期刊
IMMUNOLOGY AND CELL BIOLOGY
卷 80, 期 3, 页码 280-285出版社
WILEY
DOI: 10.1046/j.1440-1711.2002.01088.x
关键词
hidden Markov models; HLA allele; immunoinformatics; peptide binding; predictive modelling
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. multipred is 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. multipred replaces 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.
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