Peptide binding motif predictive algorithms correspond with experimental binding of leukemia vaccine candidate peptides to HLA-A*0201 molecules.
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.
Department of Medicine and Program in Molecular Pharmacology and Chemistry, Memorial Sloan Kettering Cancer Center, New York, NY 10021, USA.
Marta
M
Gomez-Nunez
Javier
J
Pinilla-Ibarz
Tao
T
Dao
Rena J
RJ
May
Mary
M
Pao
Jaspreet S
JS
Jaggi
David A
DA
Scheinberg
D000465:Algorithms; D000595:Amino Acid Sequence; D001665:Binding Sites; D019496:Cancer Vaccines / Q000276:immunology*; D002460:Cell Line; D015234:HLA-A Antigens / Q000276:immunology*; D015789:HLA-A2 Antigen; D006801:Humans; D008285:Major Histocompatibility Complex; D010446:Peptide Fragments / Q000276:immunology; D011485:Protein Binding; D015203:Reproducibility of Results
D019496:Cancer Vaccines; D015234:HLA-A Antigens; C435939:HLA-A*02:01 antigen; D015789:HLA-A2 Antigen; D010446:Peptide Fragments