Journal of Bioinformatics and Sequence Analysis
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Article Number - FE6EE595768

Vol.5(2), pp. 16-24 , February 2013
DOI: 10.5897/JBSA11.023
ISSN: 2141-2464

Full Length Research Paper

Prediction of MHC Class II binders/non-binders using negative selection algorithm in vaccine designing

S. S. Soam1*, Feroz Khan2, Bharat Bhasker3 and B. N. Mishra4


1Department of Computer Science and Engineering, Institute of Engineering and Technology Gautam Buddh Technical University, Lucknow, India.

2Department of Metabolic and Structural Biology, CSIR-Central Institute of Medicinal and Aromatic Plants, Lucknow, India.

3Department of Information Technology and System, Indian Institute of Management, Lucknow, India.

4Department of Biotechnology, Institute of Engineering and Technology, Gautam Buddh Technical University, Lucknow, India.


 Accepted: 05 February 2013  Published: 28 February 2013

Copyright © 2013 Author(s) retain the copyright of this article.
This article is published under the terms of the Creative Commons Attribution License 4.0


The identification of major histocompatibility complex (MHC) class-II restricted peptides is an important goal in human immunological research leading to peptide based vaccine designing. These MHC class II peptides are predominantly recognized by CD4+ T-helper cells, which when turned on, have profound immune regulatory effects. Thus, prediction of such MHC class-II binding peptide is very helpful towards epitope based vaccine designing. HLA-DR proteins were found to be associated with autoimmune diseases e.g. HLA-DRB1*0401 with rheumatoid arthritis. It is important for the treatment of autoimmune diseases to determine, which peptides bind to MHC class II molecules. The experimental methods for identification of these peptides are both time consuming and cost intensive. Therefore, computational methods have been found helpful in classifying these peptides as binders or non-binders. We have applied negative selection algorithm, an artificial immune system approach to predict MHC class-II binders and non-binders. For the evaluation of the NSA algorithm, five fold cross validation has been used and six MHC class-II alleles have been taken. The average area under ROC curve for HLA-DRB1*0301, DRB1*0401, DRB1*0701, DRB1*1101, DRB1*1501, DRB1*1301 have been found to be 0.75, 0.77, 0.71, 0.72, 0.69, and 0.84, respectively indicating good predictive performance for the small training set.


Key words: Negative selection algorithm, MHC class-II peptides, artificial immune system, epitope, vaccine designing, human immunology.

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APA (2013). Prediction of MHC Class II binders/non-binders using negative selection algorithm in vaccine designing. Journal of Bioinformatics and Sequence Analysis, 5(2), 16-24.
Chicago S. S. Soam, Feroz Khan, Bharat Bhasker and B. N. Mishra. "Prediction of MHC Class II binders/non-binders using negative selection algorithm in vaccine designing." Journal of Bioinformatics and Sequence Analysis 5, no. 2 (2013): 16-24.
MLA S. S. Soam, et al. "Prediction of MHC Class II binders/non-binders using negative selection algorithm in vaccine designing." Journal of Bioinformatics and Sequence Analysis 5.2 (2013): 16-24.
DOI 10.5897/JBSA11.023

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