AllerTOP v.2 - a server for in silico prediction of allergens

Ivan Dimitrov, Ivan Bangov, Darren R. Flower, Irini Doytchinova*

*Corresponding author for this work

Research output: Contribution to journalArticle

Abstract

Allergy is an overreaction by the immune system to a previously encountered, ordinarily harmless substance - typically proteins - resulting in skin rash, swelling of mucous membranes, sneezing or wheezing, or other abnormal conditions. The use of modified proteins is increasingly widespread: their presence in food, commercial products, such as washing powder, and medical therapeutics and diagnostics, makes predicting and identifying potential allergens a crucial societal issue. The prediction of allergens has been explored widely using bioinformatics, with many tools being developed in the last decade; many of these are freely available online. Here, we report a set of novel models for allergen prediction utilizing amino acid E-descriptors, auto- and cross-covariance transformation, and several machine learning methods for classification, including logistic regression (LR), decision tree (DT), naïve Bayes (NB), random forest (RF), multilayer perceptron (MLP) and k nearest neighbours (kNN). The best performing method was kNN with 85.3% accuracy at 5-fold cross-validation. The resulting model has been implemented in a revised version of the AllerTOP server (http://www.ddg-pharmfac.net/AllerTOP).

Original languageEnglish
Article number2278
Number of pages6
JournalJournal of Molecular Modeling
Volume20
Issue number6
DOIs
Publication statusPublished - 31 May 2014

Bibliographical note

This paper belongs to Topical Collection MIB 2013 (Modeling Interactions in Biomolecules VI).

Funding: Bulgarian Science Fund (Grants DCVNP 02-1/2009 and IO1/7)

Keywords

  • ACC transformation
  • allergen prediction
  • decision tree
  • e-descriptors
  • k nearest neighbours
  • logistic regression
  • multilayer perceptrone
  • naïve bayes
  • random forest

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