Original Research
ALLERDET: A novel web app for prediction of protein allergenicity

https://doi.org/10.1016/j.jbi.2022.104217Get rights and content
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Highlights

  • A new software tool to detect new allergens from amino acid sequences in FASTA format.

  • Our tool can be used to discover new allergens.

  • The accuracy achieved by ALLERDET is 97.26%, overcoming the state-of-the-art methods.

  • ALLERDET provides a methodological reference for allergen detection tools.

Abstract

Allergic diseases are increasing around the world with unprecedented complexity and severity. One of the reasons is that genetically modified crops produce new potentially allergenic proteins. From this starting point, many researchers have paid attention to the development of tools to predict the allergenicity of new proteins. In this study, a novel approach is introduced for the prediction of food allergens based on Artificial Intelligence techniques: a pairwise sequence alignment with the FASTA program for feature extraction and the use of the Deep Learning technique known as Restricted Boltzmann Machines in combination with the Decision Tree method for the prediction process. The developed tool, called ALLERDET (publicly available at http://allerdet.frangam.com), overcomes the state-of-the-art methods. The performance of our method is: 98.46% sensitivity, 94.37% specificity and 97.26% accuracy), on a data set built from several publicly available sources.

MSC

68-04
92-04

Keywords

ALLERDET
Allergen detection
Food allergy
Pairwise sequence alignment
FASTA
Restricted Boltzmann Machines

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