TY - JOUR

T1 - Predicting Structural Motifs of Glycosaminoglycans using Cryogenic Infrared Spectroscopy and Random Forest

AU - Riedel, Jerome

AU - Lettow, Maike

AU - Grabarics, Márkó

AU - Götze, Michael

AU - Miller, Rebecca L.

AU - Boons, Geert Jan

AU - Meijer, Gerard

AU - von Helden, Gert

AU - Szekeres, Gergo Peter

AU - Pagel, Kevin

N1 - Publisher Copyright: © 2023 The Authors. Published by American Chemical Society.

PY - 2023

Y1 - 2023

N2 - In recent years, glycosaminoglycans (GAGs) have emerged into the focus of biochemical and biomedical research due to their importance in a variety of physiological processes. These molecules show great diversity, which makes their analysis highly challenging. A promising tool for identifying the structural motifs and conformation of shorter GAG chains is cryogenic gas-phase infrared (IR) spectroscopy. In this work, the cryogenic gas-phase IR spectra of mass-selected heparan sulfate (HS) di-, tetra-, and hexasaccharide ions were recorded to extract vibrational features that are characteristic to structural motifs. The data were augmented with chondroitin sulfate (CS) disaccharide spectra to assemble a training library for random forest (RF) classifiers. These were used to discriminate between GAG classes (CS or HS) and different sulfate positions (2-O-, 4-O-, 6-O-, and N-sulfation). With optimized data preprocessing and RF modeling, a prediction accuracy of >97% was achieved for HS tetra- and hexasaccharides based on a training set of only 21 spectra. These results exemplify the importance of combining gas-phase cryogenic IR ion spectroscopy with machine learning to improve the future analytical workflow for GAG sequencing and that of other biomolecules, such as metabolites.

AB - In recent years, glycosaminoglycans (GAGs) have emerged into the focus of biochemical and biomedical research due to their importance in a variety of physiological processes. These molecules show great diversity, which makes their analysis highly challenging. A promising tool for identifying the structural motifs and conformation of shorter GAG chains is cryogenic gas-phase infrared (IR) spectroscopy. In this work, the cryogenic gas-phase IR spectra of mass-selected heparan sulfate (HS) di-, tetra-, and hexasaccharide ions were recorded to extract vibrational features that are characteristic to structural motifs. The data were augmented with chondroitin sulfate (CS) disaccharide spectra to assemble a training library for random forest (RF) classifiers. These were used to discriminate between GAG classes (CS or HS) and different sulfate positions (2-O-, 4-O-, 6-O-, and N-sulfation). With optimized data preprocessing and RF modeling, a prediction accuracy of >97% was achieved for HS tetra- and hexasaccharides based on a training set of only 21 spectra. These results exemplify the importance of combining gas-phase cryogenic IR ion spectroscopy with machine learning to improve the future analytical workflow for GAG sequencing and that of other biomolecules, such as metabolites.

U2 - 10.1021/jacs.2c12762

DO - 10.1021/jacs.2c12762

M3 - Journal article

C2 - 37000483

AN - SCOPUS:85151828266

VL - 145

SP - 7859

EP - 7868

JO - Journal of the American Chemical Society

JF - Journal of the American Chemical Society

SN - 0002-7863

IS - 14

ER -