The use of tryptic food protein digests data in public proteomic repositories to assess the effects of chemical and post-translational modifications on digestion outcomes
Bibliographic record
Abstract
Porcine-derived trypsin generated proteomic data of the major peanut allergen Ara h 1 from the peanut was reassessed to search for possible facilitating/hindrance effects on trypsin digestion efficacy caused by post-translational and chemical modifications (PTMs) positioned on arginine or lysine (K/R) residues. If the potential effects caused by PTMs are observed with porcine trypsin, they can be just augmented and more pronounced within human intestinal digestion. The reasoning is in inferior performance of human trypsin compared to porcine-derived used in proteomic digestion protocols, also in the lower trypsin-to-sample ratio and much shorter digestion times, even though gastric digestion precedes and trypsin is not the sole digestive enzyme. A novel method was developed to decipher cleavage or miscleavage outcomes at scissile bonds in each, modified and unmodified sequence counterparts, using PEAKS Studio-X+ (Bioinformatics Solutions Inc., Ontario, Canada) in the reassessment of high-resolution tandem mass spectrometry data, from 18-hour long trypsin digestion proteomic protocols. In general, eight site-specific and modified K/R residues with methylation, dihydroxy and formylation showed significantly higher content of miscleaved bonds (at least >10%) compared to their unmodified counterpart peptides. Specifically, dihydroxylation and formylation hindered trypsin efficacy, while methylation on several K/R showed opposite effects. It is essential to elucidate the specific impacts of modifications on trypsin digestion performance and if there are additional effects generated by food processing, which could influence digestion outcomes and allergenicity of food proteins/peptides.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".