AllerTOP--a server for in silico prediction of allergens

• Published in: BMC bioinformatics Vol. 14 Suppl 6; no. Suppl 6; p. S4
• Main Authors: Dimitrov, Ivan, Flower, Darren R, Doytchinova, Irini
• Format: Journal Article
• Language: English
• Published: England BioMed Central 17.04.2013; BioMed Central Ltd
• Subjects: Algorithms; Allergens - chemistry; Allergens - immunology; Allergies; Amino Acid Sequence; Amino acids; Artificial Intelligence; Bayes Theorem; Bioinformatics; Computational Biology - methods; Computer Simulation; Databases, Protein; Food Hypersensitivity; Humans; Hypersensitivity - immunology; Proceedings; Proteins; Proteins - chemistry; Proteins - immunology; Studies; Toxins, Biological - chemistry; Toxins, Biological - immunology
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/1471-2105-14-s6-s4

Allergy is a form of hypersensitivity to normally innocuous substances, such as dust, pollen, foods or drugs. Allergens are small antigens that commonly provoke an IgE antibody response. There are two types of bioinformatics-based allergen prediction. The first approach follows FAO/WHO Codex alimentarius guidelines and searches for sequence similarity. The second approach is based on identifying conserved allergenicity-related linear motifs. Both approaches assume that allergenicity is a linearly coded property. In the present study, we applied ACC pre-processing to sets of known allergens, developing alignment-independent models for allergen recognition based on the main chemical properties of amino acid sequences. A set of 684 food, 1,156 inhalant and 555 toxin allergens was collected from several databases. A set of non-allergens from the same species were selected to mirror the allergen set. The amino acids in the protein sequences were described by three z-descriptors (z1, z2 and z3) and by auto- and cross-covariance (ACC) transformation were converted into uniform vectors. Each protein was presented as a vector of 45 variables. Five machine learning methods for classification were applied in the study to derive models for allergen prediction. The methods were: discriminant analysis by partial least squares (DA-PLS), logistic regression (LR), decision tree (DT), naïve Bayes (NB) and k nearest neighbours (kNN). The best performing model was derived by kNN at k = 3. It was optimized, cross-validated and implemented in a server named AllerTOP, freely accessible at http://www.pharmfac.net/allertop. AllerTOP also predicts the most probable route of exposure. In comparison to other servers for allergen prediction, AllerTOP outperforms them with 94% sensitivity. AllerTOP is the first alignment-free server for in silico prediction of allergens based on the main physicochemical properties of proteins. Significantly, as well allergenicity AllerTOP is able to predict the route of allergen exposure: food, inhalant or toxin.