Angiotensin I Converting Enzyme Inhibitory Peptides from Simulated <i>in Vitro</i> Gastrointestinal Digestion of Cooked Eggs
Bibliographic record
Abstract
Egg proteins are an excellent source of bioactive peptides. The purpose of this work was to study the effect of cooking methods on the production of angiotensin converting enzyme (ACE) inhibitory peptides. Boiled or fried eggs (in the forms of whites, yolks, and whole eggs) were digested by gastrointestinal tract proteases at simulated gut conditions. Fried egg digests showed more potent activity than those of boiled egg digests; the fried whole egg digest had an IC(50) value of 0.009 mg protein/mL. This hydrolysate was further purified by cation exchange chromatography and gel filtration chromatography. Seven peptides, Val-Asp-Phe (IC(50): 6.59 microM), Leu-Pro-Phe (10.59 microM), Met-Pro-Phe (17.98 microM), Tyr-Thr-Ala-Gly-Val (23.38 microM), Glu-Arg-Tyr-Pro-Ile (8.76 microM), Ile-Pro-Phe (8.78 microM), and Thr-Thr-Ile (24.94 microM), were identified by liquid chromatography-mass spectrometry (LC-MS/MS), and their IC(50) values were predicted by using our previously reported structure and activity models. The presence of several tripeptides from in vitro simulated gastrointestinal egg digest indicates that these peptides may be absorbed into the body and exert an in vivo antihypertensive activity, although in vivo study is needed to confirm this assumption. Our results showed that in vitro digestion of cooked eggs could generate a number of potent ACE inhibitory peptides which may have implications for cardiovascular disease prevention, including hypertension.
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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) | 0.000 | 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".