Computational Immunology: HLA-peptide Binding Prediction

  • Pandjassarame Kangueane
  • Bing Zhao
  • Meena K. Sakharkar


HLA molecules are immune proteins that play an important role in T-cell mediated immune response. They bind short 8-20 residues long peptides from antigen proteins to induce immune response. Therefore, the binding of short antigen peptides to HLA molecules is the rate limiting step in T-cell mediated immune response. Several constructs of overlapping short peptides can be designed from a given protein antigen sequence. The number of overlapping peptides is large for systematic experimental testing. Moreover, HLA molecules are highly polymorphic and more than 1500 HLA alleles are known among the human population. Thus, the binding of short peptides to HLA is combinatorial and specific. The binding can be studied using expensive and laborious competitive binding assays. Alternatively, prediction of peptide binding to HLA molecules is highly useful. Efficient prediction models enable systematic scanning of candidate peptides in an effective manner. Here, we describe some commonly used prediction models.


Support Vector Machine Hide Markov Model Peptide Binding Binding Free Energy Stepwise Discriminant Analysis 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  • Pandjassarame Kangueane
    • 1
  • Bing Zhao
    • 2
  • Meena K. Sakharkar
    • 2
  1. 1.Biomedical InformaticsIndia
  2. 2.Nanyang Technological UniversitySingapore

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