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GproDIA enables data-independent acquisition glycoproteomics with comprehensive statistical control
Yi Yang1, Guoquan Yan1, Siyuan Kong1
1Department of Chemistry and Institutes of Biomedical Sciences, Fudan University, Shanghai, 200000, China.
Nature Communications
|October 19, 2021
Summary
We developed GproDIA, a new method for analyzing intact glycopeptides using data-independent acquisition (DIA). This approach enhances glycopeptide identification and quantification accuracy in glycoproteomics.
Area of Science:
- Biochemistry
- Proteomics
- Glycomics
Background:
- Intact glycopeptide profiling is essential but challenging in glycoproteomics.
- Data-independent acquisition (DIA) shows promise for deep proteome coverage and accurate quantification.
Purpose of the Study:
- To develop GproDIA, a framework for comprehensive intact glycopeptide characterization from DIA data.
- To improve the accuracy, completeness, and quantification of glycopeptide identification.
Main Methods:
- Utilized a 2-dimensional false discovery rate approach for statistical control.
- Employed a glycoform inference algorithm for accurate identification.
- Implemented a semi-empirical spectrum prediction strategy to expand spectral library coverage.
Main Results:
- GproDIA enables accurate intact glycopeptide identification using wide isolation windows.
- Demonstrated superior performance of DIA with GproDIA over data-dependent acquisition methods in yeast and human serum samples.
- Achieved higher capacity, data completeness, accuracy, and precision in glycopeptide profiling.
Conclusions:
- GproDIA provides a powerful and accurate tool for proteome-wide intact glycopeptide characterization.
- This framework advances the application of DIA in glycoproteomics.
- The method enhances identification and quantification capabilities for glycopeptide studies.
