TAS2R Taste Receptor Clustering Suggests that Bitter Wine Taste Perception Forms a 2D Dataspace.
Published In: Journal of Computational Biophysics & Chemistry, 2025, v. 24, n. 6. P. 795 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Shityakov, Sergey; Skorb, Ekaterina V.; Nosonovsky, Michael 3 of 3
Abstract
In this paper, we investigated the in silico molecular docking of 10 ligands relevant to wine tannins with 27 bitter taste receptors, TAS2Rs. A 3D superimposition analysis using TAS2R38 as the reference revealed that the majority of the remaining TAS2R proteins deviated from the reference structure within the 2–3 Å range, indicating moderate conformational shifts among them. We identified the protein-ligand binding site at the extracellular part and studied docking with the ligands associated with bitterness in wine tannins. Rigid–flexible molecular docking revealed protein-ligand binding affinities below the standard energy threshold (Δ G < −6.0 kcal/mol) for catechin, epicatechin, and flavan-3-ol, with a standard deviation of ± 0.5 kcal/mol or less. These three ligands are relevant to the bitter taste of wine. Principal component analysis was used to determine the optimal mean number of clusters (n = 3). As a result, the TAS2R proteins are categorized into three distinct clusters on the basis of their ability to bind bitter ligands. This suggests that bitter taste forms a 2D space. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:Journal of Computational Biophysics & Chemistry. 2025/08, Vol. 24, Issue 6, p795
- Document Type:Article
- Subject Area:Anatomy and Physiology
- Publication Date:2025
- ISSN:2737-4165
- DOI:10.1142/S2737416524500844
- Accession Number:184999803
- Copyright Statement:Copyright of Journal of Computational Biophysics & Chemistry is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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