NetAllergen, a random forest model integrating MHC-II presentation propensity for improved allergenicity prediction

Abstract

AbstractAllergy is a pathological immune reaction towards innocuous protein antigens. Although only a small fraction of plant or animal proteins induce allergy, the atopic disorders affect millions of children and adults, and cost billions in healthcare systems worldwide. In-silico predictors can aid in the development of more innocuous food sources. Previous allergenicity predictors are based on sequence similarity, common structural domains, and amino acid physicochemical features. However, these predictors strongly rely on similarity to previous allergens and fail to accurately predict allergenicity when protein sequence similarity diminishes. Furthermore, allergen is a wide terminology that may include different compounds, hindering the classification task. To overcome these limitations, we used allergens collected from AllergenOnline, a curated database of IgE inducing allergens, carefully removed allergen redundancy with a novel protein partitioning pipeline, and developed a new allergen prediction method, NetAllergen, introducing MHC presentation propensity as a novel feature. NetAllergen was demonstrated to out-perform a sequence similarity-based Blast method as well as previous allergenicity predictors when similarity to known allergens is diminished. NetAllergen is available as a web-service (services.healthtech.dtu.dk/service.php?NetAllergen-1.0) and can predict allergenicity from a protein sequence.