pGlyco 2.0 enables precision N-glycoproteomics with comprehensive quality control and one-step mass spectrometry for intact glycopeptide identification. Liu, M., Zeng, W., Fang, P., Cao, W., Liu, C., Yan, G., Zhang, Y., Peng, C., Wu, J., Zhang, X., Tu, H., Chi, H., Sun, R., Cao, Y., Dong, M., Jiang, B., Huang, J., Shen, H., Wong, C. C. L., He, S., & Yang, P. Nature Communications, 8(1):438, September, 2017. Number: 1 Publisher: Nature Publishing Group![pGlyco 2.0 enables precision N-glycoproteomics with comprehensive quality control and one-step mass spectrometry for intact glycopeptide identification [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex The precise and large-scale identification of intact glycopeptides is a critical step in glycoproteomics. Owing to the complexity of glycosylation, the current overall throughput, data quality and accessibility of intact glycopeptide identification lack behind those in routine proteomic analyses. Here, we propose a workflow for the precise high-throughput identification of intact N-glycopeptides at the proteome scale using stepped-energy fragmentation and a dedicated search engine. pGlyco 2.0 conducts comprehensive quality control including false discovery rate evaluation at all three levels of matches to glycans, peptides and glycopeptides, improving the current level of accuracy of intact glycopeptide identification. The N-glycoproteome of samples metabolically labeled with 15N/13C were analyzed quantitatively and utilized to validate the glycopeptide identification, which could be used as a novel benchmark pipeline to compare different search engines. Finally, we report a large-scale glycoproteome dataset consisting of 10,009 distinct site-specific N-glycans on 1988 glycosylation sites from 955 glycoproteins in five mouse tissues.
@article{liu_pglyco_2017,
title = {{pGlyco} 2.0 enables precision {N}-glycoproteomics with comprehensive quality control and one-step mass spectrometry for intact glycopeptide identification},
volume = {8},
copyright = {2017 The Author(s)},
issn = {2041-1723},
url = {https://www.nature.com/articles/s41467-017-00535-2},
doi = {10.1038/s41467-017-00535-2},
abstract = {The precise and large-scale identification of intact glycopeptides is a critical step in glycoproteomics. Owing to the complexity of glycosylation, the current overall throughput, data quality and accessibility of intact glycopeptide identification lack behind those in routine proteomic analyses. Here, we propose a workflow for the precise high-throughput identification of intact N-glycopeptides at the proteome scale using stepped-energy fragmentation and a dedicated search engine. pGlyco 2.0 conducts comprehensive quality control including false discovery rate evaluation at all three levels of matches to glycans, peptides and glycopeptides, improving the current level of accuracy of intact glycopeptide identification. The N-glycoproteome of samples metabolically labeled with 15N/13C were analyzed quantitatively and utilized to validate the glycopeptide identification, which could be used as a novel benchmark pipeline to compare different search engines. Finally, we report a large-scale glycoproteome dataset consisting of 10,009 distinct site-specific N-glycans on 1988 glycosylation sites from 955 glycoproteins in five mouse tissues.},
language = {en},
number = {1},
urldate = {2022-08-04},
journal = {Nature Communications},
author = {Liu, Ming-Qi and Zeng, Wen-Feng and Fang, Pan and Cao, Wei-Qian and Liu, Chao and Yan, Guo-Quan and Zhang, Yang and Peng, Chao and Wu, Jian-Qiang and Zhang, Xiao-Jin and Tu, Hui-Jun and Chi, Hao and Sun, Rui-Xiang and Cao, Yong and Dong, Meng-Qiu and Jiang, Bi-Yun and Huang, Jiang-Ming and Shen, Hua-Li and Wong, Catherine C. L. and He, Si-Min and Yang, Peng-Yuan},
month = sep,
year = {2017},
note = {Number: 1
Publisher: Nature Publishing Group},
keywords = {Bioinformatics, Glycomics, Mass spectrometry, Proteomic analysis},
pages = {438},
}
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