NetMHCpan-4.0: Improved Peptide-MHC Class I Interaction Predictions Integrating Eluted Ligand and Peptide Binding Affinity Data.
Jurtz, V., Paul, S., Andreatta, M., Marcatili, P., Peters, B. and Nielsen, M.
Department of Bio and Health Informatics, Technical University of Denmark, DK-2800 Lyngby, Denmark.
Division of Vaccine Discovery, La Jolla Institute for Allergy and Immunology, La Jolla, CA 92037; and.
Instituto de Investigaciones Biotecnologicas, Universidad Nacional de San Martin, CP1650 San Martin, Argentina.
Department of Bio and Health Informatics, Technical University of Denmark, DK-2800 Lyngby, Denmark; mniel@cbs.dtu.dk.
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.
Journal of Immunology : en prensa (2017)