The application of NMR-pattern-recognition methods to the classification of peracetylated oligosaccharide residues: effects of intraclass structure
| Autor: | Denise S. Weber, Warren J. Goux |
|---|---|
| Rok vydání: | 1992 |
| Předmět: |
Glycan
Anomer Magnetic Resonance Spectroscopy Stereochemistry Molecular Sequence Data Oligosaccharides Biochemistry Analytical Chemistry Pattern Recognition Automated chemistry.chemical_compound chemistry.chemical_classification biology business.industry Chemical shift Organic Chemistry Pattern recognition Pulse sequence Acetylation General Medicine Carbon-13 NMR Oligosaccharide Acetic anhydride chemistry Carbohydrate Sequence Proton NMR biology.protein Artificial intelligence business |
| Zdroj: | Carbohydrate research. 233 |
| ISSN: | 0008-6215 |
| Popis: | In the present study a variety of homo- and hetero-nuclear correlation spectroscopies have been used to assign the acetoxyl carbonyl carbon, the pyranosyl proton and the acetoxyl methyl proton resonances of thirteen oligosaccharide derivatives peracetylated with [1,1′-13C]acetic anhydride. The nonderivatized forms of these structures occur as d -glucose, 2-acetamido-2-deoxy- d -glucose, d -galactose and 2-acetamido-2-deoxy- d -galactose containing substructures of O-linked glycans. On the basis of the assigned NMR variables, two pattern recognition methods, K-nearest neighbor (KNN) and SIMCA, were used to classify residues contained in these and previously studied peracetylated derivatives according to their structure and anomeric ring configuration. It was found that the SIMCA method was able to classify residues into one of eight structurally homogeneous classes with greater than 99% accuracy. In contrast, the KNN method proved to be most successful in classifying residues into one of six larger more structurally diverse classes, where some of the classes were formed by members of the same residue type but having different anomeric ring configurations. While the performance of the KNN method was improved by using variable subsets as a basis for classification. SIMCA performed best using the full compliment of 15 NMR variables. Neither of the methods was able to classify residues well when only proton chemical shifts and coupling contants were used to assign structures. This suggests that those previous methods which have traditionally used limited 1 H NMR data to make structural assignments of carbohydrate residues may be significantly improved by using complimentary 13C NMR data. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|