Toward Automatic Inference of Glycan Linkages Using MS and Machine Learning─Proof of Concept Using Sialic Acid...

Glycosidic linkages in oligosaccharides play essential roles in determining their chemical properties and biological activities. MS has been widely used to infer glycosidic linkages but requires a substantial amount of starting material, which limits its application. In addition, there is a lack of rigorous research on what MS protocols are proper for characterizing glycosidic linkages. In this work, to deliver high-quality experimental data and analysis results, we propose a machine learning-based framework to establish appropriate MS protocols and build effective data analysis methods. We demonstrate the proof-of-principle by applying our approach to elucidate sialic acid linkages (α2'-3' and α2'-6') in a set of sialyllactose standards and NIST sialic acid-containing N-glycans as well as identify several protocol configurations for producing high-quality experimental data. Our companion data analysis method achieves nearly 100% accuracy in classifying α2'-3' vs α2'-6' using MS⁵, MS⁴, MS³, or even MS² spectra alone. The ability to determine glycosidic linkages using MS² or MS³ is significant as it requires substantially less sample, enabling linkage analysis for quantity-limited natural glycans and synthesized materials, as well as shortens the overall experimental time. MS² is also more amenable than MS^3/4/5 to automation when coupled to direct infusion or LC-MS. Additionally, our method can predict the ratio of α2'-3' and α2'-6' in a mixture with 8.6% RMSE (root-mean-square error) across data sets using MS⁵ spectra. We anticipate that our framework will be generally applicable to analysis of other glycosidic linkages.