NetTCR: sequence-based prediction of TCR binding to peptide-MHC complexes using convolutional neural networks

Jurtz, V. I.; Jessen, L. E.; Bentzen, A. K.; Jespersen, M. C.; MAHAJAN, S.; Vita, R.; Jensen, K. K.; Marcatili, P.; Hadrup, S. R.; Peters, B.; Nielsen, M.

doi:10.1101/433706

bioRxiv · 10.1101/433706

NetTCR: sequence-based prediction of TCR binding to peptide-MHC complexes using convolutional neural networks

Jurtz, V. I.·Jessen, L. E.·Bentzen, A. K.·Jespersen, M. C.·MAHAJAN, S.·Vita, R.·Jensen, K. K.·Marcatili, P.·Hadrup, S. R.·Peters, B.·Nielsen, M.

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Abstract

Predicting epitopes recognized by cytotoxic T cells has been a long standing challenge within the field of immuno- and bioinformatics. While reliable predictions of peptide binding are available for most Major Histocompatibility Complex class I (MHCI) alleles, prediction models of T cell receptor (TCR) interactions with MHC class I-peptide complexes remain poor due to the limited amount of available training data. Recent next generation sequencing projects have however generated a considerable amount of data relating TCR sequences with their cognate HLA-peptide complex target. Here, we utilize such data to train a sequence-based predictor of the interaction between TCRs and peptides presented by the most common human MHCI allele, HLA-A*02:01. Our model is based on convolutional neural networks, which are especially designed to meet the challenges posed by the large length variations of TCRs. We show that such a sequence-based model allows for the identification of TCRs binding a given cognate peptide-MHC target out of a large pool of non-binding TCRs.

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Jurtz, V. I., Jessen, L. E., Bentzen, A. K., Jespersen, M. C., MAHAJAN, S., Vita, R., Jensen, K. K., Marcatili, P., Hadrup, S. R., Peters, B., Nielsen, M.. 2018-10-03. NetTCR: sequence-based prediction of TCR binding to peptide-MHC complexes using convolutional neural networks. https://doi.org/10.1101/433706

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