Chemoinformatics View on Bitter Taste Receptor Agonists in Food

Bayer, Sebastian; Mayer, Ariane Isabell; Borgonovo, Gigliola; Morini, Gabriella; Pizio, Antonella Di; Bassoli, Angela

doi:10.1021/acs.jafc.1c05057

Cited by 32 publications

(31 citation statements)

References 64 publications

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Section: Discussionmentioning

confidence: 99%

“…Interestingly, in a previous study on the chemical space of natural bitterants, scientists found that the chemical superclasses of compounds are not relevant for the promiscuity and selectivity for TAS2Rs [ 33 ]. Moreover, bitter compounds with a high degree of chemical structural similarity (e.g., caffeine and theobromine) may have different promiscuity profiles [ 33 ]. According to their results, and our results as well, the chemical structural characteristics seem not to be sufficient, at least in some cases, for the complete description of the pharmacological activity of natural compounds (e.g., interaction with various biological targets, which triggers a biological response).…”

Section: Discussionmentioning

confidence: 99%

“…Organoleptic evaluation, used more in the past, has some limitations due to sensorial interindividual and intercultural variability, heterogeneity of protocols, high detection threshold of compounds, etc. [ 33 , 70 , 71 ]. Characterization of tastants based on in vitro assay with taste receptors is a better solution for detection of mild tastants, which are difficult to be identified using only sensory methods [ 33 ].…”

Section: Discussionmentioning

confidence: 99%

See 2 more Smart Citations

Phytochemicals and Inflammation: Is Bitter Better?

Dragoș

Petran

Gradinaru

et al. 2022

Plants

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The taste of a herb influences its use in traditional medicine. A molecular basis for the taste-based patterns ruling the distribution of herbal (ethno) pharmacological activities may not be excluded. This study investigated the potential correlations between the anti-inflammatory activity (AIA) and the phytocompound taste and/or its chemical class. The study relies on information gathered by an extensive literature (articles, books, databases) search and made public as PlantMolecularTasteDB. Out of a total of 1527 phytotastants with reliably documented taste and structure available in PlantMolecularTasteDB, 592 (for each of which at least 40 hits were found on PubMed searches) were included in the statistical analysis. A list of 1836 putative molecular targets of these phytotastants was afterwards generated with SwissTargetPrediction tool. These targets were systematically evaluated for their potential role in inflammation using an international databases search. The correlations between phytochemical taste and AIA, between chemical class and AIA, and between the taste and the number of inflammation related targets were statistically analyzed. Phytochemical taste may be a better predictor of AIA than the chemical class. Bitter phytocompounds have a higher probability of exerting AIA when compared with otherwise phytotastants. Moreover, bitter phytotastants act upon more inflammation related targets than non-bitter tasting compounds.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Phytochemicals and Inflammation: Is Bitter Better?

Dragoș

Petran

Gradinaru

et al. 2022

Plants

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“…Recent representative molecular diversity studies include the analysis of novel libraries, such as compounds applied in the food industry [5,46], peptides [47], focused libraries [48], de novo virtual libraries [49], and commercially available fragments libraries for medicinal chemistry [50]. The results are summarized in Table 1.…”

Section: Molecular Diversitymentioning

confidence: 99%

“…Another recent example of the application of chemical space to SP(A)R analysis lies at the interface of drug discovery and food chemistry [46]. Bayer et al explored the associations between the chemical structures of 133 compounds with known biological activities and extra-oral bitter taste receptors, which belong to the superfamily of G-protein-coupled receptors.…”

Section: Structure-property (Activity) Relationshipsmentioning

confidence: 99%

Approaches for enhancing the analysis of chemical space for drug discovery

Saldívar-González

Medina-Franco

2022

Expert Opinion on Drug Discovery

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Chemical space is a powerful, general, and practical conceptual framework in drug discovery and other areas in chemistry that addresses the diversity of molecules and it has various applications. Moreover, chemical space is a cornerstone of chemoinformatics as a scientific discipline. In response to the increase in the set of chemical compounds in databases, generators of chemical structures, and tools to calculate molecular descriptors, novel approaches to generate visual representations of chemical space in low dimensions are emerging and evolving. Such approaches include a wide range of commercial and free applications, software, and open-source methods. Herein, the current state of chemical space in drug design and discovery is reviewed. The topics discussed herein include advances for efficient navigation in chemical space, the use of this concept in assessing the diversity of different data sets, exploring structure-property/activity relationships for one or multiple endpoints, and compound library design. Recent advances in methodologies for generating visual representations of chemical space have been highlighted, thereby emphasizing open-source methods. It is concluded that quantitative and qualitative generation and analysis of chemical space require novel approaches for handling the increasing number of molecules and their information available in chemical databases (including emerging ultra-large libraries). In addition, it is of utmost importance to note that chemical space is a conceptual framework that goes beyond visual representation in low dimensions. However, the graphical representation of chemical space has several practical applications in drug discovery and beyond.

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Machine learning approaches to predict TAS2R receptors for bitterants

Ferri,

Cannariato,

Deriu

et al. 2024

Biotech & Bioengineering

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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.

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Chemoinformatics View on Bitter Taste Receptor Agonists in Food

Cited by 32 publications

References 64 publications

Phytochemicals and Inflammation: Is Bitter Better?

Phytochemicals and Inflammation: Is Bitter Better?

Approaches for enhancing the analysis of chemical space for drug discovery

Machine learning approaches to predict TAS2R receptors for bitterants

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