Fish Isoallergens and Variants: Database Compilation, In Silico Allergenicity Prediction Challenges, and Epitope-Based Threshold Optimization

Vachiranee Limviphuvadh^1*Thimo Ruethers^1,2,3,4Minh N. Nguyen⁵Dean R. Jerry^2,3Benjamin P.C. Smith^2,6Yulan Wang⁷Yansong Miao⁸Anand Kumar Andiappan⁹Andreas L. Lopata^1,2,3,4Sebastian Maurer-Stroh^1,10*

• ¹Biomolecular Sequence To Function Division (BSFD), Bioinformatics Institute (BII), Agency for Science, Technology and Research (A*STAR), Singapore, Singapore
• ²Tropical Futures Institute, James Cook University, Singapore, Singapore
• ³Centre for Sustainable Tropical Fisheries and Aquaculture, College of Science and Engineering, James Cook University, Townsville, Queensland, Australia
• ⁴Molecular Allergy Research Laboratory, College of Science and Engineering, James Cook University, Townsville, Queensland, Australia
• ⁵Biomolecular Structure To Mechanism Division (BSMD), Bioinformatics Institute (BII), Agency for Science, Technology and Research (A*STAR), Singapore, Singapore
• ⁶Monell Chemical Senses Center, Philadelphia, United States
• ⁷Singapore Phenome Center, Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
• ⁸School of Biological Sciences, Nanyang Technological University, Singapore, Singapore
• ⁹Singapore Immunology Network (SIgN), Agency for Science, Technology and Research (A*STAR), Singapore, Singapore
• ¹⁰Yong Loo Lin School of Medicine and Department of Biological Sciences, National University of Singapore (NUS), Singapore, Singapore

Fish is a major food allergy trigger with a complex variety of allergenic protein isoforms and vast species diversity with variable allergenicity. This is the first study to systematically compile fish isoallergen and variant entries associated with ingestion-related allergic reactions, supported by evidence from in vitro IgE-binding assays and complete amino acid sequences, sourced from four major allergen databases: World Health Organisation and International Union of Immunological Societies (WHO/IUIS), AllergenOnline, Comprehensive Protein Allergen Resource (COMPARE), and Allergome. 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. Challenges in allergenicity predictions 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, with AllerCatPro 2.0 achieving the highest sensitivity (97.5%). Furthermore, 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 phylogenetic tree was constructed, integrating epitope data to optimize protein family-specific thresholds for differentiating allergenic from less/non-allergenic parvalbumins. Our innovative approach established 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 these parvalbumin classes. This study enhances our understanding of fish allergy by systematically compiling fish isoallergens and their variants, and by integrating B-cell epitope data. The optimized thresholds improve the performance of allergenicity prediction tools, and the approach can be applied to other protein families in future.