Interactions of C. frondosa-derived inhibitory peptides against angiotensin I-converting enzyme (ACE), α-amylase and lipase
Bibliographic record
Abstract
The study illustrates the molecular mechanisms by which marine-derived peptides exhibited different structures and inhibition functions to concurrently inhibit multiple enzymes involved in chronic diseases. Peptides (2 mg/mL) exhibited inhibition against angiotensin-converting enzyme (ACE, inhibition of 52.2–78.8%), pancreatic α-amylase (16.3–27.2%) and lipase (5.3–17.0%). Further in silico analyses on physiochemistry, bioactivity, safety and interaction energy with target enzymes indicated that one peptide could inhibit multiple enzymes. Peptide FENLLEELK potent in inhibiting both ACE and α-amylase showed different mechanisms: it had ordered extended structure in ACE active pocket with conventional H-bond towards Arg522 which is the ligand for activator Cl-, while the peptide folded into compact “lariat” conformation within α-amylase active site and the K residue in peptide formed intensive H-bonds and electrostatic interactions with catalytic triad Asp197 − Asp300 − Glu233. Another peptide APFPLR showed different poses in inhibiting ACE, α-amylase and lipase, and it formed direct interactions to lipase catalytic residues Phe77 & His263.
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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 | 0.000 | 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) | 0.001 | 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".