“Allergolds”: Gold Nanocluster-Based Bioconjugates of Food Allergens with Reduced Immunoglobulin E Binding
Bibliographic record
Abstract
Allergen-specific immunotherapy represents the only method of achieving a lasting reduction in the severity of allergic symptoms. However, the need to expose patients to the allergens to which they are sensitized carries risks. One solution is to use denatured allergens whereby the structure of allergenic proteins is disrupted, preventing their recognition by immunoglobulin E (IgE) antibodies and thus reducing the risk of adverse reactions. Denaturation is often carried out by using chemical cross-linking to generate allergoids. Gold nanoclusters (AuNCs) are emerging as versatile tools in biotechnology due in part to their ability to conjugate a wide range of biological molecules. Previous works have described the formation of AuNC using egg allergens such as Gal d 4 (lysozyme), Gal d 2 (ovalbumin), and whole egg whites. In all cases, AuNC bioconjugation disrupted the protein structure, allowing for their use in biosensing applications. In this work, we hypothesize that these AuNC-allergen bioconjugates could be used to generate "Allergolds", chemically altered versions of allergenic proteins analogous to traditional allergoid formulations. Using spectroscopic techniques, we confirm that the formation of AuNC bioconjugates of the chicken egg Gal d 4 and Gal d 2 disrupts protein structure when generated from both purified protein and whole egg whites. This structural perturbation was found to be resilient to a range of chemical conditions and successfully disrupted recognition by human IgE. These results establish Allergolds as a potential tool for generating systematically denatured allergens from both purified proteins and biological extracts.
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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".