Prediction of promiscuous peptides that bind HLA class I molecules

Vladimir Brusic, Nikolai Petrovsky, Guanglan Zhang, Vladimir B. Bajic

Research output: Contribution to journalReview article

86 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)280-285
Number of pages6
JournalImmunology and Cell Biology
Volume80
Issue number3
DOIs
Publication statusPublished - 26 Jun 2002
Externally publishedYes

Keywords

  • Hidden Markov models
  • HLA allele
  • Immunoinformatics
  • Peptide binding
  • Predictive modelling

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