaswamy H. Sarma.

Conformational Insight on WT- and Mutated-EGFR Receptor Activation and Inhibition by Epigallocatechin-3-Gallate: Over a Rational Basis for the Design of Selective Non-Small-Cell Lung Anticancer Agents

Non-small cell lung cancer (NSCLC) represents a difficult condition to treat, due to epidermal growth factor receptor (EGFR) kinase domain mutations, which lead to ligand-independent phosphorylation. Deletion of five amino acids (ELREA) in exon 19 and mutational change from leucine to arginine at position 858 (L858R) are responsible for tyrosine kinase domain aberrant activation. These two common types of EGFR-mutated forms are clinically associated with high response with Tyrosine Kinase Inhibitors (TKI); however, the secondary T790M mutation within the Tyrosine Kinase Domain (TKD) determines a resistance to these EGFR-TKIs. Using molecular dynamic simulation (MD), the present study investigated the architectural changes of wild-type and mutants EGFR's kinase domains in order to detect any conformational differences that could be associated with a constitutively activated state and thus to evaluate the differences between the wild-type and its mutated forms. In addition, in order to evaluate to which extent the EGFR mutations affect its inhibition, Epigallocatechin 3-Gallate (EGCG) and Erlotinib (Erl), known EGFR-TKI, were included in our study. Their binding modes with the EGFR-TK domain were elucidated and the binding differences between EGFR wild-type and the mutated forms were evidenced. The aminoacids mutations directly influence the binding affinity of these two inhibitors, resulting in a different efficacy of Erl and EGCG inhibition. In particular, for the T790M/L858R EGFR, the binding modes of studied inhibitors were compromised by aminoacidic substitution confirming the experimental findings. These results may be useful for novel drug design strategies targeting the dimerization domain of the EGFR mutated forms, thus preventing receptor activation.

Innovative Three-Step Microwave-Promoted Synthesis of N-Propargyltetrahydroquinoline and 1,2,3-Triazole Derivatives as a Potential Factor Xa (FXa) Inhibitors: Drug Design, Synthesis, and Biological Evaluation

The coagulation cascade is the process of the conversion of soluble fibrinogen to insoluble fibrin that terminates in production of a clot. Factor Xa (FXa) is a serine protease involved in the blood coagulation cascade. Moreover, FXa plays a vital role in the enzymatic sequence which ends with the thrombus production. Thrombosis is a common causal pathology for three widespread cardiovascular syndromes: acute coronary syndrome (ACS), venous thromboembolism (VTE), and strokes. In this research a series of N-propargyltetrahydroquinoline and 1,2,3-triazole derivatives as a potential factor Xa (FXa) inhibitor were designed, synthesized, and evaluated for their FXa inhibitor activity, cytotoxicity activity and coagulation parameters. Rational design for the desired novel molecules was performed through protein-ligand complexes selection and ligand clustering. The microwave-assisted synthetic strategy of selected compounds was carried out by using Ullmann-Goldberg, N-propargylation, Mannich addition, Friedel-Crafts, and 1,3-dipolar cycloaddition type reactions under microwave irradiation. The microwave methodology proved to be an efficient way to obtain all novel compounds in high yields (73–93%). Furthermore, a thermochemical analysis, optimization and reactivity indexes such as electronic chemical potential (µ), chemical hardness (η), and electrophilicity (ω) were performed to understand the relationship between the structure and the energetic behavior of all the series. Then, in vitro analysis showed that compounds 27, 29–31, and 34 exhibited inhibitory activity against FXa and the corresponding half maximal inhibitory concentration (IC₅₀) values were calculated. Next, a cell viability assay in HEK293 and HepG2 cell lines, and coagulation parameters (anti FXa, Prothrombin time (PT), activated Partial Thromboplastin Time (aPTT)) of the most active novel molecules were performed to determine the corresponding cytotoxicity and possible action on clotting pathways. The obtained results suggest that compounds 27 and 29 inhibited FXa targeting through coagulation factors in the intrinsic and extrinsic pathways. However, compound 34 may target coagulation FXa mainly by the extrinsic and common pathway. Interestingly, the most active compounds in relation to the inhibition activity against FXa and coagulation parameters did not show toxicity at the performed coagulation assay concentrations. Finally, docking studies confirmed the preferential binding mode of N-propargyltetrahydroquinoline and 1,2,3-triazole derivatives inside the active site of FXa.

