Abstract
The purpose of the present study is to develop a simple method for the classification of food proteins with respect to their allerginicity. The methods applied to solve the problem are well-known multivariate statistical approaches (hierarchical and non-hierarchical cluster analysis, two-way clustering, principal components and factor analysis) being a substantial part of modern exploratory data analysis (chemometrics). The methods were applied to a data set consisting of 18 food proteins (allergenic and non-allergenic). The results obtained convincingly showed that a successful separation of the two types of food proteins could be easily achieved with the selection of simple and accessible physicochemical and structural descriptors. The results from the present study could be of significant importance for distinguishing allergenic from non-allergenic food proteins without engaging complicated software methods and resources. The present study corresponds entirely to the concept of the journal and of the Special issue for searching of advanced chemometric strategies in solving structural problems of biomolecules.
Highlights
Food allergy is an atypical immunological reaction to food proteins, which causes an adverse clinical reaction
This study reveals a workflow based on chemometric methods for prediction of the allergenicity nature of the most important food allergens causing IgE-mediated food allergy that is believed to be responsible for most immediate-type, food-induced hypersensitive reactions
Cluster Analysis for Protein Classification Based on 2D and 3D Molecular Descriptors
Summary
Food allergy is an atypical immunological reaction to food proteins, which causes an adverse clinical reaction. In the work of Maleki et al, the digestibility of the major allergens in peanut during boiling, frying or roasting and in refined form was considered [5,6]. Despite the social importance of this issue, there is still no valuable methodology for prediction of allergenic structure or a proper methodology for the treatment of food allergies. The structural features of proteins could possibly contribute towards their allergenicity prediction. Developing such an in silico classification model may validate an appropriate approach for assisting in the allergenic potential of novel proteins. The exploratory methods based on a three-dimensional structure of allergens is of significance importance to make prediction models for allergenicity, which would allow the interpretation of the possible reasons for allergenicity of the proteins by combination of experimental and theoretical approaches
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