Bioactive Properties of Enzymatic Gelatin Hydrolysates Based on In Silico, In Vitro, and In Vivo Studies
Bibliographic record
Abstract
This current study aims to analyze the potential bioactivities possessed by the enzymatic hydrolysates of commercial bovine, porcine, and tilapia gelatins using bioinformatics in combination with in vitro and in vivo studies. The hydrolysate with superior inhibition of angiotensin converting enzyme (ACE) activity was used to treat the D-galactose (DG)-induced amnesic mice. In silico digestion of the gelatins led to the identification of peptide sequences with potential antioxidant, ACE-inhibitory, and anti-amnestic properties. The results of in vitro digestion revealed that the <1 kDa peptide fraction of porcine gelatin hydrolysate obtained after 1 h digestion with papain (PP) (PP1, <1 kDa) potently inhibited ACE, acetylcholinesterase, and prolyl endopeptidase activities at 87.42%, 21.24%, and 48.07%, respectively. Administering the PP1 to DG-induced amnesic mice ameliorated the spatial cognitive impairment and Morris water maze learning abilities. The dentate area morphology in the PP1-treated mice was relatively similar to the control group. In addition, PP1 enhanced the antioxidant capacity in the DG-induced amnesic mice. This study suggests that PP1 could serve as a potential treatment tool against oxidative stress, hypertension, and neurodegenerative diseases.
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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 | 0.000 |
| 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) | 0.001 | 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".