Deep Learning Prediction of <i>O</i> ‐Glycopeptide Tandem Mass Spectra Enhances <i>O</i> ‐Glycoproteomics

Yu Zong, Yuxin Wang, Liang Qiao

Protein glycosylation, a post-translational modification involving the attachment of glycans to proteins, plays critical roles in numerous physiological and pathological cellular functions. Characterization of protein glycosylation is one of the most challenging problems due to the high heterogeneity of glycosites and glycan structures. Recently, deep learning has been adopted to predict N-glycopeptide tandem mass spectrometry (MS/MS) spectra and exhibited a promising effect in N-glycoproteomics analysis. However, current deep learning frameworks struggle to accurately predict O-glycopeptide MS/MS spectra due to the complexity of O-glycopeptides and the limited availability of training data. In this study, we introduce DeepGPO, a deep learning framework for the prediction of O-glycopeptide MS/MS spectra. The DeepGPO incorporates a Transformer module alongside two graph neural network modules designed for handling branched glycans. To address the issue of data scarcity in O-glycoproteomics, various training methods are adopted in DeepGPO, such as the introduction of training weights for different MS/MS spectra and the adoption of pre-training strategies. With the predicted MS/MS, O-glycosylation sites can be localized even in the absence of site-determining ions. Currently, DeepGPO supports both mono- and double-O-glycosylated peptides. It shows promising application in clinical human O-glycoproteomics. We anticipate that DeepGPO will inspire future advancements in glycoproteomics research.