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

Leuk Res. 2006 Oct;30(10):1293-8. doi: 10.1016/j.leukres.2006.02.010. Epub 2006 Mar 14.


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 types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Amino Acid Sequence
  • Binding Sites
  • Cancer Vaccines / immunology*
  • Cell Line
  • HLA-A Antigens / immunology*
  • HLA-A2 Antigen
  • Humans
  • Major Histocompatibility Complex
  • Peptide Fragments / immunology
  • Protein Binding
  • Reproducibility of Results


  • Cancer Vaccines
  • HLA-A Antigens
  • HLA-A*02:01 antigen
  • HLA-A2 Antigen
  • Peptide Fragments