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PLoS One. 2018 Nov 6;13(11):e0206654. doi: 10.1371/journal.pone.0206654. eCollection 2018.

Predicting CD4 T-cell epitopes based on antigen cleavage, MHCII presentation, and TCR recognition.

Author information

1
Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA, United States of America.
2
Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA, United States of America.
3
Graduate Group in Biophysics, University of California at San Francisco, San Francisco, CA, United States of America.
4
Department of Immunology, Weizmann Institute of Science, Rehovot, Israel.
5
California Institute for Quantitative Biosciences (QB3), University of California, San Francisco, San Francisco, CA, United States of America.
6
Uniformed Services University of the Health Sciences, Bethesda, MD, United States of America.
7
Bayer HealthCare, San Francisco, CA, United States of America.

Abstract

Accurate 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).

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