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

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 () and Weiqian Cao ()
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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
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
Chao Liu: Beihang University
Weiqian Cao: Fudan University

Nature Communications, 2022, vol. 13, issue 1, 1-17

Abstract: 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.

Date: 2022
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DOI: 10.1038/s41467-022-35172-x

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