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Predicting the allergenicity of proteins is challenging.
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Herein, we used a hybrid prediction (SVMc + IgEepitope + ARPs BLAST + MAST) approach to predict the allergenicity with an accuracy of about 86% at a threshold value of −0.4.
AlgPred predicted the allergenicity of the epitopes based on amino acid composition.
46 In order to predict the allergenicity of the proposed epitopes with high accuracy, a Web-based server AlgPred 47 was used.
The aim of this study is to predict the potential allergenicity of proteins efficiently and analyze the key factors resulted in allergenicity.
Predicting the future?
Commence predicting the cards.
There are also prediction tools often used to predict the presence or absence of linear or structural epitopes following physico-chemical features of the protein obtained from the amino acid sequence of the protein, sequence identity or the relevant FAO/WHO allergenicity rules based on sequence homology (19).
Many predicted the worst.
Lack of these features would be anticipated to reduce allergenicity even in the presence of structural epitopes that may otherwise predict allergenicity.
Structural characterization of allergens is required for understanding the allergenicity of food allergens and for the development of immunotherapeutic agents.
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