Discovery of Ganoderma lucidum triterpenoids as potential inhibitors against Dengue virus NS2B-NS3 protease

Dengue virus (DENV) infection causes serious health problems in humans for which no drug is currently available. Recently, DENV NS2B-NS3 protease has been proposed as a primary target for anti-dengue drug discovery due to its important role in new virus particle formation by conducting DENV polyprotein cleavage. Triterpenoids from the medicinal fungus Ganoderma lucidum have been suggested as pharmacologically bioactive compounds and tested as anti-viral agents against various viral pathogens including human immunodeficiency virus. However, no reports are available concerning the anti-viral activity of triterpenoids from Ganoderma lucidum against DENV. Therefore, we employed a virtual screening approach to predict the functional triterpenoids from Ganoderma lucidum as potential inhibitors of DENV NS2B-NS3 protease, followed by an in vitro assay. From in silico analysis of twenty-two triterpenoids of Ganoderma lucidum, four triterpenoids, viz. Ganodermanontriol (−6.291 kcal/mol), Lucidumol A (−5.993 kcal/mol), Ganoderic acid C2 (−5.948 kcal/mol) and Ganosporeric acid A (−5.983 kcal/mol) were predicted to be viral protease inhibitors by comparison to reference inhibitor 1,8-Dihydroxy-4,5-dinitroanthraquinone (−5.377 kcal/mol). These results were further studied for binding affinity and stability using the molecular mechanics/generalized Born surface area method and Molecular Dynamics simulations, respectively. Also, in vitro viral infection inhibition suggested that Ganodermanontriol is a potent bioactive triterpenoid.

Bioelectronic tongue: Current status and perspectives

In the course of evolution, nature has endowed humans with systems for the recognition of a wide range of tastes with a sensitivity and selectivity which are indispensable for the evaluation of edibility and flavour attributes. Inspiration by a biological sense of taste has become a basis for the design of instruments, operation principles and parameters enabling to mimic the unique properties of their biological precursors. In response to the demand for fast, sensitive and selective techniques of flavouring analysis, devices belonging to the group of bioelectronic tongues (B-ETs) have been designed. They combine achievements of chemometric analysis employed for many years in electronic tongues (ETs), with unique properties of bio-inspired materials, such as natural taste receptors (TRs) regarding receptor/ligand affinity. Investigations of the efficiency of the prototype devices create new application possibilities and suggest successful implementation in real applications. With advances in the field of biotechnology, microfluidics and nanotechnologies, many exciting developments have been made in the design of B-ETs in the last five years or so. The presented characteristics of the recent design solutions, application possibilities, critical evaluation of potentialities and limitations as well as the outline of further development prospects related to B-ETs should contribute to the systematisation and expansion of our knowledge.

Plant polyphenols, chemoreception, taste receptors and taste management

PURPOSE OF REVIEW

Polyphenols display beneficial health effects through chemopreventive actions on numerous chronic diseases including cancers, metabolic disorders, reproductive disorders and eating behaviour disorders. According to the principle of chemoreception, polyphenols bind cellular targets capable of accepting their stereochemistry, namely metabolizing enzymes and protein receptors, including taste receptors. The extraoral expression of taste receptors and their pharmacological interest in terms of novel drug therapies open up new perspectives on the potential use of these compounds and their interactions with other chemicals in cells. These new perspectives suggest the need to examine these phytochemicals further. However, most polyphenols have a bitterness property that may disrupt the acceptability of healthy foods or dietary supplements.

RECENT FINDINGS

Polyphenols bind to oral and extraoral bitter type 2 taste receptors, which modulate the signalling pathways involved in anti-inflammatory processes and metabolic and dietary regulations. Depending on their chemical nature, polyphenols may act as activators or inhibitors of taste receptors, and combinations of polyphenols (or herbal mixtures) may be used to modulate the acceptability of bitterness.

