Prediction of the anomeric configuration, type of linkage, and residues in disaccharides from 1D 13C NMR data

NMR chemical shift prediction of glycans: application of the computer program CASPER in structural analysis

Carbohydrate molecules have highly complex structures and the constituent monosaccharides and substituents are linked to each other in a large number of ways. NMR spectroscopy can be used to unravel these structures, but the process may be tedious and time-consuming. The computerized approach based on the CASPER program can facilitate rapid structural determination of glycans with little user intervention, which results in the most probable primary structure of the investigated carbohydrate material. Additionally, (1)H and (13)C NMR chemical shifts of a user-defined structure can be predicted, and this tool may thus be employed in many aspects where NMR spectroscopy plays an important part of a study.

1D 13C-NMR data as molecular descriptors in spectra--structure relationship analysis of oligosaccharides

Spectra-structure relationships were investigated for estimating the anomeric configuration, residues and type of linkages of linear and branched trisaccharides using ¹³C-NMR chemical shifts. For this study, 119 pyranosyl trisaccharides were used that are trimers of the α or β anomers of D-glucose, D-galactose, D-mannose, L-fucose or L-rhamnose residues bonded through α or β glycosidic linkages of types 1→2, 1→3, 1→4, or 1→6, as well as methoxylated and/or N-acetylated amino trisaccharides. Machine learning experiments were performed for: (1) classification of the anomeric configuration of the first unit, second unit and reducing end; (2) classification of the type of first and second linkages; (3) classification of the three residues: reducing end, middle and first residue; and (4) classification of the chain type. Our previously model for predicting the structure of disaccharides was incorporated in this new model with an improvement of the predictive power. The best results were achieved using Random Forests with 204 di- and trisaccharides for the training set—it could correctly classify 83%, 90%, 88%, 85%, 85%, 75%, 79%, 68% and 94% of the test set (69 compounds) for the nine tasks, respectively, on the basis of unassigned chemical shifts.