Neuroprotective Effect of β-secretase Inhibitory Peptide from Pacific Hake (Merluccius productus) Fish Protein Hydrolysate
Bibliographic record
Abstract
BACKGROUND: Various methodologies have been employed for the therapeutic interpolation of the progressive brain disorder Alzheimer's disease. Thus, β-secretase inhibition is significant to prevent disease progression in the early stages. OBJECTIVE: This study seeks to purify and characterize a novel β-secretase inhibitory peptide from Pacific hake enzymatic hydrolysate. METHODS: A potent β-secretase inhibitory peptide was isolated by sequential purifications using Sephadex G-25 column chromatography and octadecylsilane (ODS) C18 reversed-phase HPLC. A total of seven peptides were synthesized using the isolated peptide sequences. SH-SY5Y cells stably transfected with the human ''Swedish'' amyloid precursor protein (APP) mutation APP695 (SH-SY5YAPP695swe) were used as an in-vitro model system to investigate the effect of Leu-Asn peptide on APP processing. RESULTS: The β-secretase inhibitory activity (IC50) of the purified peptide (Ser-Leu-Ala-Phe-Val-Asp- Asp-Val-Leu-Asn) from fish protein hydrolysate was 18.65 μM and dipeptide Leu-Asn was the most potent β-secretase inhibitor (IC50 value = 8.82 µM). When comparing all the seven peptides, the inhibition pattern of Leu-Asn dipeptide was found to be competitive by Lineweaver-Burk plot and Dixon plot (Ki value = 4.24 µM). The 24 h treatment with Leu-Asn peptide in SH-SY5Y cells resulted in reducing the β-amyloid (Aβ) production in a dose-dependent manner. CONCLUSION: Therefore, the results of this study suggest that β-secretase inhibitory peptides derived from marine organisms could be potential candidates to develop nutraceuticals or pharmaceuticals as antidementia agents.
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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) | 0.001 | 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".