SUMMARY

The current review summarizes recent findings on polyphenol chemoreception and highlights the evidence of healthy effects through type 2 taste receptor mediation in signalling pathways, such as new targets, with promising perspectives.

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.

Mechanistic role of plant-based bitter principles and bitterness prediction for natural product studies I: Database and methods

This chapter discusses the rationale behind the bitter sensation elicited by chemical compounds, focusing on natural products. Emphasis has been placed on a brief presentation of BitterDB (the database of bitter compounds), along with available methods for the prediction of bitterness in compounds. The fundamental basis for explaining bitterness has been provided, based on the structural features of human bitter taste receptors and have been used to shed light on the mechanistic role of a few out of the 25 known human taste receptors to provide the foundation for understanding how bitter compounds interact with their receptors. Some case studies of ligand-based prediction models based on 2D fingerprints and 3D pharmacophores, along with machine learning methods have been provided. The chapter closes with an attempt to establish the relationship between bitterness and toxicity.

Bitter tastants and artificial sweeteners activate a subset of epithelial cells in acute tissue slices of the rat trachea

Bitter and sweet receptors (T2Rs and T1Rs) are expressed in many extra-oral tissues including upper and lower airways. To investigate if bitter tastants and artificial sweeteners could activate physiological responses in tracheal epithelial cells we performed confocal Ca²⁺ imaging recordings on acute tracheal slices. We stimulated the cells with denatonium benzoate, a T2R agonist, and with the artificial sweeteners sucralose, saccharin and acesulfame-K. To test cell viability we measured responses to ATP. We found that 39% of the epithelial cells responding to ATP also responded to bitter stimulation with denatonium benzoate. Moreover, artificial sweeteners activated different percentages of the cells, ranging from 5% for sucralose to 26% for saccharin, and 27% for acesulfame-K. By using carbenoxolone, a gap junction blocker, we excluded that responses were mainly mediated by Ca²⁺ waves through cell-to-cell junctions. Pharmacological experiments showed that both denatonium and artificial sweeteners induced a PLC-mediated release of Ca²⁺ from internal stores. In addition, bitter tastants and artificial sweeteners activated a partially overlapping subpopulation of tracheal epithelial cells. Our results provide new evidence that a subset of ATP-responsive tracheal epithelial cells from rat are activated by both bitter tastants and artificial sweeteners.

Evaluation of caffeine as inhibitor against collagenase, elastase and tyrosinase using in silico and in vitro approach

Skin ageing results from enhanced activation of intracellular enzymes such as collagenases, elastases and tyrosinase, stimulated by intrinsic ageing and photoageing factors. Recently, caffeine-based cosmetics are introduced that demonstrates to slow down skin photoageing process. However, no attempts have been done so for to understand caffeine functional inhibitory activity against photoageing related enzymes. Hence, this study established the caffeine molecular interaction and inhibition activity profiles against respective enzymes using in silico and in vitro methods, respectively. Results from in silico study indicates that caffeine has comparatively good affinity with collagenase (−4.6 kcal/mol), elastase (−3.36 kcal/mol) and tyrosinase (−2.86 kcal/mol) and formed the stable protein-ligand complex as validated by molecular dynamics simulation (protein-ligand contacts, RMSD, RMSF and secondary structure changes analysis). Moreover, in vitro data showed that caffeine (1000 µg/mL) has statistically significant maximum inhibition activity of 41.86, 36.44 and 13.72% for collagenase, elastase and tyrosinase, respectively.

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.

Applications of machine learning in GPCR bioactive ligand discovery

GPCRs constitute the largest druggable family having targets for 475 Food and Drug Administration (FDA) approved drugs. As GPCRs are of great interest to pharmaceutical industry, enormous efforts are being expended to find relevant and potent GPCR ligands as lead compounds. There are tens of millions of compounds present in different chemical databases. In order to scan this immense chemical space, computational methods, especially machine learning (ML) methods, are essential components of GPCR drug discovery pipelines. ML approaches have applications in both ligand-based and structure-based virtual screening. We present here a cheminformatics overview of ML applications to different stages of GPCR drug discovery. Focusing on olfactory receptors, which are the largest family of GPCRs, a case study for predicting agonists for an ectopic olfactory receptor, OR1G1, compares four classical ML methods.

