AbstractAccurate predictions of T-cell epitopes would be useful for designing vaccines, immunotherapies for cancer and autoimmune diseases, and improved protein therapies. The humoral immune response involves uptake of antigens by antigen presenting cells (APCs), APC processing and presentation of peptides on MHC class II (pMHCII), and T-cell receptor (TCR) recognition of pMHCII complexes. Most in silico methods predict only peptide-MHCII binding, resulting in significant over-prediction of CD4 T-cell epitopes. We present a method, ITCell, for prediction of T-cell epitopes within an input protein antigen sequence for given MHCII and TCR sequences. The method integrates information about three stages of the immune response pathway: antigen cleavage, MHCII presentation, and TCR recognition. First, antigen cleavage sites are predicted based on the cleavage profiles of cathepsins S, B, and H. Second, for each 12-mer peptide in the antigen sequence we predict whether it will bind to a given MHCII, based on the scores of modeled peptide-MHCII complexes. Third, we predict whether or not any of the top scoring peptide-MHCII complexes can bind to a given TCR, based on the scores of modeled ternary peptide-MHCII-TCR complexes and the distribution of predicted cleavage sites. Our benchmarks consist of epitope predictions generated by this algorithm, checked against 20 peptide-MHCII-TCR crystal structures, as well as epitope predictions for four peptide-MHCII-TCR complexes with known epitopes and TCR sequences but without crystal structures. ITCell successfully identified the correct epitopes as one of the 20 top scoring peptides for 22 of 24 benchmark cases. To validate the method using a clinically relevant application, we utilized five factor VIII-specific TCR sequences from hemophilia A subjects who developed an immune response to factor VIII replacement therapy. The known HLA-DR1-restricted factor VIII epitope was among the six top-scoring factor VIII peptides predicted by ITCall to bind HLA-DR1 and all five TCRs. Our integrative approach is more accurate than current single-stage epitope prediction algorithms applied to the same benchmarks. It is freely available as a web server (http://salilab.org/itcell).Author summaryKnowledge of T-cell epitopes is useful for designing vaccines, improving cancer immunotherapy, studying autoimmune diseases, and engineering protein replacement therapies. Unfortunately, experimental methods for identification of T-cell epitopes are slow, expensive, and not always applicable. Thus, a more accurate computational method for prediction of T-cell epitopes needs to be developed. While the T-cell response to extracellular antigens proceeds through multiple stages, current computational methods rely only on the prediction of peptide binding affinity to an MHCII receptor on antigen presenting cells, resulting in a relatively high number of false-positive predictions of T-cell epitopes within protein antigens. We developed an integrative approach to predict T-cell epitopes that computationally combines information from three stages of the humoral immune response pathway: antigen cleavage, MHCII presentation, and TCR recognition, resulting in an increased accuracy of epitope predictions. This method was applied to predict epitopes within blood coagulation factor VIII (FVIII) that were recognized by TCRs from hemophilia A subjects who developed an anti-FVIII antibody response. The correct epitope was predicted after modeling all possible 12-mer FVIII peptides bound in ternary complexes with the relevant MHCII (HLA-DR1) and each of five experimentally determined FVIII-specific TCR sequences.
Predicting CD4 T-cell epitopes based on antigen cleavage, MHCII presentation, and TCR recognition