Novel gurmarin-like peptides from <i>Gymnema sylvestre</i> and their interactions with the sweet taste receptor T1R2/T1R3
Bibliographic record
Abstract
Gymnema sylvestre (GS) is a traditional medicinal plant known for its hypoglycemic and hypolipidemic effects. Gurmarin (hereafter Gur-1) is the only known active peptide in GS. Gur-1 has a suppressive sweet taste effect in rodents but no or only a very weak effect in humans. Here, 8 gurmarin-like peptides (Gur-2 to Gur-9) and their isoforms are reported in the GS transcriptome. The molecular mechanism of sweet taste suppression by Gur-1 is still largely unknown. Therefore, the complete architecture of human and mouse sweet taste receptors T1R2/T1R3 and their interaction with Gur-1 to Gur-9 were predicted by AlphaFold-Multimer (AF-M) and validated. Only Gur-1 and Gur-2 interact with the T1R2/T1R3 receptor. Indeed, Gur-1 and Gur-2 bind to the region of the cysteine-rich domain (CRD) and the transmembrane domain (TMD) of the mouse T1R2 subunit. In contrast, only Gur-2 binds to the TMD of the human T1R2 subunit. This result suggests that Gur-2 may have a suppressive sweet taste effect in humans. Furthermore, AF-M predicted that Gα-gustducin, a protein involved in sweet taste transduction, interacts with the intracellular domain of the T1R2 subunit. These results highlight an unexpected diversity of gurmarin-like peptides in GS and provide the complete predicted architecture of the human and mouse sweet taste receptor with the putative binding sites of Gur-1, Gur-2, and Gα-gustducin. In addition, gurmarin-like peptides may serve as promising drug scaffolds for the development of antidiabetic molecules.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".