Identifying peptides from oat protein with potential hypocholesterolemic and satiety effects
Bibliographic record
Abstract
Obesity and hypercholesterolemia cause significant public health challenges, demanding alternative dietary strategie. This study investigated the cholesterol-lowering and satiety-enhancing effects of peptides derived from oat protein hydrolysates. Oat protein was hydrolyzed using pepsin and trypsin, and the resulting peptides were fractionated based on hydrophobicity. The fractions were than evaluated for their inhibitory effects on dipeptidyl-peptidase 4 (DPP4) and HMG-CoA reductase (HMGCR), as well as their impact on cholesterol micelle solubility. The most potent fraction (F4) exhibited 50 % inhibition of DPP4 at 50 μg/ml, potentially enhancing satiety. Fractions F3, F4, and hydrolyzed peptides inhibited HMGCR by 85 %, 79 %, and 83 %, respectively, at 200 μg/ml, while F1 reduced cholesterol micelle solubility by 38 % at 2 mg/ml. LC-MS/MS analysis identified peptides enriched in proline, leucine, and aromatic amino acids, contributing to bioactivity. These findings suggested oat protein-derived peptides as promising candidate for functional food development formulations aimed at mitigating obesity and hypercholesterolemia. • In vitro digestion by pepsin and trypsin in simulated gut conditions was effective to generate small peptides from oat protein. • Oat peptides showed capacity to inhibit DPP4 and HMGCR, promoting satiety and lowering cholesterol. • Hydrophobic and aromatic amino acids enhanced enzyme inhibition potential. • LC-MS/MS and de novo sequencing identified key bioactive oat peptides. • Oat protein based functional foods have potential for obesity and cholesterol management.
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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 | 0.001 |
| 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".