Neuroprotective Effect of β-secretase Inhibitory Peptide from Pacific Hake (Merluccius productus) Fish Protein Hydrolysate
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 it