Prediction of promiscuous peptides that bind HLA class I molecules

doi:doi:10.1046/j.1440-1711.2002.01088.x

Immunology and Cell Biology (2002) 80, 280–285; doi:10.1046/j.1440-1711.2002.01088.x

Prediction of promiscuous peptides that bind HLA class I molecules

Vladimir Brusic¹, Nikolai Petrovsky², Guanglan Zhang¹ and Vladimir B Bajic¹

¹ Kent Ridge Digital Labs, Singapore
² National Bioinformatics Centre, University of Canberra and National Health Sciences Centre, Canberra Hospital, Woden, Australian Capital Territory, Australia

Correspondence: V Brusic, Kent Ridge Digital Labs, 21 Heng Mui Keng Terrace, Singapore 119613, Singapore. Email: vladimir@krdl.org.sg

Received 14 February 2002; Accepted 14 February 2002.

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Abstract

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.