Chaperone-like food components: from basic concepts to food applications
Bibliographic record
Abstract
The significance of chaperones in preventing protein aggregation including amyloid fibril formation has been extensively documented in the biological field, but there is limited research on the potential effect of chaperone-like molecules on food protein functionality and food quality. This review is intended to extend the potential of chaperone and chaperone-like molecules in the prevention of food protein aggregation and amyloid fibril formation. The common features of chaperone molecules responsible for suppressing aggregation and amyloid fibril formation are firstly presented. Then, the function and applicability of chaperone and chaperone-like molecules in food products, in particular high-protein beverages where aggregation and fibril formation are undesirable, are extensively discussed. Protein aggregation in high-protein beverages can be prevented by chaperone or chaperone-like components, such as caseins, heme-containing proteins, specific peptide fragments, phospholipids and polyphenols. The diversity of chaperone-like components and mechanisms might provide the flexibility in taking advantage of their potential applications in different food products.
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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 | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".