Structure-activity relationships of bioactive peptides derived from legumes: significance and perspectives
Bibliographic record
Abstract
Legumes, esteemed globally for their rich protein content, eco-friendly lifecycle, and versatility in vegetarian, vegan, and flexitarian diets, have emerged as pivotal crops. Peptides derived from legumes, such as soybean, chickpea, pea, lentil, and peanut, can be obtained through various methods, including microbial fermentation and enzymatic hydrolysis, to exhibit several bioactivities, including antioxidant, hypoglycemic, antihypertensive, and anti-inflammatory effects. The structure of these peptides plays a crucial role in determining the bioactivities. However, comprehensive reviews or commentaries on the structure-activity relationship (SAR) of legume-derived peptides (LDPs) are currently lacking. In this review, an overview of the commonly used LDP preparation methods, the biological activities, and mechanisms underlying the SARs of the peptides are discussed. Notably, the degree of hydrolysis, molecular weight, and the amino acid hydrophobicity, basicity, aromatic degree, and position in the peptide chain (particularly at the N- or C-termini) can influence the structural conformation of LDPs to enhance their bioactivities. It was also observed that the impact of higher-order structures on the bioactivity and safety of LDPs has received limited attention in current research. Future research could harness advanced computational methods to explore more complex SARs of LDPs to attain improved applications of LDPs in functional food contexts.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".