Determination of Nutritional and Bioactive Properties of Peptides in Enzymatic Pea, Chickpea, and Mung Bean Protein Hydrolysates
Bibliographic record
Abstract
Within the primary structure of many pea and mung bean proteins are peptide sequences that can potentially be used in the formulation of therapeutic products for the treatment and prevention of human diseases. However, these peptide sequences need protease treatments before they can be released free of the parent proteins. Unlike chemical hydrolysis, enzymatic treatment enables more efficient tailoring of peptide products without formation of toxic by-products or destruction of amino acids. This review provides information on current methods that have been used to convert inactive pea and mung bean proteins into bioactive peptides. It focuses on 3 main bioactive properties, such as inhibitions of (1) angiotensin converting enzyme (ACE) activity; (2) calmodulin (CaM)-dependent enzymes; and (3) copper-chelating activity. ACE is an established marker for hypertension, high levels of some CaM-dependent enzymes are risk factors for various human diseases including cancer and Alzheimer's disease, and high vascular copper concentrations may potentiate atherosclerosis. Also reviewed are the production and evaluation of activity of hypoallergenic peptides that may offer protection against anaphylactic reactions. The 3 main proteins discussed are chickpea, mung bean, and field pea.
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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 | 0.001 | 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) | 0.000 | 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 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".