Iterative Glycan Structure Generation by Component Localization and Classification from Mass Spectra

Analyzing intact glycopeptides via mass spectrometry data elucidates the glycan composition and attachment sites, which are pivotal for investigating protein folding, stability, and function. GlycanFinder, an advanced technique, builds the glycan structure starting from the peptide (i.e., the root) and sequentially adds monosaccharide components (i.e., the leaves). However, its major limitation is the dependence on predefined rules with strong assumptions for leaf addition locations. To address this issue, we introduce MS2Glycan, a deep learning method for iteratively generating novel glycan structures from mass spectrometry data. It models component generation similarly to an object detection task, incorporating both localization and classification sub-tasks, and constructs the glycan structure component by component. MS2Glycan offers a key improvement over GlycanFinder by using a data-driven approach to predict component locations, rather than relying on predefined rules. By analyzing intact N-linked glycopeptides collected from five different mouse tissues, we assessed MS2Glycan through five-fold cross-validation, observing a notable enhancement in structural accuracy, which rose from 32% to 74%. Additionally, our findings indicate that our model more effectively mimics the principles of glycan structure construction and offers valuable insights into fully leveraging mass spectrometry data. The Python implementation can be accessed via the following link: https://github.com/xfcui/MS2Glycan.