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

MPG-Autoren
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Zeng,  Wen-Feng
Mann, Matthias / Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Max Planck Society;

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Zitation

Kong, S., Gong, P., Zeng, W.-F., Jiang, B., Hou, X., Zhang, Y., et al. (2022). pGlycoQuant with a deep residual network for quantitative glycoproteomics at intact glycopeptide level. Nature Communications, 13(1): 7539. doi:10.1038/s41467-022-35172-x.


Zitierlink: https://hdl.handle.net/21.11116/0000-000D-1AE2-7
Zusammenfassung
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 tandemmass spectrometrybased 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 severalwidely used search engines, including pGlyco 2.0, pGlyco3, Byonic and MSFraggerGlyco. 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 979core fucosylation of L1CAM as a potential regulator of HCC metastasis. We expected further applications of the freely available pGlycoQuant in glycoproteomic studies.