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

Dimitrov, Ivan; Bangov, Ivan; Flower, Darren R. and Doytchinova, Irini (2014). AllerTOP v.2 - a server for in silico prediction of allergens. Journal of Molecular Modeling, 20 (6),


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 (

Divisions: Life & Health Sciences > Pharmacy
Additional Information: 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)
Uncontrolled Keywords: ACC transformation,allergen prediction,decision tree,e-descriptors,k nearest neighbours,logistic regression,multilayer perceptrone,naïve bayes,random forest,Physical and Theoretical Chemistry,Computer Science Applications,Computational Theory and Mathematics,Catalysis,Organic Chemistry,Inorganic Chemistry
Published Date: 2014-05-31


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