Sugar Folding:  A Novel Structural Prediction Tool for Oligosaccharides and Polysaccharides 2

Area of Science:

• Carbohydrate Chemistry
• Computational Chemistry
• Structural Biology

Background:

• Predicting oligosaccharide conformations is crucial for understanding their biological roles.
• Existing computational methods can be computationally expensive and may miss key structures.

Purpose of the Study:

• To present a new, computationally efficient scheme for predicting complex oligosaccharide conformations in solution.
• To validate the method using human milk oligosaccharides (HMOs) LNF-1 and LND-1.

Main Methods:

• Utilizing a fast sugar conformation prediction tool based on an implicit solvent model.
• Employing Nuclear Overhauser Effect (NOE) ranking for experimental comparison.
• Performing short-time molecular dynamics simulations in explicit solvent to assess solvation and thermal effects.

Main Results:

• The developed protocol efficiently identifies unique structures representing stable energy minima.
• Predicted conformations align well with previous computational studies but at a reduced cost.
• The method discovered stable energy minima missed by prior molecular dynamics simulations.
• Explicit solvent simulations confirmed the stability of predicted conformers under physiological conditions.

Conclusions:

• The novel prediction scheme offers a significant advancement in computational efficiency for oligosaccharide conformational analysis.
• This method provides a valuable tool for structural biologists and carbohydrate chemists studying complex sugars.
• The approach facilitates accurate comparison with experimental data, aiding in structure elucidation.