Department of Pathology, University of Michigan, Ann Arbor, MI, USA.
Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.
Nat Methods. 2020 Nov;17(11):1125-1132. doi: 10.1038/s41592-020-0967-9. Epub 2020 Oct 5.
Recent advances in methods for enrichment and mass spectrometric analysis of intact glycopeptides have produced large-scale glycoproteomics datasets, but interpreting these data remains challenging. We present MSFragger-Glyco, a glycoproteomics mode of the MSFragger search engine, for fast and sensitive identification of N- and O-linked glycopeptides and open glycan searches. Reanalysis of recent N-glycoproteomics data resulted in annotation of 80% more glycopeptide spectrum matches (glycoPSMs) than previously reported. In published O-glycoproteomics data, our method more than doubled the number of glycoPSMs annotated when searching the same glycans as the original search, and yielded 4- to 6-fold increases when expanding searches to include additional glycan compositions and other modifications. Expanded searches also revealed many sulfated and complex glycans that remained hidden to the original search. With greatly improved spectral annotation, coupled with the speed of index-based scoring, MSFragger-Glyco makes it possible to comprehensively interrogate glycoproteomics data and illuminate the many roles of glycosylation.
近年来，用于富集和质谱分析完整糖肽的方法取得了进展，产生了大规模的糖蛋白质组学数据集，但解释这些数据仍然具有挑战性。我们提出了 MSFragger-Glyco，这是 MSFragger 搜索引擎的糖蛋白质组学模式，用于快速、灵敏地鉴定 N-和 O-连接的糖肽和开放聚糖搜索。对最近的 N-糖蛋白质组学数据的重新分析导致注释了比之前报道的多 80%的糖肽谱匹配（glycoPSMs）。在已发表的 O-糖蛋白质组学数据中，当搜索与原始搜索相同的聚糖时，我们的方法将注释的 glycoPSMs 数量增加了一倍以上，当将搜索扩展到包括其他聚糖组成和其他修饰时，glycoPSMs 数量增加了 4 到 6 倍。扩展搜索还揭示了许多原始搜索中隐藏的硫酸化和复杂聚糖。通过大大改进的光谱注释，再加上基于索引的评分的速度，MSFragger-Glyco 使得全面研究糖蛋白质组学数据并阐明糖基化的许多作用成为可能。
