Deep learning enables direct HLA typing from immunopeptidomics data

Pilz, M.; Scheid, J.; Bauer, A.; Lemke, S.; Sachsenberg, T.; Bauer, J.; Nelde, A.; Stadelmaier, J.; Walter, A.; Rammensee, H.-G.; Nahnsen, S.; Kohlbacher, O.; Walz, J. S.

doi:10.64898/2026.04.08.717021

bioRxiv · 10.64898/2026.04.08.717021

Deep learning enables direct HLA typing from immunopeptidomics data

Pilz, M.·Scheid, J.·Bauer, A.·Lemke, S.·Sachsenberg, T.·Bauer, J.·Nelde, A.·Stadelmaier, J.·Walter, A.·Rammensee, H.-G.·Nahnsen, S.·Kohlbacher, O.·Walz, J. S.

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Abstract

The immune system eliminates malignant and infected cells through T-cell-mediated recognition of peptides presented by human leukocyte antigen molecules. Mass spectrometry-based immunopeptidomics enables unbiased identification of naturally presented HLA-restricted peptides and has become central to the development of T-cell-based immunotherapies. However, immunopeptidomics data reflects the combined peptide presentation of multiple HLA alleles, and determining which allotypes are represented in this multi-allelic complexity remains an unmet computational challenge. Here, we introduce immunotype, a deep learning-based ensemble predictor for HLA class I allotyping directly from immunopeptidomics data. Immunotype integrates peptide and HLA protein sequence information through transformer encoders and a graph neural network, complemented by a curated mono-allelic reference of known peptide-HLA binding preferences. Immunotype achieves an overall accuracy of 87.2% at protein-level resolution across diverse tissues and thereby enables rapid, cost-effective HLA typing of large-scale immunopeptidomics datasets.

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Pilz, M., Scheid, J., Bauer, A., Lemke, S., Sachsenberg, T., Bauer, J., Nelde, A., Stadelmaier, J., Walter, A., Rammensee, H.-G., Nahnsen, S., Kohlbacher, O., Walz, J. S.. 2026-04-10. Deep learning enables direct HLA typing from immunopeptidomics data. https://doi.org/10.64898/2026.04.08.717021

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