Improving Allergen Prediction in Main Crops Using a Weighted Integrative Method

AbstractAs 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 PREALW, 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 PREALW has better performance significantly in the crops ’ allergen prediction. This integrative allergen prediction algorithm could be useful for critical food safety matters. The PREALW could be accessed athttp://lilab.life.sjtu.edu.cn:8080/prealw.

Source: Interdisciplinary Sciences, Computational Life Sciences - October 11, 2016 Category: Bioinformatics Source Type: research