key: cord-270587-k56fze59
authors: Scherbinina, Sofya I.; Toukach, Philip V.
title: Three-Dimensional Structures of Carbohydrates and Where to Find Them
date: 2020-10-18
journal: Int J Mol Sci
DOI: 10.3390/ijms21207702
sha: 
doc_id: 270587
cord_uid: k56fze59

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.

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] [6] [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. Herein we focus on the important aspects of carbohydrate 3D structure availability to researchers: structural repositories; glycoinformatics tools and workflows to assist structure building, modeling and erroneous molecular geometry data detection and remediation; carbohydrate 3D structure presentation and visualization methods.

Structural databases make significant contribution to bringing information technologies to glycoscience [29] . With no focus on spatial structure, glycan databases and online tools have been recently reviewed [30] [31] [32] . Depositing huge number of carbohydrates with detailed data for each entry, databases are valuable sources of structural information, biological assignments, references and external links. Structural data are often accompanied by original and sometimes assigned experimental observables: NMR spectra, HPLC and MS profiles, etc. The services built on top of the databases can include 3D structure simulation, validation, and storage. A viewpoint of the authors at the ideal integration of data resources and services in glycoinformatics is summarized in Figure 2 . A subject of this review is databases providing theoretical or empirical 3D structures of carbohydrates and related data-mining tools. Herein we focus on the important aspects of carbohydrate 3D structure availability to researchers: structural repositories; glycoinformatics tools and workflows to assist structure building, modeling and erroneous molecular geometry data detection and remediation; carbohydrate 3D structure presentation and visualization methods.

Structural databases make significant contribution to bringing information technologies to glycoscience [29] . With no focus on spatial structure, glycan databases and online tools have been recently reviewed [30] [31] [32] . Depositing huge number of carbohydrates with detailed data for each entry, databases are valuable sources of structural information, biological assignments, references and external links. Structural data are often accompanied by original and sometimes assigned experimental observables: NMR spectra, HPLC and MS profiles, etc. The services built on top of the databases can include 3D structure simulation, validation, and storage. A viewpoint of the authors at the ideal integration of data resources and services in glycoinformatics is summarized in Figure 2 . A subject of this review is databases providing theoretical or empirical 3D structures of carbohydrates and related data-mining tools.

Networking between glycoinformatics projects and related services that promotes achievement of data integration in glycomics. Reproduced with permission from [29] , © 2020 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

The majority of existing repositories for carbohydrate 3D structures offer open-access data via web interface. Deposited datasets can be represented by glycoproteins, protein-carbohydrate complexes, poly-and oligosaccharides with 3D structure experimentally resolved or specified by means of NMR, X-ray crystallography, cryoEM, small angle X-ray scattering, etc. [27] . Several databases such as GLYCAM-Web, EK3D, 3DSDSCAR, GlycoMapsDB contain data from molecular dynamics simulations. We have also mentioned databases featuring information on protein structures involving carbohydrate moiety in terms of glycosylation (as post-translational modification, dbPTM), carbohydrate active enzymes (CAZy) and homology modeling (SWISS-MODEL). Table 1 displays currently active structural databases maintaining three-dimensional data on carbohydrates.

For Table 1 , we have selected carbohydrate and related databases using the following criteria:

• Database can be freely accessed through web user interface; • Database must contain experimentally confirmed and/or predicted 3D structures (preprocessed and/or generated on-the-fly from a primary structure input) of glycans, glycoproteins, or protein-carbohydrate complexes; • Stored 3D structures must be deposited as atomic coordinates in PDB, MOL, or other format, and the structures must contain a saccharide moiety; •

Databases with records linked to other large 3D data collections (e.g., RCSB PDB, PDBe, PDBj, PDBsum, UniProtKB etc.) are included in Table 1 (as long as database entries contain carbohydrate moiety, e.g., as a part of a lectin or an antibody); • Databases with derived carbohydrate 3D structural data (conformational maps, conformer energy minima, etc.) are included in Table 1 even if they provide no atomic coordinates (e.g., GlycoMapsDB and GFDB).

Despite no fit to the criteria above, assistance of large structure repositories offering only glycan primary structures (e.g., GlyToucan [17] (https://glytoucan.org/), UniCarbKB [33] (http://www.unicarbkb.org/)) can be useful for cross-referencing of existing carbohydrate resources and serve as supplementation to 3D modeling pipelines.

Some out-of-date projects, such as Complex Carbohydrate Structural Database (CCSD) [34, 35] , EUROCarbDB [33, 36] , GlycomeDB [36] [37] [38] , Glycoconjugate Data Bank [39] , GlycoSuite [40, 41] are noteworthy as they had shaped the modern vision of structural glycoinformatics. Networking between glycoinformatics projects and related services that promotes achievement of data integration in glycomics. Reproduced with permission from [29] , © 2020 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

The majority of existing repositories for carbohydrate 3D structures offer open-access data via web interface. Deposited datasets can be represented by glycoproteins, protein-carbohydrate complexes, poly-and oligosaccharides with 3D structure experimentally resolved or specified by means of NMR, X-ray crystallography, cryoEM, small angle X-ray scattering, etc. [27] . Several databases such as GLYCAM-Web, EK3D, 3DSDSCAR, GlycoMapsDB contain data from molecular dynamics simulations. We have also mentioned databases featuring information on protein structures involving carbohydrate moiety in terms of glycosylation (as post-translational modification, dbPTM), carbohydrate active enzymes (CAZy) and homology modeling (SWISS-MODEL). Table 1 displays currently active structural databases maintaining three-dimensional data on carbohydrates.

