Glycans are among the most complex biological molecules found in nature, being composed of monosaccharide units in various combinations and linkages, and featuring diverse and asymmetric types of branching. Until the 1970s, glycan structures were generally represented in scientific publications as chemically accurate drawings, or using standard IUPAC-approved abbreviations (Sharon 1986; McNaught 1997; Chester 1998). However, given the complexity of many glycan structures, it became difficult to represent such molecules in publication figures, especially those involving multistep biosynthetic pathways or multiple structures. Thus, various investigators began to use symbols to represent monosaccharides in glycans. In 1978, Kornfeld and colleagues put forward an elegant and simple system for representation of vertebrate glycans (Kornfeld et al. 1978), and it entered into popular use over the next two decades. This system was eventually adopted and standardized by the editors of the first edition of the textbook Essentials of Glycobiology (Varki et al. 1999). While this adoption increased the popularity of the symbols, the system had some limitations and inconsistencies and did not take advantage of color. Anticipating a revision, the editors developed an updated symbol nomenclature, which was made available to the community in 2005 before the publication of the Second Edition (Varki, Cummings, Esko, Freeze, Stanley, Bertozzi, et al. 2009). This nomenclature was adopted and widely disseminated, especially by the NIGMS-funded Consortium for Functional Glycomics (CFG), which also consulted the scientific community on this issue, thus enhancing acceptance. The official version of the Second Edition nomenclature has since been available in the online version of the textbook. A suggested extended version was then presented at the website Glycopedia. Meanwhile, alternate symbol nomenclature systems have been proposed (Harvey et al. 2009). Issues regarding nomenclature tend to be more controversial than the underlying science (Varki, Cummings, Esko, Freeze, Stanley, Marth, et al. 2009), as there is never one correct answer, and some aspects are matters of taste. For example, the two systems mentioned each have some relative advantages and disadvantages.

The Symbol Nomenclature for Glycans (SNFG) is a community-curated standard for the depiction of monosaccharides and complex glycans using various colored-coded, geometric shapes, along with defined text additions. It is hosted by the National Center for Biotechnology Information (NCBI) at the NCBI-Glycans Page (www.ncbi.nlm.nih.gov/glycans/snfg.html). Several changes have been made to the SNFG page in the past year to update the rules for depicting glycans using the SNFG, to include more examples of use, particularly for non-mammalian organisms, and to provide guidelines for the depiction of ambiguous glycan structures. This Glycoforum article summarizes these recent changes.

The Carbohydrate Structure Databases (CSDBs, http://csdb.glycoscience.ru) store structural, bibliographic, taxonomic, NMR spectroscopic, and other data on natural carbohydrates and their derivatives published in the scientific literature. The CSDB project was launched in 2005 for bacterial saccharides (as BCSDB). Currently, it includes two parts, the Bacterial CSDB and the Plant&Fungal CSDB. In March 2015, these databases were merged to the single CSDB. The combined CSDB includes information on bacterial and archaeal glycans and derivatives (the coverage is close to complete), as well as on plant and fungal glycans and glycoconjugates (almost all structures published up to 1998). CSDB is regularly updated via manual expert annotation of original publications. Both newly annotated data and data imported from other databases are manually curated. The CSDB data are exportable in a number of modern formats, such as GlycoRDF. CSDB provides additional services for simulation of ¹H, ¹³C and 2D NMR spectra of saccharides, NMR-based structure prediction, glycan-based taxon clustering and other.

There are considerable differences between bacterial and mammalian glycans. In contrast to most eukaryotic carbohydrates, bacterial glycans are often composed of repeating units with diverse functions ranging from structural reinforcement to adhesion, colonization and camouflage. Since bacterial glycans are typically displayed at the cell surface, they can interact with the environment and, therefore, have significant biomedical importance.

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.