RT Journal Article
SR Electronic
T1 NetMHCpan-4.0: Improved Peptide–MHC Class I Interaction Predictions Integrating Eluted Ligand and Peptide Binding Affinity Data
JF The Journal of Immunology
JO J. Immunol.
FD American Association of Immunologists
SP 3360
OP 3368
DO 10.4049/jimmunol.1700893
VO 199
IS 9
A1 Jurtz, Vanessa
A1 Paul, Sinu
A1 Andreatta, Massimo
A1 Marcatili, Paolo
A1 Peters, Bjoern
A1 Nielsen, Morten
YR 2017
UL http://www.jimmunol.org/content/199/9/3360.abstract
AB Cytotoxic T cells are of central importance in the immune system’s response to disease. They recognize defective cells by binding to peptides presented on the cell surface by MHC class I molecules. Peptide binding to MHC molecules is the single most selective step in the Ag-presentation pathway. Therefore, in the quest for T cell epitopes, the prediction of peptide binding to MHC molecules has attracted widespread attention. In the past, predictors of peptide–MHC interactions have primarily been trained on binding affinity data. Recently, an increasing number of MHC-presented peptides identified by mass spectrometry have been reported containing information about peptide-processing steps in the presentation pathway and the length distribution of naturally presented peptides. In this article, we present NetMHCpan-4.0, a method trained on binding affinity and eluted ligand data leveraging the information from both data types. Large-scale benchmarking of the method demonstrates an increase in predictive performance compared with state-of-the-art methods when it comes to identification of naturally processed ligands, cancer neoantigens, and T cell epitopes.