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

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

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 spect...

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Bibliographic Details
Published in:Nature communications Vol. 13; no. 1; pp. 7539 - 17
Main Authors: Kong, Siyuan, Gong, Pengyun, Zeng, Wen-Feng, Jiang, Biyun, Hou, Xinhang, Zhang, Yang, Zhao, Huanhuan, Liu, Mingqi, Yan, Guoquan, Zhou, Xinwen, Qiao, Xihua, Wu, Mengxi, Yang, Pengyuan, Liu, Chao, Cao, Weiqian
Format: Journal Article
Language:English
Published: London Nature Publishing Group UK 07-12-2022
Nature Publishing Group
Nature Portfolio
Subjects:
631/114/2784
631/1647/296
631/45/221
631/45/475
82/58
Algorithms
Antibiotics
Carcinoma, Hepatocellular
Cell lines
Deep learning
Glycopeptides
Glycosylation
Humanities and Social Sciences
Humans
Liver Neoplasms
Machine learning
Mass spectrometry
Mass spectroscopy
Metastases
Molecular Biology
multidisciplinary
Proteomes
Quantitation
Quantitative analysis
Science
Science (multidisciplinary)
Search engines
Software
Software utilities
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Summary: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. Software tools for larger-scale intact glycopeptide quantification lag far behind, which hinders exploring the differential sitespecific glycosylation. Here, the authors report pGlycoQuant, a generic tool with a deep learning model for quantitative glycoproteomics at intact glycopeptide level.
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ISSN:2041-1723
2041-1723
DOI:10.1038/s41467-022-35172-x