IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v13y2022i1d10.1038_s41467-022-35172-x.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-022-35172-x
    File Function: Abstract
    Download Restriction: no
    File URL: https://libkey.io/10.1038/s41467-022-35172-x?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    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. 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.
    6. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zheng Fang & Hongqiang Qin & Jiawei Mao & Zhongyu Wang & Na Zhang & Yan Wang & Luyao Liu & Yongzhan Nie & Mingming Dong & Mingliang Ye, 2022. "Glyco-Decipher enables glycan database-independent peptide matching and in-depth characterization of site-specific N-glycosylation," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
    2. Klemens Fröhlich & Eva Brombacher & Matthias Fahrner & Daniel Vogele & Lucas Kook & Niko Pinter & Peter Bronsert & Sylvia Timme-Bronsert & Alexander Schmidt & Katja Bärenfaller & Clemens Kreutz & Oliv, 2022. "Benchmarking of analysis strategies for data-independent acquisition proteomics using a large-scale dataset comprising inter-patient heterogeneity," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    3. Xuhong Hou & Limin Wang & Dalong Zhu & Lixin Guo & Jianping Weng & Mei Zhang & Zhiguang Zhou & Dajin Zou & Qiuhe Ji & Xiaohui Guo & Qiang Wu & Siyu Chen & Rong Yu & Hongli Chen & Zhengjing Huang & Xia, 2023. "Prevalence of diabetic retinopathy and vision-threatening diabetic retinopathy in adults with diabetes in China," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    4. Bei Wang & Arabella H. Wan & Yu Xu & Ruo-Xin Zhang & Ben-Chi Zhao & Xin-Yuan Zhao & Yan-Chuan Shi & Xiaolei Zhang & Yongbo Xue & Yong Luo & Yinyue Deng & G. Gregory Neely & Guohui Wan & Qiao-Ping Wang, 2023. "Identification of indocyanine green as a STT3B inhibitor against mushroom α-amanitin cytotoxicity," Nature Communications, Nature, vol. 14(1), pages 1-15, December.
    5. Stacy A. Malaker & Nicholas M. Riley & D. Judy Shon & Kayvon Pedram & Venkatesh Krishnan & Oliver Dorigo & Carolyn R. Bertozzi, 2022. "Revealing the human mucinome," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    6. Yi Yang & Qun Fang, 2024. "Prediction of glycopeptide fragment mass spectra by deep learning," Nature Communications, Nature, vol. 15(1), pages 1-12, December.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-35172-x. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.