Antioxidative Peptides from Proteolytic Hydrolysates of False Abalone (Volutharpa ampullacea perryi): Characterization, Identification, and Molecular Docking
Bibliographic record
Abstract
Antioxidative peptides were produced from false abalone (Volutharpa ampullacea perryi) using enzymatic hydrolysis. Trypsin produced the most bioactive hydrolysates with the highest scavenging ABTS+• free radicals compared to pepsin, alcalase, neutrase, and flavourzyme. The response surface methodology studies on trypsin hydrolysis indicated that the hydrolysis temperature, time, and pH were interacted with each other (p < 0.05), and the optimal conditions were hydrolysis at 51.8 °C for 4.1 h, pH 7.7 and the maximum predicted hydrolysis degree was 13.18% and ABTS+• scavenging activity of 79.42%. The optimized hydrolysate was subjected to ultrafiltration fractionation, and the fraction with MW < 3 kDa showed the highest ABTS+• scavenging activity. There were 193 peptide sequences identified from this peptide fraction and 133 of them were successfully docked onto human myeloperoxidase (MPO), an enzyme involved in forming reactive oxidants in vivo. The highest scored peptide, no. 39, consists of DTETGVPT. Its structure and molecular interactions with MPO active site were compared with previously characterized peptide hLF1-11. The interactions between peptide no. 39 and MPO include electrostatic charge, hydrogen bonds, and covalent bonds. The antioxidative peptide produced in this research may exert antioxidant activity in vivo due to its potential inhibition effect on MPO.
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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 | 0.000 |
| 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) | 0.000 | 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".