Development of a Computational Tool for Automated Interpretation of Intact <i>O</i>-Glycopeptide Tandem Mass Spectra from Single Proteins
Jiangming Huang, Biyun Jiang, Huanhuan Zhao, Mengxi Wu, Siyuan Kong, Mingqi Liu, Pengyuan Yang, Weiqian Cao
Abstract
Precise and automated analysis of site-specific O-glycosylation on single proteins is crucial for comprehensive characterization of some important glycoproteins, such as tumor biomarkers and recombinant drug proteins. Mass spectrometry has been proven to be a powerful technique for protein sequencing and N-glycosylation analysis. However, challenges remain in developing computational tools for intact O-glycopeptide analysis, which has greatly hindered the development of mass-spectrometry-based O-glycosylation analysis. Herein, an integrated strategy together with a dedicated automated computational tool termed AOGP was developed for intact O-glycopeptide analysis on single proteins. AOGP utilized de novo sequencing for O-glycans and a database search strategy for peptide backbones. The false discovery rate (FDR) of the identification results was controlled and validated by a mixed Gaussian distribution estimation method. AOGP exhibited superior performance in identifying intact O-glycopeptides of the human erythropoietin with a total of 188 O-glycopeptide spectra reported under 1% FDR. AOGP is developed in Python, is fully open-sourced, and is equipped with a user-friendly interface. Such an easy-operating and robust tool would greatly facilitate O-glycosylation analysis on single proteins in tumor biomarker and recombinant drug protein development.