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.

软件工具已经推进了大规模完整糖肽的鉴定。然而，定量分析工具仍然落后，这阻碍了对差异位点特异性糖基化的探索。在此，我们报告了 pGlycoQuant，这是一种用于基于初级和串联质谱的完整糖肽定量的通用工具。 pGlycoQuant 通过应用深度学习模型在糖肽匹配方面取得了进步，与 Byologic、MSFragger-Glyco、Skyline 和 Proteome Discoverer 相比，该模型将缺失值减少了 19-89%，同时还采用了 Match In Run 算法来实现更多糖肽覆盖，从而极大地扩展了糖肽匹配范围。几个广泛使用的搜索引擎的定量功能，包括pGlyco 2.0、pGlyco3、Byonic和MSFragger-Glyco。 pGlycoQuant 进一步应用于三种不同转移性 HCC 细胞系的 N-糖蛋白组学研究，定量了 6435 个完整的 N-糖肽，并结合体外分子生物学实验，说明了 L1CAM 的位点 979 核心岩藻糖基化作为 HCC 转移的潜在调节剂。我们期望免费提供的 pGlycoQuant 在糖蛋白质组学研究中得到进一步应用。