Prediction of supertype-specific HLA class I binding peptides using support vector machines

Guang Lan Zhang, Ivana Bozic, Chee Keong Kwoh, J. Thomas August, Vladimir Brusic

Research output: Journal PublicationArticlepeer-review

40 Citations (Scopus)


Experimental approaches for identifying T-cell epitopes are time-consuming, costly and not applicable to the large scale screening. Computer modeling methods can help to minimize the number of experiments required, enable a systematic scanning for candidate major histocompatibility complex (MHC) binding peptides and thus speed up vaccine development. We developed a prediction system based on a novel data representation of peptide/MHC interaction and support vector machines (SVM) for prediction of peptides that promiscuously bind to multiple Human Leukocyte Antigen (HLA, human MHC) alleles belonging to a HLA supertype. Ten-fold cross-validation results showed that the overall performance of SVM models is improved in comparison to our previously published methods based on hidden Markov models (HMM) and artificial neural networks (ANN), also confirmed by blind testing. At specificity 0.90, sensitivity values of SVM models were 0.90 and 0.92 for HLA-A2 and -A3 dataset respectively. Average area under the receiver operating curve (AROC) of SVM models in blind testing are 0.89 and 0.92 for HLA-A2 and -A3 datasets. AROC of HLA-A2 and -A3 SVM models were 0.94 and 0.95, validated using a full overlapping study of 9-mer peptides from human papillomavirus type 16 E6 and E7 proteins. In addition, a large-scale experimental dataset has been used to validate HLA-A2 and -A3 SVM models. The SVM prediction models were integrated into a web-based computational system MULTIPRED1, accessible at

Original languageEnglish
Pages (from-to)143-154
Number of pages12
JournalJournal of Immunological Methods
Issue number1-2
Publication statusPublished - 30 Mar 2007
Externally publishedYes


  • Human Leukocyte Antigen supertype
  • Promiscuous binding peptide
  • Support vector machines
  • T-cell epitope

ASJC Scopus subject areas

  • Immunology and Allergy
  • Immunology


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