Footprints of antigen processing boost MHC class II natural ligand binding predictions

Abstract

Major Histocompatibility complex class II (MHC-II) molecules present peptide fragments to T cells for immune recognition. Current predictors for peptide:MHC-II binding are trained on binding affinity data, generated in-vitro and therefore lacking information about antigen processing. For the first time, we here describe prediction models of peptide:MHC-II binding trained directly on naturally eluted peptides, and show that these, in addition to peptide binding to the MHC, incorporate identifiable rules of antigen processing. In fact, we observed detectable signals of protease cleavage at defined positions of the peptides. We also hypothesize a role of the length of the terminal ligand protrusions for trimming the peptide to the epitope presented. The results of integrating binding affinity and eluted ligand data in a combined model demonstrate improved performance for the prediction of MHC-II ligands, and foreshadow a new generation of improved peptide:MHC-II prediction tools of considerable importance for understanding and manipulating immune responses.