In Vitro Acetylcholinesterase‐Inhibitory Properties of Enzymatic Hemp Seed Protein Hydrolysates
Bibliographic record
Abstract
Abstract The aim of this work was to characterize the structural and functional properties of hemp seed protein‐derived acetylcholinesterase (AChE)‐inhibitory enzymatic hydrolysates. Hemp seed protein isolate hydrolysis was performed using six different proteases (pepsin, papain, thermoase, flavourzyme, alcalase and pepsin + pancreatin) at different concentrations (1–4 %). The degree of hydrolysis was directly related to the amount of protease used but had no relationship with AChE‐inhibitory activity. Amino acid composition results showed that the hemp seed protein hydrolysates (HPHs) had high levels of negatively charged amino acids (39.62–40.18 %) as well as arginine. The 1 % pepsin HPH was the most active AChE inhibitor with ~6 µg/mL IC 50 value when compared to 8–11.6 µg/mL for the other HPHs. Mass spectrometry analysis showed that most of the peptides in all the hydrolysates were less than 1000 Da in size. However, the pepsin HPHs contained larger‐sized peptides (244–1009 Da) than the papain HPHs (246–758 Da), which in turn was larger than the alcalase HPH (246–607 Da). The higher AChE‐inhibitory effects of the pepsin HPHs may be due to increased synergistic effects from a wider peptide size range when compared to the papain and alcalase HPHs that had narrower ranges. The narrow peptide size range in the alcalase HPH confirms the higher efficiency of this protease in releasing small‐sized peptides from food proteins.
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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".