Speaker
Description
Large-scale profiling of intact glycopeptides is critical but challenging in glycoproteomics. In 2021, we propose GproDIA [1], a framework for the proteome-wide characterization of intact glycopeptides from DIA data with comprehensive statistical control by a 2-dimentional false discovery rate approach and a glycoform inference algorithm, enabling accurate identification of intact glycopeptides using wide isolation windows. We benchmark our method for N-glycopeptide profiling on DIA data of yeast and human serum samples, demonstrating that DIA with GproDIA outperforms the data-dependent acquisition-based methods for glycoproteomics in terms of capacity and data completeness of identification, as well as accuracy and precision of quantification.
In 2024, we further present DeepGP [2], a hybrid deep learning framework based on Transformer and graph neural network (GNN), for the prediction of MS/MS spectra and retention time of glycopeptides. Testing on multiple biological datasets, we demonstrate that DeepGP can predict MS/MS spectra and retention time of glycopeptides closely aligning with the experimental results. Comprehensive benchmarking of DeepGP on synthetic and biological datasets validates its effectiveness in distinguishing similar glycans. Remarkably, DeepGP can differentiate isomeric glycopeptides using MS/MS spectra without diagnostic ions.
More recently, we present a method using the ZenoTOF instrument with optimized fragmentation for intact glycopeptide identification and demonstrate its ability to analyze large-cohort glycoproteomes[3]. From 124 clinical serum samples of breast cancer, non-cancerous diseases, and non-disease controls, a total of 6901 unique site-specific glycans on 807 glycosites of proteins were detected. Much more differences of glycoproteome were observed in breast diseases than the proteome. By employing machine learning, 15 site-specific glycans were deter-mined as potential glyco-signatures in detecting breast cancer.
[1] Nature Communications, 2021, 12, 6073
[2] Nature Machine Intelligence, 2024, 6, 950-961
[3] Analytical Chemistry, 2025, 97, 114-121
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