Screening, separation and identification of metal-chelating peptides for nutritional, cosmetics and pharmaceutical applications
Bibliographic record
Abstract
Metal-chelating peptides, which form metal-peptide coordination complexes with various metal ions, can be used as biofunctional ingredients notably to enhance human health and prevent diseases. This review aims to discuss recent insights into food-derived metal-chelating peptides, the strategies set up for their discovery, their study, and identification. After understanding the overall properties of metal-chelating peptides, their production from food-derived protein sources and their potential applications will be discussed, particularly in nutritional, cosmetics and pharmaceutical fields. In addition, the review provides an overview of the last decades of progress in discovering food-derived metal-chelating peptides, addressing several screening, separation and identification methodologies. Furthermore, it emphasizes the methods used to assess peptide-metal interaction, allowing for better understanding of chemical and thermodynamic parameters associated with the formation of peptide-metal coordination complexes, as well as the specific amino acid residues that play important roles in the metal ion coordination.
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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 | 0.001 | 0.001 |
| 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".