Author: Bravi, Barbara; Tubiana, Jérôme; Cocco, Simona; Monasson, Rémi; Mora, Thierry; Walczak, Aleksandra M.
Title: Flexible machine learning prediction of antigen presentation for rare and common HLA-I alleles Cord-id: h71imtzl Document date: 2020_9_19
ID: h71imtzl
Snippet: 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 specifi
Document: 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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