糖蛋白组学
聚糖
计算生物学
数据库搜索引擎
鉴定(生物学)
糖基化
数据挖掘
化学
生物
计算机科学
搜索引擎
情报检索
生物化学
糖蛋白
植物
作者
Daniel A. Polasky,Fengchao Yu,Guo Ci Teo,Alexey I. Nesvizhskii
出处
期刊:Nature Methods
[Springer Nature]
日期:2020-10-05
卷期号:17 (11): 1125-1132
被引量:137
标识
DOI:10.1038/s41592-020-0967-9
摘要
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. MSFragger-Glyco allows identification of N- and O-linked glycopeptides using the localization-aware open search strategy of the MSFragger search engine.
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