Identification of potential antioxidant peptides from protein hydrolysates of pearl oil apricot almonds: Combination of in vitro and molecular docking studies
Bibliographic record
Abstract
Abstract Pearl oil apricot almonds are rich in proteins, being a valuable raw material for preparing antioxidant peptides. In this study, almond protein hydrolysates (APHs) were obtained by enzymatic hydrolysis and divided into APHs‐1 (<3 kDa), APHs‐2 (3–10 kDa), and APHs‐3 (>10 kDa). APHs‐1‐c, purified from APHs‐1, was proved to show the strongest antioxidant effect through activity tracking experiments. APHs‐1‐c also can protect HepG2 cells against oxidative damage by reducing intracellular reactive oxygen species and MDA levels, elevating glutathione levels as well as increasing superoxide dismutase and catalase activities, and its protective effect is related to the Keap1–Nrf2 signaling pathway. Forty bioactive peptide sequences were identified from APHs‐1‐c, and Thr‐Glu‐Asp‐Asp‐Trp‐Arg‐Trp‐His (TEDDWRWH; P1), Trp‐Tyr‐Asp‐Asn‐Glu‐Trp‐Gly‐Tyr‐Arg (WYDNEWGYR; P2), and Ala‐Glu‐Asp‐His‐Glu‐Trp‐Trp‐Arg (AEDHEWWWR; P3) had the most potential for antioxidant activity by molecular docking studies. P1, P2, and P3 showed desirable 1,1‐diphenyl‐2‐picrylhydrazyl radical scavenging activity (IC 50 = 0.634–3.381 mmol/L), 2,2′‐azino‐bis (3‐ethylbenzothiazoline‐6‐sulfonic acid) diammonium salt radical scavenging activity (IC 50 = 0.221–0.309 mmol/L), and ferric‐reducing antioxidant power. These results suggested that almonds protein hydrolysates and its derived antioxidant peptides could serve as potential ingredients applied in functional foods.
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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) | 0.000 | 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".