Prediction of glycopeptide fragment mass spectra by deep learning

Yi Yang () and Qun Fang ()
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Yi Yang: Zhejiang University
Qun Fang: Zhejiang University

Nature Communications, 2024, vol. 15, issue 1, 1-12

Abstract: Abstract Deep learning has achieved a notable success in mass spectrometry-based proteomics and is now emerging in glycoproteomics. While various deep learning models can predict fragment mass spectra of peptides with good accuracy, they cannot cope with the non-linear glycan structure in an intact glycopeptide. Herein, we present DeepGlyco, a deep learning-based approach for the prediction of fragment spectra of intact glycopeptides. Our model adopts tree-structured long-short term memory networks to process the glycan moiety and a graph neural network architecture to incorporate potential fragmentation pathways of a specific glycan structure. This feature is beneficial to model explainability and differentiation ability of glycan structural isomers. We further demonstrate that predicted spectral libraries can be used for data-independent acquisition glycoproteomics as a supplement for library completeness. We expect that this work will provide a valuable deep learning resource for glycoproteomics.

Date: 2024
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DOI: 10.1038/s41467-024-46771-1

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