BitterX: a tool for understanding bitter taste in humans

Huang, Wenkang; Shen, Qiancheng; Su, Xubo; Ji, Mingfei; Liu, Xinyi; Chen, Yingyi; Lu, Shaoyong; Zhuang, Huisheng; Zhang, Jian

doi:10.1038/srep23450

Cited by 53 publications

(64 citation statements)

References 28 publications

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“…We hypothesized that flavones may activate one or more of these T2Rs located in the upper respiratory epithelium (22,25). The molecular structures of flavones were examined using BitterX, an open-access platform for predicting whether a molecule is bitter as well as predicting which T2R receptors are likely to respond (100). BitterX identified T2R14 and T2R39 as having the highest probabilities of being activated by all flavone molecules examined here (supplemental Table 1).…”

Section: Flavones Activate T2r14 Expressed In Sinonasal Epithelial Cementioning

confidence: 99%

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

Hariri¹,

McMahon²,

Chen³

et al. 2017

Journal of Biological Chemistry

106

176

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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. polymorphisms encode functional (PAV:roline, lanine, andaline at positions 49, 262, and 296, respectively) or non-functional (AVI: lanine,aline, soleucine 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.

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Section: Flavones Activate T2r14 Expressed In Sinonasal Epithelial Cementioning

confidence: 99%

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

Hariri¹,

McMahon²,

Chen³

et al. 2017

Journal of Biological Chemistry

106

176

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“…However, inconsistencies in the curation process due to the inclusion of molecules with unverified taste information and incomplete representation of chemical space can lead to incorrect inferences and predictions. BitterPredict 19 and BitterX 13 have used unverified non-bitter molecules as a significant part of their training sets (55.6% and 50% respectively), which potentially adds noise to their models. While e-Bitter 21 , Rojas et al 16 and BitterSweetForest 22 mitigated this problem by utilizing only experimentally verified data, this significantly reduced the size of their datasets and might have led to insufficient representation of the bitter-sweet chemical space.…”

Section: Data Compilation and Curationmentioning

confidence: 99%

“…In one of the pioneering studies for bitter-taste prediction, Rodgers et al 12 used a proprietary dataset of bitter molecules and randomly selected molecules (expected to be non-bitter), to develop a Naïve Bayes classifier utilizing circular fingerprints as molecular descriptors. Similarly, BitterX 13 used random molecules to form their negative set while utilizing (publicly available) BitterDB 14 compounds to form their positive set. As opposed to the use of 2D fingerprints, BitterX used physicochemical features of molecules (from Handbook of Molecular Descriptors 15 ) towards the training of Support Vector Machine (SVM) classifier.…”

Section: Introductionmentioning

confidence: 99%

“…Rojas et al 16 instead tackled the problem of sweet-taste prediction using experimentally verified data from the literature, 2D molecular descriptors and ECFPs (Extended Connectivity Fingerprints) from Dragon 17 towards the development of a QSTR-based expert system 16,18 . BitterPredict 19 enhanced the training data of BitterX 13 , by including molecules curated by Rojas et al 18 and those identified as being 'probably non-bitter' from Fenaroli's Handbook of Flavor Ingredients 20 , while excluding the random molecules. They further established rigorous external validation sets (curated from literature and other unpublished resources), to show the efficacy of their AdaBoost model based on physicochemical and ADMET properties from Canvas.…”

Section: Introductionmentioning

confidence: 99%

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

Tuwani

Wadhwa

Bagler

2018

Preprint

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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, based on structured data compilation our study provides an integrative framework with 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 also present an end-to-end software for bitter-sweet taste prediction based on freely available chemical descriptors.

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“…We have previously developed BitterPredict (37), which classifies molecules into bitter or non-bitter with over 80% accuracy. Several other machine learning predictors followed suit (38,39). In addition, structure-based methods were developed for identification of new agonists for specific bitter taste receptors (12,40,41).…”

Section: Introductionmentioning

confidence: 99%

Intense bitterness of molecules: machine learning for expediting drug discovery

Margulis

Dagan-Wiener

Ives

et al. 2020

Preprint

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Author ContributionsMYN conceived the project, MYN, EM and ADW designed the computational analysis, EM wrote the codes, trained the model and performed all computational analyses, ADW provided data on compounds for the positive and negatives sets and data for toxicity and in-vitro activities, KS provided data and critical discussions, RI and SJ obtained the in-vivo data, EM and MYN have written the manuscript, all authors have read and approved the manuscript. AbstractDrug 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. BitterIntense suggests that intense bitterness does not correlate with toxicity and hepatotoxicity of drugs and that the prevalence of very bitter compounds among drugs is lower than among microbial compounds. BitterIntense allows quick and easy prediction of strong bitterness of compounds of interest for food and pharma industries. We estimate that implementation of BitterIntense or similar tools early in drug discovery and development process may lead to reduction in delays, in animal use and in overall financial burden. Significance StatementDrug development integrates increasingly sophisticated technologies, but extreme bitterness of drugs remains a poorly addressed cause of medicine regimen incompletion. Reformulating the drug can result in delays in the development of a potential medicine, increasing the lead time to the patients. It might also require the use of extra animals in toxicity trials and lead to increased costs for pharma companies. We have developed a computational predictor for intense bitterness, that

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BitterX: a tool for understanding bitter taste in humans

Cited by 53 publications

References 28 publications

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

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

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

Intense bitterness of molecules: machine learning for expediting drug discovery

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