High-accuracy glycan de novo prediction for N- and O-linked glycopeptides across multiple fragmentation techniques

peer-reviewed · Nature Communications · 2026

peer-reviewed · Nature Communications · 2026. Qianqiu Zhang et al. N- and O-glycosylation are structurally diverse post-translational modifications that affect a range of…

Date	2026-09-10
Type	peer-reviewed
Venue	Nature Communications
Publisher	Springer Science and Business Media LLC
Contribution	adjacent
DOI	10.1038/s41467-026-77012-2
Citations (OpenAlex)	0
Venue 2-year citedness	17.60

Abstract

N- and O-glycosylation are structurally diverse post-translational modifications that affect a range of biological functions. Glycoproteomics faces substantial challenges, particularly in the analysis of O-glycans due to their diversity compared to that of N-glycans. In addition, tandem mass spectrometry data patterns exhibit variability between different fragmentation methods. Existing de novo algorithms often lack sensitivity and are limited to sceHCD fragmentation, restricting their practical application. To address these limitations, we introduce DeepGlycan, a deep-learning-based method for de novo glycopeptide sequencing that captures relationships between glycopeptide spectra and fragment ions from both N- and O-glycans. DeepGlycan achieves over 92% glycan recall and around 95% glycan precision on N-glycopeptide spectra generated using both sceHCD and EThcD. In addition, it enables O-glycan de novo sequencing without additional training. Beyond benchmarking, DeepGlycan identifies an O-glycopeptide in mouse heart tissue whose assignment is supported by exoglycosidase treatment and comparison with a synthetic standard.

Authors

1. Qianqiu Zhang · University of Waterloo
2. Zeping Mao · Bioinformatics Solutions Inc., University of Waterloo
3. Yuling Chen · Tsinghua University
4. Baozhen Shan · Bioinformatics Solutions Inc., Western University
5. Haiteng Deng · Tsinghua University, Yangtze Delta Region Institute of Tsinghua University
6. Ming Li · Bioinformatics Solutions Inc., Central China Institute of Artificial Intelligence, Peng Cheng Laboratory, University of Waterloo, University of Western Ontario

Methods and tools

• DeepGlycan: Deep-learning de novo glycopeptide sequencing that learns the relationship between glycopeptide spectra and fragment ions for both N- and O-glycans, rather than being tied to one fragmentation method. Reports over 92% glycan recall and about 95% precision on N-glycopeptide spectra under both sceHCD and EThcD, and sequences O-glycans with no additional training. Identifies an O-glycopeptide in mouse heart tissue confirmed by exoglycosidase treatment and a synthetic standard.

Data deposited

• High-Accuracy De Novo Prediction for N- and O-linked Glycopeptides Across Multiple Fragmentation Techniques (as deposited) · MSV000096767

Data used

• Mouse glycoproteome - pGlyco 2 (as deposited) · PXD005555
• Mouse glycoproteome - pGlyco 2 (PXD005553) (as deposited) · PXD005553
• Mouse glycoproteome: Heart - pGlyco 2 (as deposited) · PXD005413
• Mouse glycoproteome: Kidney - pGlyco 2 (as deposited) · PXD005412
• Proteome-wide discovery of human brain protein glycoforms and glycan modification changes in Alzheimer’s disease (as deposited) · PXD045001
• Role of N-glycosylation in FcRIIIa interaction with IgG (as deposited) · PXD035846

Cites (5)

• Deep Learning Methods for De Novo Peptide Sequencing (2024) both
• Mitigating the missing-fragmentation problem in de novo peptide sequencing with a two-stage graph-based deep learning model (2023) both
• Glycopeptide database search and de novo sequencing with PEAKS GlycanFinder enable highly sensitive glycoproteomics (2023) both
• pNovo: De novo Peptide Sequencing and Identification Using HCD Spectra (2010) both
• De Novo Peptide Sequencing and Identification with Precision Mass Spectrometry (2007) both

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