Efficient and Accurate Glycopeptide Identification Pipeline for High-Throughput Site-Specific N‑Glycosylation Analysis

Study
of site-specific N-glycosylation in complex sample remains
a huge analytical challenge because protein glycosylation is structurally
diverse in post-translational modifications, resulting in an intricacy
of N-glycopeptides. Here we have developed a novel approach for high-throughput
N-glycopeptide profiling based on a network-centric algorithm for
deciphering glycan fragmentation in mass spectrometry. We performed
an extensive validation and a high-throughput N-glycosylation study
on serum and identified thousands of N-glycopeptide spectra with high
confidence. The results revealed a similar level of glycan microheterogeneity
to that of conventional glycomics approach on individual proteins
and provided the unique in-depth site-specific information that could
only be studied through glycopeptide profiling.

复杂样本中的位点特异性N-糖基化（site-specific N-glycosylation）研究仍面临巨大的分析挑战：蛋白质糖基化作为一类翻译后修饰（post-translational modifications），其结构多样性极高，进而导致N-糖肽（N-glycopeptide）的结构复杂度大幅提升。为此，我们开发了一种基于以网络为中心的算法的高通量（high-throughput）N-糖肽谱分析新方法，该算法可用于解析质谱（mass spectrometry）中的糖链碎裂行为。我们针对血清样本开展了大规模验证与高通量N-糖基化研究，以高置信度鉴定得到了数千条N-糖肽质谱谱图。研究结果显示，该方法在单蛋白水平上的糖链微观异质性（glycan microheterogeneity）分析能力与传统糖组学方法相当，同时还提供了唯有通过糖肽谱分析才能获取的独特深度位点特异性信息。