Effects of Alcalase and Flavourzyme from different manufacturers on the antigenicity and mechanisms of β-lactoglobulin
Bibliographic record
Abstract
β-lactoglobulin (β-LG) is a major allergen in dairy, and enzymatic hydrolysis is an effective way to reduce its antigenicity. This research aimed to explore the differential mechanisms of the effects of Alcalase and Flavourzyme from different manufacturers, Novozymes (N) and Yuanye (Y), on the antigenicity of β-LG. Following optimization using Response Surface Methodology, the antigenicity reduction rates of β-LG by the two Alcalase enzymes were both higher than those of Flavourzyme. However, the degree of hydrolysis indicated that there was no linear relationship between the DH and antigenicity. The results of conformational changes indicated that compared with the other three enzymes, N-Alcalase treatment significantly altered the vibrational stretching of C=O and N-H groups, accompanied by a reduction in α-helix content, fluorescence intensity, and surface hydrophobicity. Peptidomics analysis further revealed that N-Alcalase effectively removed critical allergenic linear epitope regions of β-LG, including amino acids (AA) 16–22, 88–107, and 150–158, where AA 88–107 tends to exhibit higher allergenic potential. In contrast, Flavourzyme exhibited limited efficacy in this regard. The findings indicate that N-Alcalase holds greater potential for reducing β-LG antigenicity, providing a theoretical foundation and technical support for the production of hypoallergenic dairy-based materials.
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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".