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 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.

Automated summary

pGlycoQuant is a generic tool for quantitative analysis of intact glycopeptides using both primary and tandem mass spectrometry. It employs a deep learning model and a Match In Run algorithm to improve glycopeptide matching and expand the quantitative function of various search engines. Its application in N-glycoproteomic study demonstrates its potential in exploring site-specific glycosylation and its role in biological processes.