<i>In silico</i> investigation of molecular targets, pharmacokinetics, and biological activities of chicken egg ovalbumin protein hydrolysates
Bibliographic record
Abstract
Food-derived bioactive peptides are promising ingredients for developing functional foods and nutraceuticals due to their putative safety, low cost, and multiple health benefits. Chicken egg is considered a major source of dietary protein, lipids, vitamins, and minerals but is also highly allergenic. The aim of this work was to investigate the inherent bioactive properties of chicken ovalbumin peptides using in silico approaches. Ovalbumin was in silico hydrolyzed with gastrointestinal proteases (chymotrypsin, trypsin, and pepsin) and results indicated cleavage of the most allergenic protein with an overall 36.62% theoretical degree of hydrolysis, consisting of 132 fragments of which 65 were di-, tri-, tetra- or oligopeptides. The most represented biological targets obtained for these peptides include HLA class I histocompatibility antigen A-3, E3 ubiquitin-protein ligase XIAP, and angiotensin-converting enzyme. Notably, peptides AIVF and AVL were found to have multi-target potentials. Gene enrichment analysis showed interaction of these peptides with some kinases and transcription factors. Overall, results from binding affinity, pharmacokinetics and physicochemical properties, and therapeutic activity showed that PGF, SSL, GGL, AVL, VY, and IL are promising peptide candidates for further studies. These results are important in the design of peptide-based functional foods and therapeutic products devoid of allergenic property of ovalbumin.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.002 | 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".