Enzymatic Pea Protein Hydrolysates Are Active Trypsin and Chymotrypsin Inhibitors
Bibliographic record
Abstract
In this work, we report the potency of enzymatic hydrolysates of pea proteins against trypsin and chymotrypsin. Pea protein concentrate was digested with each of alcalase, chymotrypsin, pepsin, and trypsin, followed by membrane separation of the protein hydrolysates into peptide fractions (<1, 1–3, 3–5, and 5–10 kDa). Peptide size profiling with size-exclusion gel chromatography indicated the narrowest size range (0.85–4.98 kDa) for alcalase. Trypsin activity was strongly (p < 0.05) inhibited by the ultrafiltration fractions (mean IC50 = 2.2 mg/mL) obtained from the trypsin hydrolysate when compared to the unfractionated hydrolysate (IC50 = 6.8 mg/mL). Similarly, ultrafiltration also enhanced trypsin inhibition by the alcalase-digested peptides with an IC50 of 21.4 mg/mL for the unfractionated hydrolysate in comparison to 3.1–4.7 mg/mL for the fractions. However, ultrafiltration did not enhance trypsin inhibitory activity of chymotrypsin-digested peptides, while the peptide separation reduced efficacy of pepsin-digested peptides. In contrast, chymotrypsin inhibition by all the enzymatic digests was significantly (p < 0.05) enhanced by ultrafiltration, especially peptide sizes >3 kDa. Kinetics of enzyme inhibition indicate peptides were bound to the enzyme active site in a competitive mode that led to reduced catalysis. We conclude that the pea peptides could function as useful tools to promote human health and as a preservative during food processing and storage.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".