Accuracy of protein allergenicity prediction can be improved by taking into account data on allergenic protein discontinuous peptides

Abstract

Allergy poses major health problems in industrialized countries, affecting over 20% of the population. Proteins from transgenic foods, cosmetics, animal hair, and other ubiquitous sources can be allergens. For this reason, development of improved methods for the prediction of potential allergenicity of proteins is timely. The currently available approaches to allergenicity prediction are numerous. Some approaches relied heavily on information on protein three-dimensional (3D) structure for allergenicity prediction. They required knowledge about 3D structure of query protein, thereby considerably restricting analysis to only those proteins whose 3D structure was known. As a consequence, many proteins with unknown structure could be overlooked. We developed a new method for allergenicity prediction, using information on protein 3D structure only for training. Three-dimensional structures of known allergenic proteins were used for representing protein surface as patches designated as discontinuous peptides. Allergenicity was predicted through search of such peptides in query protein sequences. It was demonstrated that the information on the discontinuous peptides made feasible better prediction of allergenic proteins. The allergenicity prediction method is available at http://www-bionet.sscc.ru/psd/cgi-bin/programs/Allergen/allergen.cgi.