Comparative study of raw and thermally treated peanut major allergen post- translational modifications (PTMs)
Bibliographic record
Abstract
Introduction. Peanut allergy affects a large portion of world population causing reactions ranging \nfrom mild to severe. Major peanut allergen IgE epitopes are well characterized but little is known \nabout their post-translational modifications (PTM) and how they are affected by thermal \ntreatment. PTM profile may differ between raw and thermally treated peanut, which could affect \nits allergic potential depending on type, size and position of modifications. \nObjective. Our aim was to analyse and compare PTM profiles of 4 major peanut allergens - Ara h 1, \nAra h 2, Ara h 3 and Ara h 6, as well as their amounts in raw and roasted samples using bottom-up \nproteomics methods. \nMethodology. Full peanut protein extracts (both thermally treated and non-treated) were digested \nin gel and in solution, and analysed by a Top10 nLC-MS/MS method by LTQ Orbitrap XL (Thermo \nFisher Scientific Inc., Germany). Within the extracts major allergens - Ara h 1, Ara h 2, Ara h 3 and \nAra h 6 were identified, label free quantified (LFQ) and searched for PTMs by Peaks X software \n(Bioinformatics solutions Inc.I, Canada). Epitope sequences were acquired from the Immune \nEpitope Database (IEDB www.iedb.org). \nMain findings. LFQ results show that there is no significant change in the amountsof any of the \nstudied allergens between raw and roasted extracts.Out of the 4 allergens Ara h 6 is modified in the \nhighest portion, with respect to the protein size: 15% and 12% of its positions are modified in raw \nand roasted sample, respectively. Total of 21 modifications were quantified between the two \npreparations, with oxidation (M), methylation (K,R) and dethiomethylation affecting the largest \nnumber of peptides. \nConclusions. Peanut allergen epitopes are indeed carriers of PTMs that differ in pattern and \nquantity between treated and non-treated extracts. The in silico discovered PTMs could affect \nprotein digestibility and allergenicity. Further investigation is necessary in order to fully understand \nthe impact protein modifications could have on their allergenic potential.
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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 | 0.000 | 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 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".