Pan-Specific Prediction of Peptide-MHC Class I Complex Stability, a Correlate of T Cell Immunogenicity

J Immunol. 2016 Aug 15;197(4):1517-24. doi: 10.4049/jimmunol.1600582. Epub 2016 Jul 8.

Abstract

Binding of peptides to MHC class I (MHC-I) molecules is the most selective event in the processing and presentation of Ags to CTL, and insights into the mechanisms that govern peptide-MHC-I binding should facilitate our understanding of CTL biology. Peptide-MHC-I interactions have traditionally been quantified by the strength of the interaction, that is, the binding affinity, yet it has been shown that the stability of the peptide-MHC-I complex is a better correlate of immunogenicity compared with binding affinity. In this study, we have experimentally analyzed peptide-MHC-I complex stability of a large panel of human MHC-I allotypes and generated a body of data sufficient to develop a neural network-based pan-specific predictor of peptide-MHC-I complex stability. Integrating the neural network predictors of peptide-MHC-I complex stability with state-of-the-art predictors of peptide-MHC-I binding is shown to significantly improve the prediction of CTL epitopes. The method is publicly available at http://www.cbs.dtu.dk/services/NetMHCstabpan.

MeSH terms

  • Antigen Presentation / immunology*
  • Epitopes, T-Lymphocyte / immunology*
  • Histocompatibility Antigens Class I / chemistry
  • Histocompatibility Antigens Class I / immunology*
  • Histocompatibility Antigens Class I / metabolism
  • Humans
  • Lymphocyte Activation / immunology*
  • Neural Networks, Computer*
  • Peptides / chemistry
  • Peptides / immunology
  • Peptides / metabolism
  • Protein Stability

Substances

  • Epitopes, T-Lymphocyte
  • Histocompatibility Antigens Class I
  • Peptides