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NetTepi: an integrated method for the prediction of T cell epitopes

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Multiple factors determine the ability of a peptide to elicit a cytotoxic T cell lymphocyte response. Binding to a major histocompatibility complex class I (MHC-I) molecule is one of the most essential factors, as no peptide can become a T cell epitope unless presented on the cell surface in complex with an MHC-I molecule. As such, peptide-MHC (pMHC) binding affinity predictors are currently the premier methods for T cell epitope prediction, and these prediction methods have been shown to have high predictive performances in multiple studies. However, not all MHC-I binders are T cell epitopes, and multiple studies have investigated what additional factors are important for determining the immunogenicity of a peptide. A recent study suggested that pMHC stability plays an important role in determining if a peptide can become a T cell epitope. Likewise, a T cell propensity model has been proposed for identifying MHC binding peptides with amino acid compositions favoring T cell receptor interactions. In this study, we investigate if improved accuracy for T cell epitope discovery can be achieved by integrating predictions for pMHC binding affinity, pMHC stability, and T cell propensity. We show that a weighted sum approach allows pMHC stability and T cell propensity predictions to enrich pMHC binding affinity predictions. The integrated model leads to a consistent and significant increase in predictive performance and we demonstrate how this can be utilized to decrease the experimental workload of epitope screens. The final method, NetTepi, is publically available at

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This project has been funded in whole or in part with federal funds from the National Institutes of Allergy and Infectious Diseases, National Institutes of Health, and Department of Health and Human Services, under Contract No. HHSN272201200010C. MN is a researcher at the Argentinean national research council (CONICET).

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Correspondence to Morten Nielsen.

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Fig. S1

Bar-plot showing the accumulated number of wins for each prediction method as a function of the predicted binding affinity for each epitope in the evaluation data set. (PDF 96 kb)

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Trolle, T., Nielsen, M. NetTepi: an integrated method for the prediction of T cell epitopes. Immunogenetics 66, 449–456 (2014).

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