2017  0,977
2016  0,799
2015  0,662
2014  0,740
2013  0,739
2012  0,637
2011  0,658
2010  0,654
2009  0,570
2008  0,849
2007  0,805
2006  0,330
2005  0,435
2004  0,623
2003  0,567
2002  0,641
2001  0,490
2000  0,477
1999  0,762
1998  0,785
1997  0,507
1996  0,518
1995  0,502
Vol 52(2018) N 2 p. 285-293; DOI 10.1134/S0026893318020127 Full Text

Vijayakumar Saravanan*, Namasivayam Gautham

BCIgEPRED-a Dual-Layer Approach for Predicting Linear IgE Epitopes

Center for Advanced Study in Crystallography and Biophysics, University of Madras, Guindy Campus, Chennai, Tamil Nadu, 600025 India

Received - 2016-12-02; Accepted - 2017-03-13

Allergy is a common health problem worldwide, especially food allergy. Since B cell epitopes that are recognized by the IgE antibodies act as antigenic determinants for allergy, they play a vital role in diagnostics. Hence, knowledge of an IgE binding epitope in a protein is of particular interest for identifying aller-genic proteins. Though IgE epitopes maybe conformational or linear, identification of the later is useful especially in food allergens that undergo processing or digestion. Very few computational tools are available for the prediction of linear IgE epitopes. Here we report a prediction system that predicts the exact linear IgE epitope. Since our earlier study on linear B cell epitope prediction demonstrated the effectiveness of using an exact epitope dataset (in contrast to epitope containing region datasets), the dataset in this study uses only experimentally verified exact IgE, IgG, IgM and IgA epitopes. Models for Support Vector Machine (SVM) and Random Forest (RF) were constructed adopting Dipeptide Deviation from the Expected mean (DDE) feature vector. Extensive validation procedures including five-fold cross validation and two different independent dataset tests have been performed to validate the proposed method, which achieved a balanced accuracy ranging from 74 to 78% with area under receiver operator curve greater than 0.8. Performance of the proposed method was observed to be better (accuracy difference of 16-28%) in comparison to the existing available method. The proposed method is developed as a standalone tool that could be used for predicting IgE epitopes as well as to be incorporated into any allergen prediction tool

epitopes, immunoglobulin E, food allergy, B cell epitope, dipeptide deviation from expected mean, BCIgEPred