Sweet Taste Receptors and Associated Sweet Peptides: Insights into Structure and Function
Bibliographic record
Abstract
Long-term consumption of a high-sugar diet may contribute to the pathogenesis of several chronic diseases, such as obesity and type 2 diabetes. Sweet peptides derived from a wide range of food sources can enhance sweet taste without compromising the sensory properties. Therefore, the research and application of sweet peptides are promising strategies for reducing sugar consumption. This work first outlined the necessity for global sugar reduction, followed by the introduction of sweet taste receptors and their associated transduction mechanisms. Subsequently, recent research progress in sweet peptides from different protein sources was summarized. Furthermore, the main methods for the preparation and evaluation of sweet peptides were presented. In addition, the current challenges and potential applications are also discussed. Sweet peptides can stimulate sweetness perception by binding sweet taste receptors T1R2 and T1R3 in taste buds, which is an effective strategy for reducing sugar consumption. At present, sweet peptides are mainly prepared artificially by synthesis, hydrolysis, microbial fermentation, and bioengineering strategies. Furthermore, sensory evaluation, electronic tongues, and cell models have been used to assess the sweet taste intensity. The present review can provide a theoretical reference for reducing sugar consumption with the aid of sweet peptides in the food industry.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".