Identification and analysis of putative allergens in Edible mushroom (Agaricus bisporus)

Abstract

Allergies result from abnormal immune responses to otherwise harmless proteins. In recent years, the incidence of food allergies has increased significantly, necessitating improved methods for allergen identification and risk assessment. While conventional allergen detection methods rely on clinical or laboratorybased protocols, computational approaches offer a faster and scalable alternative for preliminary allergenicity screening. Agaricus bisporus var. burnetti, a widely consumed edible mushroom, remains understudied with respect to its allergenic potential. This study employs a multi-layered in silico strategy to evaluate the allergenicity of Agaricus bisporus proteins using peptide frequency analysis, epitope mapping, and structural modeling. RESULTS Putative allergens were first shortlisted through sequence-based homology screening using FASTA and BLAST, identifying proteins with >50% identity to known allergens in the AllergenOnline database. Amino acid, dipeptide, tripeptide, and tetrapeptide compositions of the full proteome (11,675 proteins), 55 putative allergens, and 2,334 known allergens were compared. Physicochemical property-based predictions from AllergenFP, AlgPred, and AllerTOP further validated the allergenicity of several candidates. 3D structures of key putative allergens were modeled using SWISS-MODEL. Protein-peptide docking simulations conducted via ClusPro 2.0 identified strong binding affinities between modeled epitopes and known IgE-binding domains. Functional classification using Uniprot and GO annotations revealed biological relevance of candidate proteins in stress and defense-related pathways. Peptidomic profiling revealed distinct enrichment of specific short peptides in the allergen datasets relative to the proteome. Linear B-cell epitopes were retrieved from IEDB and matched against putative allergens to uncover immunologically relevant motifs. CONCLUSION 10 This study demonstrates that in silico analysis can reliably identify and characterize putative allergens in Agaricus bisporus var. burnetti from the rest of the proteome. Peptide-level enrichment analysis, epitope mapping, and structurebased validation collectively provide a novel framework for early-stage allergenicity screening in novel food proteins. Keywords: Agaricus bisporus var. burnetti; allergy; food allergy; fungal allergens; peptide enrichment; in silico allergen prediction; cross-reactivity; B-cell epitopes.