Major peanut allergen Ara h1 and Ara h 3 epitope post-translational modifications (PTMs)
Bibliographic record
Abstract
Objective: Peanuts are widely used for the preparation of a variety of foods and are also relied on \nas a protein extender. Peanut allergies affect a large portion of world population causing reactions \nranging from mild to severe that can lead to anaphylaxis and even death. Seed storage proteins Ara \nh 1 and Ara h 3 are known as major peanut allergens. IgE epitopes of these allergens have been \ncharacterized, but little is known about how post-translational modifications (PTMs) affect their \nallergenicity and digestibility. Our aim was to investigate PTMs present on known epitopes of said \nproteins using bottom-up proteomcs methods. \nMaterial and Methods: Purified 2S albumins (Ara h 1 and Ara h 3) were analysed by a Top5 nLC- \nMS/MS method by LTQ Orbitrap XL (Thermo Fischer Scientific, Germany). Spectra were \ncompared to Uniprot derived Peanut protein database, hybridized with the Repository of \nAdventitious Proteins (cRAP), using Peaks 8.5 software package (BSI, Canada). Epitopes were \nsearched for possible PTMs by matching PEAKS PTM results with mapped positions of epitope \nsequences (found in the Immune Epitope Database – IEDB www.iedb.org). \nResults: According to IEDB Ara h 1 contains 327 peptide epitopes, within which we detected 8 \nlikely PTMs. Hydroxylation Pro and pyro-glu from Q were found as most common in Ara h 1 \nepitopes. Ara h 3 has only 110 epitopes, according to IEDB with 10 likely PTMs. Hydroxylation \nPro, dehydration and methylation (KR) were found as most frequent in Ara h 3 epitopes. PTMs \ncould be found in the vicinity of trypsin cleavage sites, which could have an impact on digestibility. \nConclusions: Peanut allergen epitopes are indeed carriers of PTMs. These results show promise in \nrevealing a possible role PTMs could have on protein allergenicity and digestibility. Further \ninvestigation is necessary in order to fully understand the impact protein modifications could have \non their allergenic potential
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".