SweetNET: A Bioinformatics Workflow for Glycopeptide MS/MS Spectral Analysis

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• 作者：Waqas Nasir ; Alejandro Gomez Toledo ; Fredrik Noborn ; Jonas Nilsson ; Mingxun Wang ; Nuno Bandeira ; Göran Larson
• 刊名：Journal of Proteome Research
• 出版年：2016
• 出版时间：August 5, 2016
• 年：2016
• 卷：15
• 期：8
• 页码：2826-2840
• 全文大小：862K
• 年卷期：0
• ISSN：1535-3907

文摘

Glycoproteomics has rapidly become an independent analytical platform bridging the fields of glycomics and proteomics to address site-specific protein glycosylation and its impact in biology. Current glycopeptide characterization relies on time-consuming manual interpretations and demands high levels of personal expertise. Efficient data interpretation constitutes one of the major challenges to be overcome before true high-throughput glycopeptide analysis can be achieved. The development of new glyco-related bioinformatics tools is thus of crucial importance to fulfill this goal. Here we present SweetNET: a data-oriented bioinformatics workflow for efficient analysis of hundreds of thousands of glycopeptide MS/MS-spectra. We have analyzed MS data sets from two separate glycopeptide enrichment protocols targeting sialylated glycopeptides and chondroitin sulfate linkage region glycopeptides, respectively. Molecular networking was performed to organize the glycopeptide MS/MS data based on spectral similarities. The combination of spectral clustering, oxonium ion intensity profiles, and precursor ion m/z shift distributions provided typical signatures for the initial assignment of different N-, O- and CS-glycopeptide classes and their respective glycoforms. These signatures were further used to guide database searches leading to the identification and validation of a large number of glycopeptide variants including novel deoxyhexose (fucose) modifications in the linkage region of chondroitin sulfate proteoglycans.