International Journal of Molecular Sciences Review Three-Dimensional Structures of Carbohydrates and Where to Find Them Sofya I. Scherbinina 1,2,* and Philip V. Toukach 1,* 1 N.D. Zelinsky Institute of Organic Chemistry, Russian Academy of Science, Leninsky prospect 47, 119991 Moscow, Russia 2 Higher Chemical College, D. Mendeleev University of Chemical Technology of Russia, Miusskaya Square 9, 125047 Moscow, Russia * Correspondence: [email protected] (S.I.S.); [email protected] (P.V.T.) Received: 26 September 2020; Accepted: 16 October 2020; Published: 18 October 2020 Abstract: Analysis and systematization of accumulated data on carbohydrate structural diversity is a subject of great interest for structural glycobiology. Despite being a challenging task, development of computational methods for efficient treatment and management of spatial (3D) structural features of carbohydrates breaks new ground in modern glycoscience. This review is dedicated to approaches of chemo- and glyco-informatics towards 3D structural data generation, deposition and processing in regard to carbohydrates and their derivatives. Databases, molecular modeling and experimental data validation services, and structure visualization facilities developed for last five years are reviewed. Keywords: carbohydrate; spatial structure; model build; database; web-tool; glycoinformatics; structure validation; PDB glycans; structure visualization; molecular modeling 1. Introduction Knowledge of carbohydrate spatial (3D) structure is crucial for investigation of glycoconjugate biological activity [1,2], vaccine development [3,4], estimation of ligand-receptor interaction energy [5–7] studies of conformational mobility of macromolecules [8], drug design [9], studies of cell wall construction aspects [10], glycosylation processes [11], and many other aspects of carbohydrate chemistry and biology. Therefore, providing information support for carbohydrate 3D structure is vital for the development of modern glycomics and glycoproteomics. As result of growing interest to glycoprofiling, glycan microarrays, carbohydrate active enzymes (CAZy) and glycan-binding proteins (GBP) which are involved in biological processes, several major international projects (e.g., GlySpace [12], GlyCosmos [13], Glycomics@ExPASy [14], GlyGen [15], JCGGDB [16], Glytoucan [17], MIRAGE [18], CFG [19], RINGS [20], GLIC (https://glic.glycoinfo.org/), SysGlyco (https://sysglyco.org/)) were launched to integrate variety of data produced by glycobiological research. The main goal of existing glycoinformatics projects is to provide versatile resources with user-friendly access helpful for disease diagnostics [21,22], glycobioinformatics studies [23], glycosylation site prediction [24], CAZy activity prognosis [25,26] and other applications. Appending of structural repositories with 3D structural data opens the way for computational glycobiology and modeling of carbohydrate structures at atomic resolution. Design of novel workflows and techniques to connect carbohydrate spatial structure modes and experimental data with verification, processing, analysis and deposition of associated data has gained increased popularity in glycoscience community [27]. A Carbohydrate Structure Database (CSDB, [28]) module for carbohydrate 3D structure modeling is a demonstrative example of 3D structural data integration facilities (as a database) combined with dedicated interface (as a glycoinformatics project). Further details on CSDB 3D facilities are discussed below. Int. J. Mol. Sci. 2020, 21, 7702; doi:10.3390/ijms21207702 www.mdpi.com/journal/ijms Int. J. Mol. Sci. 2020, 21, x FOR PEER REVIEW 2 of 48 Int. J. Mol. Sci. 2020, 21, 7702 2 of 46 integration facilities (as a database) combined with dedicated interface (as a glycoinformatics project). Further details on CSDB 3D facilities are discussed below. The typical types of knowledge about a carbohydrate 3D structure include (Figure1): The typical types of knowledge about a carbohydrate 3D structure include (Figure 1): Primary structure (atom connectivity); • Primary structure (atom connectivity); Monosaccharide ring conformation; • Monosaccharide ring conformation; • Rotational states of inter-residue and exocyclic linkages and their energies; • Rotational states of inter-residue and exocyclic linkages and their energies; • Ring puckering and transitions of glycosidic linkage conformation on a time scale; • Ring puckering and transitions of glycosidic linkage conformation on a time scale; • Large-scaleLarge-scale spatialspatial arrangementarrangement (tertiary(tertiary structure).structure). • Figure 1.1. Typical componentscomponents of aa carbohydratecarbohydrate 3D3D structurestructure exemplifiedexemplified onon sucrose:sucrose: (a) primaryprimary structurestructure (in(in SymbolSymbol Nomenclature Nomenclature for for Glycans Glycans (SNFG)); (SNFG)); (b) ( superimposedb) superimposed conformational conformational states states and c Cremer–Popleand Cremer–Pople diagram; diagram; ( ) conformational (c) conformational space of a two-torsionspace of a glycosidic two-torsion linkage glycosidic (Ramachandran linkage plot); (d) transitions of glycosidic dihedrals. (Ramachandran plot); (d) transitions of glycosidic dihedrals. Herein we focus on the important aspects of carbohydrate 3D structure availability to researchers: Herein we focus on the important aspects of carbohydrate 3D structure availability to structural repositories; glycoinformatics tools and workflows to assist structure building, modeling and researchers: structural repositories; glycoinformatics tools and workflows to assist structure erroneous molecular geometry data detection and remediation; carbohydrate 3D structure presentation building, modeling and erroneous molecular geometry data detection and remediation; and visualization methods. carbohydrate 3D structure presentation and visualization methods. 2. Structural Databases 2. Structural Databases Structural databases make significant contribution to bringing information technologies to glycoscienceStructural [29 databases]. 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A data-miningsubject of this tools. review is databases providing theoretical or empirical 3D structures of carbohydrates and related data-mining tools. Int. J. Mol. Sci. 2020, 21, 7702 3 of 46 Int. J. Mol. Sci. 2020, 21, x FOR PEER REVIEW 3 of 48 Figure 2.2.Networking Networking between between glycoinformatics glycoinformatics projects projec and relatedts and servicesrelated that services promotes that achievement promotes achievementof data integration of data in glycomics.integration Reproduced in glycomics. with Reproduced permission with from [permission29], © 2020 from Wiley-VCH [29], © Verlag 2020 Wiley-VCHGmbH & Co. Verlag KGaA, GmbH Weinheim. & Co. KGaA, Weinheim. The majoritymajority ofof existingexisting repositories repositories for for carbohydrate carbohydrate 3D 3D structures structures offer offer open-access open-access data data via web via webinterface. interface. Deposited Deposited datasets datasets can be representedcan be repres byented glycoproteins, by glycoproteins, protein-carbohydrate protein-carbohydrate complexes, poly-complexes, and oligosaccharides poly- and oligosaccharides with 3D structure with experimentally3D structure experimentally resolved or specified resolved by or means specified of NMR, by X-raymeans crystallography,of NMR, X-ray cryoEM,crystallography, small angle cryoEM, X-ray sm scattering,all angle etc.X-ray [27 ].scattering, Several databasesetc. [27]. suchSeveral as databasesGLYCAM-Web, such EK3D,as GLYCAM-Web, 3DSDSCAR, GlycoMapsDBEK3D, 3DSDSCAR contain, GlycoMapsDB data from molecular contain dynamicsdata from simulations. molecular Wedynamics have also simulations. mentioned databasesWe have featuringalso mentioned information databases on protein featuring structures information involving carbohydrate on protein structuresmoiety in termsinvolving of glycosylation carbohydrate (as post-translationalmoiety in terms modification, of glycosylation dbPTM), (as carbohydrate post-translational