Structural diversity and conformational flexibility of carbohydrates position them as a promising green material of the future. Synthetically, the most popular method for selective coupling of two monosaccharides involves incorporation of the participating group on the glycosyl donor. However, the systematic evaluation of the role of the participating groups on the reaction outcome remains limited, mainly because condensed phased techniques, such as NMR, cannot resolve the short-lived and reactive glycosyl ion intermediate. Lack of such understanding hinders rational design and non-empirical reaction optimizations, which, in effect, leaves certain desirable synthetic targets inaccessible.<br/><br/>In this contribution, we will present our efforts in understanding of the enantiomeric outcome of the glycosylation reactions using a combination of ab initio computational methods and cold-ion infrared spectroscopy. First, we will discuss the relation between the structure of the glycosyl cation intermediate during the neighboring group and remote group participation, and the reaction enantiomeric outcome. Based upon the correlation between the relative stability of the oxocarbenium and dioxolenium types of the reaction intermediate, we explore computationally the chemical space to propose a reactivity scale of glycosyl donors. For selected donors, we characterize the Sn1 vs Sn2 character of their reactivity using short ab initio molecular dynamics simulations. Finally, we will discuss the implementation the automated sampling of the ring puckers and glycosidic linkages in a python package, glyPpy, which aims at improving the accessibility of ab initio calculations to non-experts.<br/><br/>The experimental and computational characterization of the glycosyl donor improves our understanding of the glycosylation reactions. Unlocking new reaction pathways and proposing new reagents will expand the available chemical space of carbohydrate-based materials.

Intermetdiate-based optimization of the glycosylation reaction with computational chemistry

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ACS Fall 2022

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