Urban James, Joeres Roman, Thomès Luc, Thomsson Kristina A, Bojar Daniel
Department of Chemistry and Molecular Biology, University of Gothenburg, Gothenburg, Sweden.
Wallenberg Centre for Molecular and Translational Medicine, University of Gothenburg, Gothenburg, Sweden.
Anal Bioanal Chem. 2025 Feb;417(5):931-943. doi: 10.1007/s00216-024-05500-9. Epub 2024 Aug 24.
Structural details of oligosaccharides, or glycans, often carry biological relevance, which is why they are typically elucidated using tandem mass spectrometry. Common approaches to distinguish isomers rely on diagnostic glycan fragments for annotating topologies or linkages. Diagnostic fragments are often only known informally among practitioners or stem from individual studies, with unclear validity or generalizability, causing annotation heterogeneity and hampering new analysts. Drawing on a curated set of 237,000 O-glycomics spectra, we here present a rule-based machine learning workflow to uncover quantifiably valid and generalizable diagnostic fragments. This results in fragmentation rules to robustly distinguish common O-glycan isomers for reduced glycans in negative ion mode. We envision this resource to improve glycan annotation accuracy and concomitantly make annotations more transparent and homogeneous across analysts.
寡糖或聚糖的结构细节通常具有生物学相关性，这就是为什么它们通常使用串联质谱来阐明。区分异构体的常用方法依赖于用于注释拓扑结构或连接的诊断性聚糖片段。诊断性片段通常仅在从业者中非正式知晓，或源于个别研究，其有效性或普遍性不明确，导致注释异质性并阻碍新的分析人员。利用一组精心策划的237,000个O-糖组学谱图，我们在此提出一种基于规则的机器学习工作流程，以发现可量化有效的和可推广的诊断性片段。这产生了在负离子模式下用于稳健区分常见O-聚糖异构体以用于还原聚糖的碎裂规则。我们设想该资源可提高聚糖注释的准确性，并同时使注释在分析人员之间更加透明和统一。
