Prediction of promiscuous peptides that bind HLA class I molecules.
Promiscuous T-cell epitopes make ideal targets for vaccine development. We report here a computational system, MULTIPRED, for the prediction of peptide binding to the HLA-A2 supertype. It combines a novel representation of peptide/MHC interactions with a hidden Markov model as the prediction algorithm. MULTIPREDis both sensitive and specific, and demonstrates high accuracy of peptide-binding predictions for HLA-A*0201, *0204, and *0205 alleles, good accuracy for *0206 allele, and marginal accuracy for *0203 allele. MULTIPREDreplaces earlier requirements for individual prediction models for each HLA allelic variant and simplifies computational aspects of peptide-binding prediction. Preliminary testing indicates that MULTIPRED can predict peptide binding to HLA-A2 supertype molecules with high accuracy, including those allelic variants for which no experimental binding data are currently available.
Brusic
Vladimir
V
Kent Ridge Digital Labs, Singapore. vladimir@krdl.org.sg
Petrovsky
Nikolai
N
Zhang
Guanglan
G
Bajic
Vladimir B
VB
D020816:Amino Acid Motifs; D000595:Amino Acid Sequence; D001665:Binding Sites; D019295:Computational Biology / Q000379:methods*; D005544:Forecasting; D005805:Genes, MHC Class I; D015789:HLA-A2 Antigen / Q000737:chemistry / Q000235:genetics* / Q000378:metabolism*; D006801:Humans; D008969:Molecular Sequence Data; D010446:Peptide Fragments / Q000276:immunology / Q000378:metabolism*; D012680:Sensitivity and Specificity
D015789:HLA-A2 Antigen; D010446:Peptide Fragments