Autoclave processing modulates allergenic potential of red kidney beans (Phaseolus vulgaris L.): proteomic and bioinformatics insights after in vitro gastric digestion
Bibliographic record
Abstract
Red kidney beans ( Phaseolus vulgaris L.), a nutrient-dense legume, face significant limitations in food applications due to persistent allergenicity. The dual effects of industrial autoclaving (121 °C, 15 min) on allergenicity modulation were evaluated through in vitro gastrointestinal digestion models in this study, integrated with sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE), high performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS), immunoblotting, and computational epitope prediction. Electrophoretic profiling demonstrated partial degradation of major allergens for 43 kDa phaseollin-like protein, lectin subunits (31/25 kDa). Legumin α -subunits exhibited pepsin resistance (74.8% stability), contrasting with rapid degradation of α -amylase inhibitor ( α -AI) and group 3 late embryogenesis abundance (G3LEA) proteins. Chromatographic immunoreactivity screening revealed autoclaved samples generated 8 antigenic fractions, including novel hydrophobic peptides with enhanced immunoglobulin E (IgE)-binding potential. Mass spectrometry identified 849 and 1 516 unique peptides in raw and autoclaved digests, respectively, with phaseollin and lectins dominating post-processing epitopes. Bioinformatics highlighted conserved allergenic motifs (e.g., VLVKPIQIR, LPQQADAE) in phaseollin and lectins, alongside cross-reactive sequences homologous. Paradoxically, autoclaving reduced intact allergen content but amplified immunoreactive diversity through conformational exposure of epitopes and hydrophobic residue enrichment. These findings underscore the necessity for epitope-specific risk assessment in legume processing, balancing benefits of thermal intervention against unintended allergenic consequences.
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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) | 0.000 | 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".