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bioRxiv · 10.1101/2020.04.25.061069

Flexible machine learning prediction of antigen presentation for rare and common HLA-I alleles

Bravi, B.·Tubiana, J.·Cocco, S.·Monasson, R.·Mora, T.·Walczak, A. M.
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Abstract

The recent increase of immunopeptidomic data, obtained by mass spectrometry or binding assays, opens unprecedented possibilities for investigating endogenous antigen presentation by the highly polymorphic human leukocyte antigen class I (HLA-I) protein. We introduce a flexible and easily interpretable peptide presentation prediction method, RBM-MHC. We validate its performance as a predictor of cancer neoantigens and viral epitopes and we use it to reconstruct peptide motifs presented on specific HLA-I molecules. By benchmarking RBM-MHC performance on a wide range of HLA-I alleles, we show its importance to improve prediction accuracy for rarer alleles.

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Bravi, B., Tubiana, J., Cocco, S., Monasson, R., Mora, T., Walczak, A. M.. 2020-04-25. Flexible machine learning prediction of antigen presentation for rare and common HLA-I alleles. https://doi.org/10.1101/2020.04.25.061069

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