Using patient serum to epitope map soybean glycinins reveals common epitopes shared with many legumes and tree nuts
Bibliographic record
Abstract
Soybean consumption is increasing in many Western diets; however, recent reviews suggest that the prevalence of soy allergy can be as high as 0.5% for the general population and up to 13% for children. The immunoglobulin-E (IgE) binding of sera from six soy-sensitive adult human subjects to soybean proteins separated by 2D gel electrophoresis was studied. Synthetic peptide sets spanning the mature glycinin subunit A2 and A3 primary sequences were used to map the IgE-binding regions. Putative epitopes identified in this study were also localized on glycinin hexamer models using bioinformatics software. We identified linear IgE-binding epitopes of the major storage protein Gly m 6 by screening individual soy-sensitive patient sera. These epitopes were then further analysed by 3D in silico model localization and compared to other plant storage protein epitopes. Web-based software applications were also used to study the ability to accurately predict epitopes with mixed results. A total of nine putative IgE-binding epitopes were identified in the glycinin A3 (A3.1-A3.3) and A2 (A2.1-A2.6) subunits. Most patients' sera IgE bound to only one or two epitopes, except for one patient's serum which bound to four different A2 epitopes. Two epitopes (A3.2 and A2.4) overlapped with a previously identified epitope hot spot of 11S globulins from other plant species. Most epitopes were predicted to be exposed on the surface of the 3D model of the glycinin hexamer. Amino acid sequence alignments of soybean acidic glycinins and other plant globulins revealed one dominant epitope hot spot among the four reported hot spots. This study may be helpful for future development of soy allergy immunotherapy and diagnosis.
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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) | 0.001 | 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 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".