Rational Design, Structure–Activity Relationship, and Immunogenicity of Hypoallergenic Pru p 3 Variants
Bibliographic record
Abstract
SCOPE: Allergies to lipid transfer proteins involve severe adverse reactions; thus, effective and sustainable therapies are desired. Previous attempts disrupting disulfide bonds failed to maintain immunogenicity; thus, the aim is to design novel hypoallergenic Pru p 3 variants and evaluate the applicability for treatment of peach allergy. METHODS AND RESULTS: Pru p 3 proline variant (PV) designed using in silico mutagenesis, cysteine variant (CV), and wild-type Pru p 3 (WT) are purified from Escherichia coli. Variants display homogenous and stable protein conformations with an altered secondary structure in circular dichroism. PV shows enhanced long-term storage capacities compared to CV similar to the highly stable WT. Using sera of 33 peach allergic patients, IgE-binding activity is reduced by 97% (PV) and 71% (CV) compared to WT. Both molecules show strong hypoallergenicity in Pru p 3 ImmunoCAP cross-inhibition and histamine release assays. Immunogenicity of PV is demonstrated with a phosphate-based adjuvant formulation in a mouse model. CONCLUSIONS: An in silico approach is used to generate a PV without targeting disulfide bonds, T cell epitopes, or previously reported IgE epitopes of Pru p 3. PV is strongly hypoallergenic while structurally stable and immunogenic, thus representing a promising candidate for peach allergen immunotherapy.
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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 | 0.001 |
| 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".