NetMHCpan-4.0: Improved Peptide–MHC Class I Interaction Predictions Integrating Eluted Ligand and Peptide Binding Affinity Data

TLDR

Cytotoxic T cells recognize defective cells by binding peptides presented on MHC class I molecules, making peptide–MHC binding the most selective step in antigen presentation; thus predicting this interaction has become a key focus, with recent mass‑spectrometry data revealing naturally presented peptide characteristics. We present NetMHCpan‑4.0, a method trained on both binding affinity and eluted ligand data. NetMHCpan‑4.0 integrates binding affinity and eluted ligand information to improve peptide–MHC class I interaction predictions. Benchmarking shows NetMHCpan‑4.0 outperforms state‑of‑the‑art methods for identifying naturally processed ligands, cancer neoantigens, and T cell epitopes.

Abstract

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.