# Fish isoallergens and variants: database compilation, in silico allergenicity prediction challenges, and epitope-based threshold optimization

## TL;DR

This study compiles fish isoallergens and evaluates allergenicity prediction tools, improving understanding of fish allergies and offering optimized thresholds for better predictions.

## Contribution

The first systematic compilation of fish isoallergens and variants, along with optimized epitope-based thresholds for allergenicity prediction.

## Key findings

- A dataset of 79 unique fish isoallergen and variant entries from 34 species was compiled, with 25 common across four databases.
- AllerCatPro 2.0 showed the highest sensitivity (97.5%) for allergenicity prediction.
- A threshold of ≥4 IEDB-mapped epitopes with up to two mismatches effectively distinguished allergenic from non-allergenic parvalbumins.

## Abstract

Fish is a major food allergy trigger with a complex variety of allergenic protein isoforms and vast species diversity exhibiting variable allergenicity. This is the first study to systematically compile fish isoallergen and variant entries associated with ingestion-related allergic reactions.

Entries were compiled from four major allergen databases: World Health Organization and International Union of Immunological Societies (WHO/IUIS), AllergenOnline, Comprehensive Protein Allergen Resource (COMPARE), and Allergome, including evidence from in vitro IgE-binding assays and complete amino acid sequences. Challenges in predicting the allergenicity of fish isoallergens and variants were evaluated, and the sensitivity of five widely used in silico tools (AllerCatPro 2.0, AlgPred 2.0, pLM4Alg, AllergenFP v.1.0, and AllerTop v.2.0) was assessed. Epitope mapping and phylogenetic analyses were performed for the major fish allergen parvalbumin, incorporating experimentally validated B-cell epitope data from the Immune Epitope Database (IEDB) and evolutionary relationships.

A comprehensive dataset of 79 unique fish isoallergen and variant entries from 34 fish species was identified, with 25 entries common across all four databases. AllerCatPro 2.0 achieved the highest sensitivity (97.5%). A phylogenetic tree was constructed, integrating epitope data to optimize protein family-specific thresholds for differentiating allergenic from less/non-allergenic parvalbumins. A threshold of ≥4 IEDB-mapped epitopes allowing up to two mismatches captured 52 out of 54 parvalbumin sequences (96%) in the dataset, effectively distinguishing between parvalbumin classes.

This study enhances understanding of fish allergy by systematically compiling fish isoallergens and variants and integrating B-cell epitope data. The optimized thresholds improve the performance of allergenicity prediction tools and can be applied to other protein families in future studies.