The human bitter taste receptor TAS2R7 facilitates the detection of bitter salts

The human sense of taste is devoted to the analysis of the chemical composition of food prior to ingestion. Among the five basic taste qualities bitter taste perception is believed to avoid ingestion of potentially toxic substances. The receptors facilitating the detection of hundreds of chemically different bitter compounds belong to the taste 2 receptor (TAS2R) family, which are part of the G protein-coupled superfamily. Although the chemical classes of bitter compounds that have been identified as agonists of one of the 25 potentially functional human bitter taste receptors cover an enormous chemical space, one distinct group of bitter compounds, the bitter salts have not been assigned to any bitter taste receptor. To close this gap, we screened the entire human bitter taste receptor repertoire by functional calcium mobilization assays with the most famous bitter salt, magnesium sulfate, also known as Epsom salt. Although the profound pharmacological activity and the bitter taste of spring water containing magnesium sulfate has been known since 1697, the molecular basis for its taste has not been elucidated until now. Our screening resulted in the identification of a single receptor, the TAS2R7, responding to magnesium sulfate at concentrations humans perceive this salt as bitter. Subsequently, TAS2R7 was stimulated with other salts and it was found that this receptor also responds to manganese²⁺ and iron²⁺ ions, but not to potassium ions. Magnesium sulfate is known to exert a number of beneficial effects on the human body and thus, has been used as medicine against premature uterine contractions, as anti-arrhythmic drug and as laxative, however, magnesium sulfate overdosage can result in cardiac arrest and thus have fatal consequences. Therefore, it appears reasonable that nature placed TAS2R7 as sentinel for high concentrations of bitter salts on our tongues.

Beyond the Flavour: The Potential Druggability of Chemosensory G Protein-Coupled Receptors

G protein-coupled receptors (GPCRs) belong to the largest class of drug targets. Approximately half of the members of the human GPCR superfamily are chemosensory receptors, including odorant receptors (ORs), trace amine-associated receptors (TAARs), bitter taste receptors (TAS2Rs), sweet and umami taste receptors (TAS1Rs). Interestingly, these chemosensory GPCRs (csGPCRs) are expressed in several tissues of the body where they are supposed to play a role in biological functions other than chemosensation. Despite their abundance and physiological/pathological relevance, the druggability of csGPCRs has been suggested but not fully characterized. Here, we aim to explore the potential of targeting csGPCRs to treat diseases by reviewing the current knowledge of csGPCRs expressed throughout the body and by analysing the chemical space and the drug-likeness of flavour molecules.

Exploration of N5-CAIR Mutase Novel Inhibitors from Pyrococcus horikoshii OT3: A Computational Study

In bacterial and archaeal purine biosynthetic pathways, sixth step involves utilization of enzyme PurE, catalyzing the translation of aminoimidazole ribonucleotide to 4-carboxy-5-aminoimidazole ribonucleotide (CAIR) with carbon dioxide. The formation of CAIR takes place through an unstable intermediate N5-CAIR, played by two enzymes-N5-CAIR synthetase (PurK) and N5-CAIR mutase (PurE) that further catalyzes the reaction of N5-CAIR to CAIR. In this study, N5-CAIR mutase (PH0320) from Pyrococcus horikoshii OT3 (PurE) was considered. The three-dimensional structure of Pyrococcus horikoshii OT3 was modeled based on the structure of PurE from Escherichia coli. The modeled structure was subjected to molecular dynamics simulation up to 100 ns, and least energy structure from the simulation was subjected to virtual screening and induced fit docking to identify the best potent leads. A total of five best antagonists were identified based on their affinity and mode of binding leading with conserved residues Ser18, Ser20, Asp21, Ser45, Ala46, His47, Arg48, Ala72, Gly73, Ala75, and His77 promotes the activity of Ph-N5-CAIR mutase. In addition to molecular dynamics, absorption, digestion, metabolism, and excretion properties, binding free energy and density functional theory calculations of compounds were carried out. Based on analyses, compound from National Cancer Institute (NCI) database, NCI_826 was adjudged as the best potent lead molecule and could be suggested as the suitable inhibitor of N5-CAIR mutase.

