Journal of
Bioinformatics and Sequence Analysis

  • Abbreviation: J. Bioinform. Seq. Anal.
  • Language: English
  • ISSN: 2141-2464
  • DOI: 10.5897/JBSA
  • Start Year: 2009
  • Published Articles: 42

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.
Email: [email protected]

  •  Accepted: 05 February 2013
  •  Published: 28 February 2013

Abstract

 

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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