Unraveling the glycosylated immunopeptidome with HLA-Glyco

Georges Bedran, Daniel A. Polasky, Yi Hsiao, Fengchao Yu, Felipe Veiga Leprevost, Javier A. Alfaro, Marcin Cieslik and Alexey I. Nesvizhskii ()
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Georges Bedran: University of Gdansk
Daniel A. Polasky: University of Michigan
Yi Hsiao: Department of Computational Medicine and Bioinformatics
Fengchao Yu: University of Michigan
Felipe Veiga Leprevost: University of Michigan
Javier A. Alfaro: University of Gdansk
Marcin Cieslik: University of Michigan
Alexey I. Nesvizhskii: University of Michigan

Nature Communications, 2023, vol. 14, issue 1, 1-12

Abstract: Abstract Recent interest in targeted therapies has been sparked by the study of MHC-associated peptides (MAPs) that undergo post-translational modifications (PTMs), particularly glycosylation. In this study, we introduce a fast computational workflow that merges the MSFragger-Glyco search algorithm with a false discovery rate control for glycopeptide analysis from mass spectrometry-based immunopeptidome data. By analyzing eight large-scale publicly available studies, we find that glycosylated MAPs are predominantly presented by MHC class II. Here, we present HLA-Glyco, a comprehensive resource containing over 3,400 human leukocyte antigen (HLA) class II N-glycopeptides from 1,049 distinct protein glycosylation sites. This resource provides valuable insights, including high levels of truncated glycans, conserved HLA-binding cores, and differences in glycosylation positional specificity between HLA allele groups. We integrate the workflow within the FragPipe computational platform and provide HLA-Glyco as a free web resource. Overall, our work provides a valuable tool and resource to aid the nascent field of glyco-immunopeptidomics.

Date: 2023
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DOI: 10.1038/s41467-023-39270-2

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