A bioinspired in vitro bioelectronic tongue with human T2R38 receptor for high-specificity detection of N-C=S-containing compounds

Detection and identification of bitter compounds draw great attention in pharmaceutical and food industry. Several well-known agonists of specific bitter taste receptors have been found to exhibit anti-cancer effects. For example, N-C=S-containing compounds, such as allyl-isothiocyanates, have shown cancer chemo-preventive effects. It is worth noting that human T2R38 receptor is specific for compounds containing N-C=S moiety. Here, a bioinspired cell-based bioelctronic tongue (BioET) is developed for the high-specificity isothiocyanate-induced bitter detection, utilizing human Caco-2 cells as a primary sensing element and interdigitated impedance sensor as a secondary transducer. As an intestinal carcinoma cell line, Caco-2 endogenously expresses human bitter receptor T2R38, and the activation of T2R38 induces the changes of cellular morphology which can be detected by electric cell-substrate impedance sensing (ECIS). After configuration and optimization of parameters including timing of compound administration and cell density, quantitative bitter evaluation models were built for two well-known bitter compounds, phenylthiocarbamide (PTC) and propylthiouracil (PROP). The bitter specific detection of this BioET is inhibited by probenecid and U-73122, and is not elicited by other taste modalities or bitter ligands that do not activate T2R38. Moreover, by combining different computational tools, we designed a ligand-based virtual screening (LBVS) protocol to select ligands that are likely to activate T2R38 receptor. Three computationally predicted agonists of T2R38 were selected using the LBVS protocol, and the BioET presented response to the predicted agonists, validating the capability of the LBVS protocol. This study suggests this unique cell-based BioET paves a general and promising way to specifically detect N-C=S-containing compounds that can be used for pharmaceutical study and drug development.

Identification and comparison of oligopeptides during withering process of White tea by ultra-high pressure liquid chromatography coupled with quadrupole-orbitrap ultra-high resolution mass spectrometry

Peptides could have specific tastes or bioactivities depending on the length and sequence of amino acids. Till date it remains unknown what peptides are formed during the white tea manufacturing process and whether they contribute to the flavor or bio-activities of white tea. As a first step to address these questions, we applied ultra-high pressure liquid chromatography coupled with quadrupole-orbitrap ultra-high resolution mass spectrometry (UPLC-Quadrupole-Orbitrap-UHRMS) to monitor peptides dynamic changes during the withering process. A total of 196 abundant peptides were identified. Most of them were oligopeptides within a molecular weight of 1000 Da. Four of them were randomly selected, synthesized peptides were applied for further confirmation and quantification. Sequence analysis suggested that some of them were potential taste contributors. Proteinase cleave site analysis identified two separate periods of active proteins degradation at 0-12 h and 30-42 h of the withering processes. Further analysis of cleavage sites also suggested that protein degradation during withering steps were random rather than a stepwise reaction.

Looking at Flavonoid Biodiversity in Horticultural Crops: A Colored Mine with Nutritional Benefits

Flavonoids represent a wide group of plant secondary metabolites implicated in many physiological roles, from the attraction of pollinators to the protection against biotic or abiotic stresses. Flavonoids are synthetized in a number of horticultural crops that are important components of our daily diet. In the last decades, the consumption of vegetables rich in antioxidants has been strongly promoted from the perspective of prevention/protection against chronic diseases. Therefore, due to their nutritional importance, several attempts have been made to enhance flavonoid levels in species of agronomic interest. In this review, we focus on the flavonoid biodiversity among the major horticultural species, which is responsible of differences among closely related species and influences the qualitative/quantitative composition. We also review the role of flavonoids in the nutritional quality of plant products, contributing to their organoleptic and nutritional properties, and the main strategies of biofortification to increase their content.

