Decoding the Taste of Peptides: Structure, Interactions With Taste Receptors, Bioactivities, and Applications
Bibliographic record
Abstract
ABSTRACT Taste‐active peptides (TAPs) impart significant organoleptic attributes of food products. Their diverse taste profiles, such as umami, bitter, sweet, salty, sour, and kokumi, hold great potential for various food and nutraceutical applications. The perception of taste is influenced by TAPs' interactions with taste receptors, which are governed by their structural features, including amino acid composition, sequence, charge, molecular weight, and bond configurations. Beyond taste, many TAPs exhibit bioactive properties with potential health benefits that may find applications for mitigating chronic diseases, such as diabetes, cardiovascular disease, inflammation, obesity, and cancer. Understanding these characteristics enables the development of peptides with tailored tastes and functional benefits. This review aimed to provide a comprehensive understanding of the structural determinants of TAPs, their receptor interactions, and their dual role in taste and health. Furthermore, it highlights advancements in computational modeling and machine learning for TAP identification and characterization. Future advancements on TAPs are expected to significantly influence how the food industry addresses both flavor and health aspects.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".