title: Towards universal structure-based prediction of class II MHC epitopes for diverse allotypes.
abstract: The binding of peptide fragments of antigens to class II MHC proteins is a crucial step in initiating a helper T cell immune response. The discovery of these peptide epitopes is important for understanding the normal immune response and its misregulation in autoimmunity and allergies and also for vaccine design. In spite of their biomedical importance, the high diversity of class II MHC proteins combined with the large number of possible peptide sequences make comprehensive experimental determination of epitopes for all MHC allotypes infeasible. Computational methods can address this need by predicting epitopes for a particular MHC allotype. We present a structure-based method for predicting class II epitopes that combines molecular mechanics docking of a fully flexible peptide into the MHC binding cleft followed by binding affinity prediction using a machine learning classifier trained on interaction energy components calculated from the docking solution. Although the primary advantage of structure-based prediction methods over the commonly employed sequence-based methods is their applicability to essentially any MHC allotype, this has not yet been convincingly demonstrated. In order to test the transferability of the prediction method to different MHC proteins, we trained the scoring method on binding data for DRB1*0101 and used it to make predictions for multiple MHC allotypes with distinct peptide binding specificities including representatives from the other human class II MHC loci, HLA-DP and HLA-DQ, as well as for two murine allotypes. The results showed that the prediction method was able to achieve significant discrimination between epitope and non-epitope peptides for all MHC allotypes examined, based on AUC values in the range 0.632-0.821. We also discuss how accounting for peptide binding in multiple registers to class II MHC largely explains the systematically worse performance of prediction methods for class II MHC compared with those for class I MHC based on quantitative prediction performance estimates for peptide binding to class II MHC in a fixed register.
authors.affiliation: Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Scottsdale, Arizona, United States of America. bordner.andrew@mayo.edu
authors.forename: Andrew J
authors.initials: AJ
authors.lastname: Bordner
mesh_terms: D000595:Amino Acid Sequence; D000818:Animals; D019540:Area Under Curve; D000939:Epitopes / Q000737:chemistry; D005802:Genes, MHC Class II; D006682:HLA-DP Antigens / Q000737:chemistry; D006683:HLA-DQ Antigens / Q000737:chemistry; D000949:Histocompatibility Antigens Class II / Q000737:chemistry* / Q000276:immunology*; D006801:Humans; D020128:Inhibitory Concentration 50; D051379:Mice; D008969:Molecular Sequence Data; D009010:Monte Carlo Method; D010455:Peptides / Q000737:chemistry; D011485:Protein Binding; D015203:Reproducibility of Results
chemical_list: D000939:Epitopes; D006682:HLA-DP Antigens; D006683:HLA-DQ Antigens; D000949:Histocompatibility Antigens Class II; D010455:Peptides