Machine learning-driven discovery of bioactive peptides from duckweed (Lemnaceae) protein hydrolysates: Identification and experimental validation of 20 novel antihypertensive, antidiabetic, and/or antioxidant peptides
Bibliographic record
Abstract
Duckweed, a sustainable, protein-rich aquatic plant, has recently emerged as a promising source of bioactive peptides. However, their identification remains limited and challenging in such complex mixtures. Following duckweed hydrolysis with pepsin, chymotrypsin, trypsin and papain, and a centrifugation step producing two fractions: supernatant (DS) and pellet (DP), interesting half-maximal inhibitory concentration (IC 50 ) for dipeptidyl peptidase (DPP)-IV and angiotensin-converting enzyme (ACE) inhibition were obtained for DS fractions, especially with pepsin (IC 50 = 0.7 and 0.07 mg/mL, respectively). Using partial least squares-discriminant analysis (PLS-DA) combined with quantitative structure-activity relationship (QSAR) models, five new DPP-IV inhibitors (most active: API, IC 50 = 126.88 μM), eleven new ACE inhibitors (most active: FAR, IC 50 = 13.54 μM) and four new antioxidants (>200 μM) were identified. Two sequences were active across all three tested bioactivities, revealing promising multi-target peptides. These findings highlight the potential of duckweed-derived peptides to support health and metabolic balance. • Duckweed proteins hydrolysates are promising sources of bioactive peptides • PLS-DA enables efficient bioactive peptide identification from a complex matrix • Discovery of 5 DPP-IV, 11 ACE inhibitors and 4 antioxidant peptides • Duckweed-derived peptides show potential for promoting metabolic health
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".