Generation of Bioactive Peptides from Porphyridium sp. and Assessment of Their Potential for Use in the Prevention of Hypertension, Inflammation and Pain
Bibliographic record
Abstract
Inflammation, hypertension, and negative heart health outcomes including cardiovascular disease are closely linked but the mechanisms by which inflammation can cause high blood pressure are not yet fully elucidated. Cyclooxygenase (COX) enzymes play a role in pain, inflammation, and hypertension development, and inhibition of these enzymes is currently of great interest to researchers and pharmaceutical companies. Non-steroidal anti-inflammatory drugs are the drug of choice in terms of COX inhibition but can have negative side effects for consumers. Functional food ingredients containing cyclooxygenase inhibitors offer a strategy to inhibit cyclooxygenases without negative side effects. Several COX inhibitors have been discovered, to date, from marine and other resources. We describe here, for the first time, the generation and characterization of a bioactive hydrolysate generated using Viscozyme® and Alcalase from the red microalga Porphyridium sp. The hydrolysate demonstrates in vitro COX-1 inhibitory activity and antihypertensive activity in vivo, assessed using spontaneously hypertensive rats (SHRs). Peptides were identified and sequenced using MS and assessed using an in silico computational approach for potential bioactivities. The peptides predicted to be bioactive, including GVDYVRFF, AIPAAPAAPAGPKLY, and LIHADPPGVGL were chemically synthesized and cyclooxygenase inhibition was confirmed. Peptides AIPAAPAAPAGPKLY and LIHADPPGVGL had COX-1 IC50 values of 0.2349 mg/mL (0.16 µM) and 0.2193 mg/mL (0.2 µM), respectively. The hydrolysate was included in a food carrier (jelly candies) and an antihypertensive effect was observed in SHRs.
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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 | 0.001 | 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 | 0.000 | 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".