In silico tools to assess the potential allergenicity of shiitake mushrooms (Lentinula edodes)

Computational tools may have an edge over conventional methods for the preliminary evaluation of food allergenicity. In this study, the allergenic potential of Lentinula edodes was evaluated and validated using in silico tools.

The potential cross-reactivity of mushroom proteins with fungal allergens was determined using sequence alignment - the Fast Alignment (FASTA) and Basic Local Alignment Search Tool (BLAST) algorithm. Eight L. edodes proteins were cross-reactive with allergens from fungal origin, showing 52%-89% sequence identity using FASTA algorithm-based alignment. The BLAST data were corroborated by percentage identity and query coverage. Physico-chemical property-based allergenicity was deciphered by AlgPred, Allermatch, and AllergenFP software, which predicted six out of eight proteins as potential allergens. Sequence alignment showed 66%-86% conservancy between mushroom protein and known fungal allergens. Secondary structure and amino acid composition supported structural affinity between query and fungal proteins. Three-dimensional structures of five mushroom proteins were generated, quality assessed, and superimposed with fungal allergens, suggesting possible allergenicity of mushroom proteins. An enzyme-linked immunosorbent assay (ELISA) demonstrated immunoglobulin E (IgE) binding in 13 out of 21 food-hypersensitive patients' sera.

In silico tools provide preliminary indications about the potential allergenicity and cross-reactivity of mushroom proteins. This approach may be used for the prelusive allergenicity assessment of allergen sources. © 2022 Society of Chemical Industry.