Recent advances in computational predictions of NMR parameters for the structure elucidation of carbohydrates: methods and limitations

Abstract

All living systems are comprised of four fundamental classes of macromolecules--nucleic acids, proteins, lipids, and carbohydrates (glycans). Glycans play a unique role of joining three principal hierarchical levels of the living world: (1) the molecular level (pathogenic agents and vaccine recognition by the immune system, metabolic pathways involving saccharides that provide cells with energy, and energy accumulation via photosynthesis); (2) the nanoscale level (cell membrane mechanics, structural support of biomolecules, and the glycosylation of macromolecules); (3) the microscale and macroscale levels (polymeric materials, such as cellulose, starch, glycogen, and biomass). NMR spectroscopy is the most powerful research approach for getting insight into the solution structure and function of carbohydrates at all hierarchical levels, from monosaccharides to oligo- and polysaccharides. Recent progress in computational procedures has opened up novel opportunities to reveal the structural information available in the NMR spectra of saccharides and to advance our understanding of the corresponding biochemical processes. The ability to predict the molecular geometry and NMR parameters is crucial for the elucidation of carbohydrate structures. In the present paper, we review the major NMR spectrum simulation techniques with regard to chemical shifts, coupling constants, relaxation rates and nuclear Overhauser effect prediction applied to the three levels of glycomics. Outstanding development in the related fields of genomics and proteomics has clearly shown that it is the advancement of research tools (automated spectrum analysis, structure elucidation, synthesis, sequencing and amplification) that drives the large challenges in modern science. Combining NMR spectroscopy and the computational analysis of structural information encoded in the NMR spectra reveals a way to the automated elucidation of the structure of carbohydrates.

Structural factors affecting 13C NMR chemical shifts of cellulose: a computational study

“…The gg C4 chemical shift was centered at * 79 ppm, which is out of the NMR C4 chemical shift range of 89-84 ppm. Our method cannot totally exclude the possibility that part of sC4 peak is dominated by gg C4, because the protocol we applied here has the RMS error of * 3 ppm for cellulose Ib and Ia (Kubicki et al 2013;Toukach and Ananikov 2013). However, based on these calculations and analysis, we believe that the gg conformation is the least probable.…”

“…This protocol has been shown to achieve an RMS error of better than 3 ppm for cellulose Ib and Ia (Kubicki et al 2013;Toukach and Ananikov 2013;Wang et al 2016b). The modified Perdue-Wang exchange-correlation functional mPW1PW91 (Adamo and Barone 1998) with the 6-31G(d) basis set (Rassolov et al 2001) and gauge-independent atomic orbitals (GIAO) (Bühl et al 1999;Cheeseman et al 1996;Karadakov 2006;Lodewyk et al 2011;Schreckenbach and Ziegler 1995;Wiitala et al 2006;Wolinski et al 1990) method in Gaussian 09 were used.…”

Structure assignment and H/D-exchange behavior of several glycosylated polyphenols

“…18 Many different Karplus equations have been developed and refined over the decades. 21 Nowadays, such equations are based on a multitude of experimental data and extensive Density Functional Theory (DFT) calculations at different levels of theory, which take bond lengths, electron densities, electron orbital terms, and dipolar electron spin terms into account. The observation of geometry-dependent coupling constants in six-membered rings and carbohydrates 22 has made Karplus equations especially useful to establish the conformation of the ring and the glycosidic bond.…”

Development of Monolithic Column Materials for the Separation and Analysis of Glycans

“…Efforts to reduce the required amount of glycan for NMR analysis are under development [35]. NMR elucidation of glycan structure can be enhanced using computational methods [36] and x-ray crystallography together with molecular modeling [37].…”