GlycoSLASH: Concurrent Glycopeptide Identification from Multiple Related LC-MS/MS Data Sets by Using Spectral Clustering and Library Searching.
GlycoSLASH: Concurrent Glycopeptide Identification from Multiple Related LC-MS/MS Data Sets by Using Spectral Clustering and Library Searching.
复制标题
GlycoSLASH：使用光谱聚类和库搜索从多个相关LC-MS/MS数据集同时鉴定糖肽。
DOI：
10.1021/acs.jproteome.3c00066
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发表时间：
2023-05-05
影响因子：
4.4
通讯作者：
Tang, Haixu
中科院分区：
文献类型：
--
作者：
Li, Sujun;Zhu, Jianhui;Lubman, David M.;Zhou, He;Tang, Haixu
关键词：
液相色谱-串联质谱联用（LC-MS/MS）通常用于涉及数百个疾病和对照样品的大规模糖蛋白组学研究。在这些数据中用于糖肽鉴定的软件（例如商业软件Byonic）分析单个数据集，并且不利用相关数据集中存在的糖肽的冗余光谱。在此，我们提出了一种新的并发方法，糖肽识别多个相关的糖蛋白组数据集，通过使用光谱聚类和光谱库搜索。对两个大规模糖蛋白组学数据集的评估表明，与单独使用Byonic的单个数据集上的糖肽鉴定相比，并行方法可以将105% - 224%的谱鉴定为糖肽。糖肽鉴定的改进也使人们能够发现肝细胞癌患者蛋白质糖基化的几种潜在生物标志物。
Liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) is commonly adopted in large-scale glycoproteomic studies involving hundreds of disease and control samples. The software for glycopeptide identification in such data (e.g. the commercial software Byonic) analyzes the individual dataset and does not exploit the redundant spectra of glycopeptides presented in the related datasets. Herein, we present a novel concurrent approach for glycopeptide identification in multiple related glycoproteomic datasets by using spectral clustering and spectral library searching. The evaluation on two large-scale glycoproteomic datasets showed that the concurrent approach can identify 105% - 224% more spectra as glycopeptides compared to the glycopeptide identification on individual datasets using Byonic alone. The improvement of glycopeptide identification also enabled the discovery of several potential biomarkers of protein glycosylations in hepatocellular carcinoma patients.
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通讯作者：
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