Peptide binding motif predictive algorithms correspond with experimental binding of leukemia vaccine candidate peptides to HLA-A*0201 molecules Academic Article uri icon


MeSH Major

  • Cancer Vaccines
  • HLA-A Antigens


  • The ability to reliably identify the peptides that can bind to MHC molecules is of practical importance for rapid vaccine development. Several computer-based prediction methods have been applied to study the interaction of MHC class I/peptide binding. Here we have compared the binding of peptides predicted by three algorithms (BIMAS, SYFPEITHI and Rankpep) to the binding of the peptides to HLA-A*0201 molecules in vitro, assessed using a MHC stabilization assay on live T2 cells. Fifty HLA-A*0201 peptides were selected from several target oncoproteins: Wilms' tumor protein (WT1), native and imatinib-mutated bcr-abl p210, JAK2 protein and Ewing's sarcoma fusion protein type 1. The sensitivity and specificity of BIMAS, SYFPEITHI and Rankpep respectively, were: 86%, and 82%; 75% and 73%; 64% and 82%. Combining two or more computer methods did not appear to significantly improve the predictive value.

publication date

  • October 2006



  • Academic Article



  • eng

Digital Object Identifier (DOI)

  • 10.1016/j.leukres.2006.02.010

PubMed ID

  • 16533527

Additional Document Info

start page

  • 1293

end page

  • 8


  • 30


  • 10