Improving Allergen Prediction in Main Crops Using a Weighted Integrative Method

As a public health problem, food allergy is frequently caused by food allergy proteins, which trigger a type-I hypersensitivity reaction in the immune system of atopic individuals. The food allergens in our daily lives are mainly from crops including rice, wheat, soybean and maize. However, allergens in these main crops are far from fully uncovered. Although some bioinformatics tools or methods predicting the potential allergenicity of proteins have been proposed, each method has their limitation. In this paper, we built a novel algorithm PREAL^W, which integrated PREAL, FAO/WHO criteria and motif-based method by a weighted average score, to benefit the advantages of different methods. Our results illustrated PREAL^W has better performance significantly in the crops' allergen prediction. This integrative allergen prediction algorithm could be useful for critical food safety matters. The PREAL^W could be accessed at http://lilab.life.sjtu.edu.cn:8080/prealw .