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Glycopeptide database search and de novo sequencing with PEAKS GlycanFinder enable highly sensitive glycoproteomics

Author

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  • Weiping Sun

    (Bioinformatics Solutions Inc.)

  • Qianqiu Zhang

    (David R. Cheriton School of Computer Science, University of Waterloo)

  • Xiyue Zhang

    (Bioinformatics Solutions Inc.)

  • Ngoc Hieu Tran

    (Bioinformatics Solutions Inc.
    David R. Cheriton School of Computer Science, University of Waterloo)

  • M. Ziaur Rahman

    (Bioinformatics Solutions Inc.)

  • Zheng Chen

    (Bioinformatics Solutions Inc.)

  • Chao Peng

    (BaizhenBio Inc.
    Wuhan BioBank)

  • Jun Ma

    (Bioinformatics Solutions Inc.)

  • Ming Li

    (David R. Cheriton School of Computer Science, University of Waterloo
    Henan Academy of Sciences)

  • Lei Xin

    (Bioinformatics Solutions Inc.)

  • Baozhen Shan

    (Bioinformatics Solutions Inc.)

Abstract

Here we present GlycanFinder, a database search and de novo sequencing tool for the analysis of intact glycopeptides from mass spectrometry data. GlycanFinder integrates peptide-based and glycan-based search strategies to address the challenge of complex fragmentation of glycopeptides. A deep learning model is designed to capture glycan tree structures and their fragment ions for de novo sequencing of glycans that do not exist in the database. We performed extensive analyses to validate the false discovery rates (FDRs) at both peptide and glycan levels and to evaluate GlycanFinder based on comprehensive benchmarks from previous community-based studies. Our results show that GlycanFinder achieved comparable performance to other leading glycoproteomics softwares in terms of both FDR control and the number of identifications. Moreover, GlycanFinder was also able to identify glycopeptides not found in existing databases. Finally, we conducted a mass spectrometry experiment for antibody N-linked glycosylation profiling that could distinguish isomeric peptides and glycans in four immunoglobulin G subclasses, which had been a challenging problem to previous studies.

Suggested Citation

  • Weiping Sun & Qianqiu Zhang & Xiyue Zhang & Ngoc Hieu Tran & M. Ziaur Rahman & Zheng Chen & Chao Peng & Jun Ma & Ming Li & Lei Xin & Baozhen Shan, 2023. "Glycopeptide database search and de novo sequencing with PEAKS GlycanFinder enable highly sensitive glycoproteomics," Nature Communications, Nature, vol. 14(1), pages 1-15, December.
  • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-39699-5
    DOI: 10.1038/s41467-023-39699-5
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    References listed on IDEAS

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    1. Mathias Wilhelm & Daniel P. Zolg & Michael Graber & Siegfried Gessulat & Tobias Schmidt & Karsten Schnatbaum & Celina Schwencke-Westphal & Philipp Seifert & Niklas Andrade Krätzig & Johannes Zerweck &, 2021. "Deep learning boosts sensitivity of mass spectrometry-based immunopeptidomics," Nature Communications, Nature, vol. 12(1), pages 1-12, December.
    2. Mathias Wilhelm & Daniel P. Zolg & Michael Graber & Siegfried Gessulat & Tobias Schmidt & Karsten Schnatbaum & Celina Schwencke-Westphal & Philipp Seifert & Niklas Andrade Krätzig & Johannes Zerweck &, 2021. "Author Correction: Deep learning boosts sensitivity of mass spectrometry-based immunopeptidomics," Nature Communications, Nature, vol. 12(1), pages 1-1, December.
    3. Florian Meier & Niklas D. Köhler & Andreas-David Brunner & Jean-Marc H. Wanka & Eugenia Voytik & Maximilian T. Strauss & Fabian J. Theis & Matthias Mann, 2021. "Deep learning the collisional cross sections of the peptide universe from a million experimental values," Nature Communications, Nature, vol. 12(1), pages 1-12, December.
    4. Siyuan Kong & Pengyun Gong & Wen-Feng Zeng & Biyun Jiang & Xinhang Hou & Yang Zhang & Huanhuan Zhao & Mingqi Liu & Guoquan Yan & Xinwen Zhou & Xihua Qiao & Mengxi Wu & Pengyuan Yang & Chao Liu & Weiqi, 2022. "pGlycoQuant with a deep residual network for quantitative glycoproteomics at intact glycopeptide level," Nature Communications, Nature, vol. 13(1), pages 1-17, December.
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    Cited by:

    1. Yi Yang & Qun Fang, 2024. "Prediction of glycopeptide fragment mass spectra by deep learning," Nature Communications, Nature, vol. 15(1), pages 1-12, December.

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