Incorporating antigen processing into CD4+ T cell epitope prediction with integer linear programming

CD4+ T-cell receptors recognize peptide-MHCII complexes displayed on the surface of antigen-presenting cells to induce an immune response. A fundamental problem in immunology is to characterize which peptides (i.e., epitopes) in an antigen induce such a response; this is the problem of computational epitope prediction. To be presented in the form of peptide-MHCII complex, peptides must satisfy two important criteria: they should be processed from an antigen to be available in the pool of peptides to which MHCII can bind and should have a sufficiently high binding affinity to MHCII molecules to form stable complexes. This latter phenomenon has been studied widely and used almost exclusively for epitope prediction. In prior work we have developed methods for modeling antigen processing and have shown that it has significant predictive power in predicting epitopes. In this paper, we propose an integer linear programming (ILP) approach to combine the contributions of antigen processing and peptide binding that provides a holistic and flexible framework for epitope prediction. We validate our results on data sets comprising of antigens associated with tumors and pathogens and show consistent enrichment and improvement in accuracy over other methods.