The relationship between the three-dimensional structures of oligosaccharides and polysaccharides and their biological properties has been the focus of many recent studies. The overall conformation of an oligosaccharide depends primarily on the orientation of the torsion angles (φ, ψ, and ω) between glycosyl residues. Numerous experimental studies have shown that in glucopyranosides the ω-torsion angle (O ₆ -C ₆ -C ₅ -O ₅ ) displays a preference for gauche orientations, in disagreement with predictions based on gas-phase quantum mechanics calculations. In contrast, the ω-angle in galactopyranosides displays a high proportion of the anti-orientation. For oligosaccharides containing glycosidic linkages at the 6-position (1→6 linked), variations in rotamer population have a direct effect on the oligosaccharides' structure and function, and yet the physical origin of these conformational preferences remains unclear. Although it is generally recognized that the gauche effect in carbohydrates is a solvent-dependent phenomenon, the mechanism through which solvent induces the gauche preference is not understood. In the present work, quantum mechanics and solvated molecular dynamics calculations were performed on two representative carbohydrates, methyl α- d -glucopyranoside and methyl α- d -galactopyranoside. We show that correct reproduction of the experimental rotamer distributions about the ω-angles is obtained only after explicit water is included in the molecular dynamics simulations. The primary role of the water appears to be to disrupt the hydrogen bonding within the carbohydrate, thereby allowing the rotamer populations to be determined by internal electronic and steric repulsions between the oxygen atoms. The results reported here provide a quantitative explanation of the conformational behavior of (1→6)-linked carbohydrates.

With improvements in computer speed and algorithm efficiency, MD simulations are sampling larger amounts of molecular and biomolecular conformations. Being able to qualitatively and quantitatively sift these conformations into meaningful groups is a difficult and important task, especially when considering the structure-activity paradigm. Here we present a study that combines two popular techniques, principal component (PC) analysis and clustering, for revealing major conformational changes that occur in molecular dynamics (MD) simulations. Specifically, we explored how clustering different PC subspaces effects the resulting clusters versus clustering the complete trajectory data. As a case example, we used the trajectory data from an explicitly solvated simulation of a bacteria’s L11·23S ribosomal subdomain, which is a target of thiopeptide antibiotics. Clustering was performed, using K-means and average-linkage algorithms, on data involving the first two to the first five PC subspace dimensions. For the average-linkage algorithm we found that data-point membership, cluster shape, and cluster size depended on the selected PC subspace data. In contrast, K-means provided very consistent results regardless of the selected subspace. Since we present results on a single model system, generalization concerning the clustering of different PC subspaces of other molecular systems is currently premature. However, our hope is that this study illustrates a) the complexities in selecting the appropriate clustering algorithm, b) the complexities in interpreting and validating their results, and c) by combining PC analysis with subsequent clustering valuable dynamic and conformational information can be obtained.