1 research outputs found
Predicting Structural Motifs of Glycosaminoglycans using Cryogenic Infrared Spectroscopy and Random Forest
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