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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 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 | 0.000 | 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 teacher head, 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".