Footprints of antigen processing boost MHC class II natural ligand predictions

Genome Med. 2018 Nov 16;10(1):84. doi: 10.1186/s13073-018-0594-6.


Background: Major histocompatibility complex class II (MHC-II) molecules present peptide fragments to T cells for immune recognition. Current predictors for peptide to MHC-II binding are trained on binding affinity data, generated in vitro and therefore lacking information about antigen processing.

Methods: We generate prediction models of peptide to MHC-II binding trained with naturally eluted ligands derived from mass spectrometry in addition to peptide binding affinity data sets.

Results: We show that integrated prediction models incorporate identifiable rules of antigen processing. In fact, we observed detectable signals of protease cleavage at defined positions of the ligands. We also hypothesize a role of the length of the terminal ligand protrusions for trimming the peptide to the MHC presented ligand.

Conclusions: 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 T cell epitopes and foreshadow a new generation of improved peptide to MHC-II prediction tools accounting for the plurality of factors that determine natural presentation of antigens.

Keywords: Antigen processing; Binding predictions; Eluted ligands; MHC-II; Machine learning; Mass spectrometry; Neural networks; T cell epitope.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Animals
  • Antigen Presentation
  • Cell Line
  • HLA-DR Antigens / metabolism*
  • Histocompatibility Antigens Class I / metabolism*
  • Humans
  • Ligands
  • Mice
  • Models, Theoretical*
  • Peptides / metabolism*


  • HLA-DR Antigens
  • Histocompatibility Antigens Class I
  • Ligands
  • Peptides