Bitterless guaifenesin prodrugs-design, synthesis, characterization, in vitro kinetics, and bitterness studies

A respected number of drugs suffer from bitter taste which results in patient incompliance. With the aim of solving the bitterness of guaifenesin, dimethyl maleate, maleate, glutarate, succinate, and dimethyl succinate prodrugs were designed and synthesized. Molecular orbital methods were utilized for the design of the ester prodrugs. The density functional theory (DFT) calculations revealed that the hydrolysis efficiency of the synthesized prodrugs is significantly sensitive to the pattern of substitution on C=C bond and distance between the nucleophile and the electrophile. The hydrolysis of the prodrugs was largely affected by the pH of the medium. The experimental t_1/2 for the hydrolysis of guaifenesin dimaleate ester prodrugs in 1N HCl was the least and for guaifenesin dimethyl succinate was the highest. Functional heterologous expression of TAS2R14, a broadly tuned bitter taste receptor responding to guaifenesin, and experiments using these prodrugs revealed that, while some of the prodrugs still activated the receptor similarly or even stronger than the parent substance, succinate derivatization resulted in the complete loss of receptor responses. The predicted binding modes of guaifenesin and its prodrugs to the TAS2R14 homology model suggest that the decreased activity of the succinate derivatives may be caused by a clash with Phe247.

Effects of isomaltulose on insulin resistance and metabolites in patients with non‑alcoholic fatty liver disease: A metabolomic analysis

Insulin resistance is associated with a poor prognosis in non-alcoholic fatty liver disease (NAFLD) patients. Isomaltulose, a naturally-occurring disaccharide, is reported to improve glucose and lipid metabolisms in obese patients. The present study aimed to investigate the effects of isomaltulose on insulin resistance and various metabolites in NAFLD patients. Five male patients with NAFLD consumed 20 g isomaltulose or sucrose (control). Changes in insulin resistance and metabolites were evaluated by alterations of serum C-peptide immunoreactivity (CPR) and metabolomic analysis from baseline to 15 min after the administration, respectively. There was no significant difference in changes of blood glucose level; however, the CPR level was significantly decreased in the Isomaltulose group compared to the control group (0.94±0.89 vs. −0.12±0.31, P=0.0216). In a metabolomic analysis, a significant alteration was seen in 52 metabolites between the control and Isomaltulose groups. In particular, the taurodeoxycholic acid level significantly increased approximately 12.5-fold, and the arachidonic acid level significantly decreased approximately 0.01-fold. Together, it present study demonstrated that isomaltulose improved insulin resistance in NAFLD patients. It was also revealed that isomaltulose affects taurodeoxycholic acid and arachidonic acid. Thus, isomaltulose may have a beneficial effect on insulin resistance through alterations of bile acid and fatty acid metabolisms in NAFLD patients.

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.

Independent Evolution of Strychnine Recognition by Bitter Taste Receptor Subtypes

The 25 human bitter taste receptors (hT2Rs) recognize thousands of structurally and chemically diverse bitter substances. The binding modes of human bitter taste receptors hT2R10 and hT2R46, which are responsible for strychnine recognition, were previously established using site-directed mutagenesis, functional assays, and molecular modeling. Here we construct a phylogenetic tree and reconstruct ancestral sequences of the T2R10 and T2R46 clades. We next analyze the binding sites in view of experimental data to predict their ability to recognize strychnine. This analysis suggests that the common ancestor of hT2R10 and hT2R46 is unlikely to bind strychnine in the same mode as either of its two descendants. Estimation of relative divergence times shows that hT2R10 evolved earlier than hT2R46. Strychnine recognition was likely acquired first by the earliest common ancestor of the T2R10 clade before the separation of primates from other mammals, and was highly conserved within the clade. It was probably independently acquired by the common ancestor of T2R43-47 before the homo-ape speciation, lost in most T2Rs within this clade, but enhanced in the hT2R46 after humans diverged from the rest of primates. Our findings suggest hypothetical strychnine T2R receptors in several species, and serve as an experimental guide for further study. Improved understanding of how bitter taste receptors acquire the ability to be activated by particular ligands is valuable for the development of sensors for bitterness and for potential toxicity.

