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.
Gomez-Nunez
Marta
M
Department of Medicine and Program in Molecular Pharmacology and Chemistry, Memorial Sloan Kettering Cancer Center, New York, NY 10021, USA.
Pinilla-Ibarz
Javier
J
Dao
Tao
T
May
Rena J
RJ
Pao
Mary
M
Jaggi
Jaspreet S
JS
Scheinberg
David A
DA
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