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pGlycoQuant with a deep residual network for quantitative glycoproteomics at intact glycopeptide level

Author

Listed:
  • Siyuan Kong

    (Fudan University)

  • Pengyun Gong

    (Beihang University)

  • Wen-Feng Zeng

    (Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, CAS
    Max Planck Institute of Biochemistry)

  • Biyun Jiang

    (Fudan University)

  • Xinhang Hou

    (Beihang University)

  • Yang Zhang

    (Fudan University)

  • Huanhuan Zhao

    (Fudan University)

  • Mingqi Liu

    (Fudan University)

  • Guoquan Yan

    (Fudan University)

  • Xinwen Zhou

    (Fudan University)

  • Xihua Qiao

    (Beihang University)

  • Mengxi Wu

    (Fudan University)

  • Pengyuan Yang

    (Fudan University
    Fudan University)

  • Chao Liu

    (Beihang University
    Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, CAS)

  • Weiqian Cao

    (Fudan University
    Fudan University)

Abstract

Large-scale intact glycopeptide identification has been advanced by software tools. However, tools for quantitative analysis remain lagging behind, which hinders exploring the differential site-specific glycosylation. Here, we report pGlycoQuant, a generic tool for both primary and tandem mass spectrometry-based intact glycopeptide quantitation. pGlycoQuant advances in glycopeptide matching through applying a deep learning model that reduces missing values by 19–89% compared with Byologic, MSFragger-Glyco, Skyline, and Proteome Discoverer, as well as a Match In Run algorithm for more glycopeptide coverage, greatly expanding the quantitative function of several widely used search engines, including pGlyco 2.0, pGlyco3, Byonic and MSFragger-Glyco. Further application of pGlycoQuant to the N-glycoproteomic study in three different metastatic HCC cell lines quantifies 6435 intact N-glycopeptides and, together with in vitro molecular biology experiments, illustrates site 979-core fucosylation of L1CAM as a potential regulator of HCC metastasis. We expected further applications of the freely available pGlycoQuant in glycoproteomic studies.

Suggested Citation

  • 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.
    • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-35172-x
    DOI: 10.1038/s41467-022-35172-x
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    References listed on IDEAS

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    1. Johannes Stadlmann & Jasmin Taubenschmid & Daniel Wenzel & Anna Gattinger & Gerhard Dürnberger & Frederico Dusberger & Ulrich Elling & Lukas Mach & Karl Mechtler & Josef M. Penninger, 2017. "Comparative glycoproteomics of stem cells identifies new players in ricin toxicity," Nature, Nature, vol. 549(7673), pages 538-542, September.
    2. Yi Yang & Guoquan Yan & Siyuan Kong & Mengxi Wu & Pengyuan Yang & Weiqian Cao & Liang Qiao, 2021. "GproDIA enables data-independent acquisition glycoproteomics with comprehensive statistical control," Nature Communications, Nature, vol. 12(1), pages 1-15, December.
    3. Nicholas M. Riley & Alexander S. Hebert & Michael S. Westphall & Joshua J. Coon, 2019. "Capturing site-specific heterogeneity with large-scale N-glycoproteome analysis," Nature Communications, Nature, vol. 10(1), pages 1-13, December.
    4. Jianbo Pan & Yingwei Hu & Shisheng Sun & Lijun Chen & Michael Schnaubelt & David Clark & Minghui Ao & Zhen Zhang & Daniel Chan & Jiang Qian & Hui Zhang, 2020. "Glycoproteomics-based signatures for tumor subtyping and clinical outcome prediction of high-grade serous ovarian cancer," Nature Communications, Nature, vol. 11(1), pages 1-13, December.
    5. Ling Dai & Liang Wu & Huating Li & Chun Cai & Qiang Wu & Hongyu Kong & Ruhan Liu & Xiangning Wang & Xuhong Hou & Yuexing Liu & Xiaoxue Long & Yang Wen & Lina Lu & Yaxin Shen & Yan Chen & Dinggang Shen, 2021. "A deep learning system for detecting diabetic retinopathy across the disease spectrum," Nature Communications, Nature, vol. 12(1), pages 1-11, December.
    6. Pan Fang & Yanlong Ji & Ivan Silbern & Carmen Doebele & Momchil Ninov & Christof Lenz & Thomas Oellerich & Kuan-Ting Pan & Henning Urlaub, 2020. "A streamlined pipeline for multiplexed quantitative site-specific N-glycoproteomics," Nature Communications, Nature, vol. 11(1), pages 1-11, December.
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    1. 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.

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