Fish isoallergens and variants: database compilation, in silico allergenicity prediction challenges, and...

Area of Science:

• Immunology
• Bioinformatics
• Food Allergy Research

Background:

• Fish allergy is a significant health concern, complicated by diverse protein isoforms and species-specific allergenicity.
• Accurate identification of allergenic fish proteins is crucial for managing food allergies.

Purpose of the Study:

• To systematically compile fish isoallergen and variant entries linked to allergic reactions.
• To evaluate the effectiveness of *in silico* tools for predicting fish allergenicity.
• To develop optimized thresholds for allergenicity prediction using epitope mapping and phylogenetic analysis.

Main Methods:

• Compiled fish isoallergen data from four major allergen databases.
• Assessed the sensitivity of five *in silico* prediction tools.
• Performed epitope mapping and phylogenetic analysis on parvalbumin, integrating B-cell epitope data.

Main Results:

• Identified 79 unique fish isoallergen and variant entries from 34 species.
• AllerCatPro 2.0 demonstrated the highest sensitivity (97.5%) among tested tools.
• Developed optimized thresholds for parvalbumin allergenicity prediction with 96% accuracy.

Conclusions:

• Systematic compilation of fish allergens and integration of epitope data enhance understanding of fish allergy.
• Optimized prediction thresholds improve the accuracy of allergenicity assessment tools.
• The developed methodology can be applied to other protein families for future allergenicity studies.