Predicting Structural Motifs of Glycosaminoglycans using Cryogenic Infrared Spectroscopy and Random Forest.
J Am Chem Soc
; 145(14): 7859-7868, 2023 04 12.
Article
em En
| MEDLINE
| ID: mdl-37000483
ABSTRACT
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.
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Algoritmo Florestas Aleatórias
/
Glicosaminoglicanos
Tipo de estudo:
Clinical_trials
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Prognostic_studies
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Risk_factors_studies
Idioma:
En
Revista:
J Am Chem Soc
Ano de publicação:
2023
Tipo de documento:
Article
País de afiliação:
Alemanha