Molecular Features Underlying Selectivity in Chicken Bitter Taste Receptors

Chickens sense the bitter taste of structurally different molecules with merely three bitter taste receptors (Gallus gallus taste 2 receptors, ggTas2rs), representing a minimal case of bitter perception. Some bitter compounds like quinine, diphenidol and chlorpheniramine, activate all three ggTas2rs, while others selectively activate one or two of the receptors. We focus on bitter compounds with different selectivity profiles toward the three receptors, to shed light on the molecular recognition complexity in bitter taste. Using homology modeling and induced-fit docking simulations, we investigated the binding modes of ggTas2r agonists. Interestingly, promiscuous compounds are predicted to establish polar interactions with position 6.51 and hydrophobic interactions with positions 3.32 and 5.42 in all ggTas2rs; whereas certain residues are responsible for receptor selectivity. Lys^3.29 and Asn^3.36 are suggested as ggTas2r1-specificity-conferring residues; Gln^6.55 as ggTas2r2-specificity-conferring residue; Ser^5.38 and Gln^7.42 as ggTas2r7-specificity conferring residues. The selectivity profile of quinine analogs, quinidine, epiquinidine and ethylhydrocupreine, was then characterized by combining calcium-imaging experiments and in silico approaches. ggTas2r models were used to virtually screen BitterDB compounds. ~50% of compounds known to be bitter to human are likely to be bitter to chicken, with 25, 20, 37% predicted to be ggTas2r1, ggTas2r2, ggTas2r7 agonists, respectively. Predicted ggTas2rs agonists can be tested with in vitro and in vivo experiments, contributing to our understanding of bitter taste in chicken and, consequently, to the improvement of chicken feed.

Biomimetic Sensors for the Senses: Towards Better Understanding of Taste and Odor Sensation

Taste and smell are very important chemical senses that provide indispensable information on food quality, potential mates and potential danger. In recent decades, much progress has been achieved regarding the underlying molecular and cellular mechanisms of taste and odor senses. Recently, biosensors have been developed for detecting odorants and tastants as well as for studying ligand-receptor interactions. This review summarizes the currently available biosensing approaches, which can be classified into two main categories: in vitro and in vivo approaches. The former is based on utilizing biological components such as taste and olfactory tissues, cells and receptors, as sensitive elements. The latter is dependent on signals recorded from animals' signaling pathways using implanted microelectrodes into living animals. Advantages and disadvantages of these two approaches, as well as differences in terms of sensing principles and applications are highlighted. The main current challenges, future trends and prospects of research in biomimetic taste and odor sensors are discussed.

The taste of toxicity: A quantitative analysis of bitter and toxic molecules

The role of bitter taste-one of the few basic taste modalities-is commonly assumed to signal toxicity and alert animals against consuming harmful compounds. However, it is known that some toxic compounds are not bitter and that many bitter compounds have negligible toxicity while having important health benefits. Here we apply a quantitative analysis of the chemical space to shed light on the bitterness-toxicity relationship. Using the BitterDB dataset of bitter molecules, The BitterPredict prediction tool, and datasets of toxic compounds, we quantify the identity and similarity between bitter and toxic compounds. About 60% of the bitter compounds have documented toxicity and only 56% of the toxic compounds are known or predicted to be bitter. The LD₅₀ value distributions suggest that most of the bitter compounds are not very toxic, but there is a somewhat higher chance of toxicity for known bitter compounds compared to known nonbitter ones. Flavonoids and alpha acids are more common in the bitter dataset compared with the toxic dataset. In contrast, alkaloids are more common in the toxic datasets compared to the bitter dataset. Interestingly, no trend linking LD₅₀ values with the number of activated bitter taste receptors (TAS2Rs) subtypes is apparent in the currently available data. This is in accord with the newly discovered expression of TAS2Rs in several extra-oral tissues, in which they might be activated by yet unknown endogenous ligands and play non-gustatory physiological roles. These results suggest that bitter taste is not a very reliable marker for toxicity, and is likely to have other physiological roles. © 2017 IUBMB Life, 69(12):938-946, 2017.