Predicting peptides bound to I-Ag7 class II histocompatibility molecules using a novel expectation-maximization alignment algorithm

Kuan Y. Chang, Anish Suri, Emil R. Unanue

Research output: Contribution to journalArticlepeer-review

14 Scopus citations

Abstract

The useful structural features of class II MHC molecules are rarely integrated into T-cell epitope predictions. We propose an approach that applies a novel expectation-maximization algorithm to align the naturally processed peptides selected by the class II MHC I-Ag7 molecule - focusing on the five MHC-specific anchor positions. Based on the alignment profile, log of odds (LOD) scores supplemented with the Laplace plus-one pseudocounts method are applied to identify the potential T-cell epitopes. In addition, an innovative computational concept of hindering residues using statistical and structural information is developed to refine the prediction. Performance analysis by receiver operating characteristics statistics and the experimental validation of the LOD scores demonstrate the accuracy of our predictive model. Furthermore, our model successfully predicts T-cell epitopes of hen egg-white lysozyme protein antigen. Our study provides a framework for predicting T-cell epitopes in class II MHC molecules.

Original languageEnglish
Pages (from-to)367-377
Number of pages11
JournalProteomics
Volume7
Issue number3
DOIs
StatePublished - Feb 2007

Keywords

  • Class II MHC molecules
  • EM algorithm
  • Epitope prediction
  • Hindering residues
  • Log of odds

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