Fudan University and others to create accurate N-sugar proteomics analysis methods

The team of Yang Yanyuan, professor of chemistry at Fudan University, He Simin, researcher of the Institute of Computational Technology of the Chinese Academy of Sciences, and Huang Chaolan, researcher of the National Protein Science Center (Shanghai), worked together to create pGlyco 2.0, a high-volume glyco-based peptide segment analysis method based on mass spectrometrometology, which provides a new technology for precision N-glycoproteomics. The results of the study were published in the journal pGlyco 2.0: Accurate N-Sugar Proteomics Glycopeptide Analysis Based on Comprehensive Quality Control and One-Step Mass Spectrometrology
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that Yang Weiyuan, He Simin and Huang Chaolan are co-authors of the newsletter. Yang Weiyuan is the Lead Contact for this article.
glycosylation is one of the most complex post-protein modifications. Compared with other post-protein modifications, glycosylation not only produces macro-uneconomicity (there may be multiple post-decoration bits on each protein), but also produces a large amount of micro-uneconomicity (there may be dozens or even hundreds of different post-modification groups at each bit). In addition, the ionization efficiency of the sugar chain itself is very low. The combination of these factors results in a much lower amount and mass of glycosylation analysis than the conventional analysis levels of proteomics.
This study, through in-depth study and testing of mass spectrometrological conditions, developed a step-by-step mass spectrometrometrometromety based on step energy, improved the influenza of glycopeptide identification and developed a pGlyco2.0 glycopeptide retrieval engine with independent title, and accurately controlled the glycopeptide database retrieval from the three levels of sugar chain, peptide segment and glycopeptide, thus greatly improving the quantity and quality of N-sugar proteomics analysis.
, for the first time, researchers applied re-labeling elements to the accuracy analysis of glycopeptide identification, providing new methods and standards for quality control analysis in this field.
, the study reported the largest glycosylation data set available: at a false positive rate of 1 percent, more than 10,000 N-glycopeptides were identified in five organ species in mice. (Source: Science Network Huang Xin)