For Table 1 , we have selected carbohydrate and related databases using the following criteria:

• Database can be freely accessed through web user interface; • Database must contain experimentally confirmed and/or predicted 3D structures (preprocessed and/or generated on-the-fly from a primary structure input) of glycans, glycoproteins, or protein-carbohydrate complexes; • Stored 3D structures must be deposited as atomic coordinates in PDB, MOL, or other format, and the structures must contain a saccharide moiety; •

Databases with records linked to other large 3D data collections (e.g., RCSB PDB, PDBe, PDBj, PDBsum, UniProtKB etc.) are included in Table 1 (as long as database entries contain carbohydrate moiety, e.g., as a part of a lectin or an antibody); • Databases with derived carbohydrate 3D structural data (conformational maps, conformer energy minima, etc.) are included in Table 1 even if they provide no atomic coordinates (e.g., GlycoMapsDB and GFDB).

Despite no fit to the criteria above, assistance of large structure repositories offering only glycan primary structures (e.g., GlyToucan [17] (https://glytoucan.org/), UniCarbKB [33] (http://www. unicarbkb.org/)) can be useful for cross-referencing of existing carbohydrate resources and serve as supplementation to 3D modeling pipelines.

Some out-of-date projects, such as Complex Carbohydrate Structural Database (CCSD) [34, 35] , EUROCarbDB [33, 36] , GlycomeDB [36] [37] [38] , Glycoconjugate Data Bank [39] , GlycoSuite [40, 41] are noteworthy as they had shaped the modern vision of structural glycoinformatics. 

• mammalian glycans

• pre-built libraries of predicted 3D structures of common bioglycans • 3D structure models * • 3D-atomic coordinates generation (http://glycam.org/Pre-builtLibraries.jsp) a Where unknown, the year of the first publication is given. b Database is marked as curated if manual verification of data was reported in the original publication or at the database web site. c Published coverage data can be outdated; database interface provides no statistics on current coverage. * Database provides no search facilities for indicated carbohydrate 3D structural data.

Methods to probe a 3D structure of carbohydrate-containing biomolecules has been developed for decades. NMR techniques (interatomic distances derived from NOE, and torsion angles derived from coupling constants), X-ray crystallography, and electron cryo-microscopy (the two latter being atomic models built on the basis of electron density map) are among most demanded methods for 3D strucural elucidation. These methods have been reviewed [93] [94] [95] [96] and are beyond the scope of this review focused in information technologies. For use of instrumental methods for the validation of a simulated structure, please refer to Section 5 "Experimental data validation".

Structural investigation of large biological systems involving protein-glycan interactions requires leveraging more resources and employing more complex experimental techniques compared to solely oligo-and polysaccharides studies. Advances in NMR methods hold great potential for direct spatial structure determination of carbohydrate-protein complexes in solution based on intermolecular NOEs which affords estimation of atomic contacts between a protein and a carbohydrate ligand [97, 98] . Further extraction of NOE-derived distance restraints for a saccharide molecule results in generation of representative conformational ensembles [99] [100] [101] .

Support of experimental data with computer simulations can significantly improve quality of 3D structures. Quantum mechanics [100, [102] [103] [104] [105] [106] and molecular dynamics modeling [107] [108] [109] [110] [111] are commonly applied to conformation search and NMR signal prediction.

To date, the following theoretical models and methods are applied for in silico design of carbohydrate three-dimensional structure [112] [113] [114] [115] [116] : Based on Scopus [135] article count we estimated the application rate for quantum mechanics (10759 publications) and molecular mechanics (14871 publications) methods applied for carbohydrate structure modeling for the recent five years (2015-2020). Search queries included abundant carbohydrate terms, typical glycan moieties, and common modeling approaches (query details are given in Supplementary Table S1 ). In spite of growing interest to QM approaches in carbohydrate structure simulation, the major contribution to the statistics for such resource-intensive calculations is application of QM to relatively simple model compounds. For complex bioglycans in solution predominance of MM methods is more pronounced [6, 8] .

Molecular dynamics methods have achieved broad scope of application in terms of reasonable computer resource consumption. They fulfill advantageous compromise between calculation accuracy and performance, when applied to glycan molecules and their structural complexity (variety of known monomeric elements, presence of ionogenic groups), high bridge flexibility and stereo-electronic effects [112, 113, 136, 137] .

In molecular mechanics simulations, Newtonian mechanics principles are applied to calculate potential energy of a system using parameter set specific for a class of compounds under study (force field). Particular features of carbohydrate moiety, e.g., ring puckering, rotational barriers, hydrogen bonds, must be taken into account to perform precise analysis of molecular behavior in vacuo or in solution [138] . Molecular dynamics simulations consider Newtonian motion equations to observe evolution of a system during a certain timespan. Conformation ensemble generation occurs via calculation of molecular trajectory at given temperature. Accuracy of calculation depends on the employed force field and sufficient conformational sampling. MD simulations are commonly used for interpretation and analysis of the NMR and X-ray observables in the context of carbohydrate 3D structure [139] . Enhanced molecular dynamics sampling technologies, such as replica-exchange MD (REMD) [140, 141] , Hamiltonian replica-exchange MD (HREX) [142] [143] [144] , multidimensional swarm-enhanced sampling MD (msesMD) [145, 146] , Gaussian accelerated MD (GAMD) [147, 148] have been reported. Density maps or energy maps built for a set of the glycosidic torsion angles (ϕ, ψ, ω) are a typical way to report conformational preferences of a glycan provided by population analysis of its MD trajectory. As a representative example, conformational characteristics of highly flexible branched oligosaccharide Glc 1 Man 9 GlcNAc 2 (GM9) were investigated by explicit-water REMD study and validated using paramagnetism-assisted NMR spectroscopy [149] (Figure 3a,b) . Due to the structural complexity of GM9, adequate exploration of conformational space requires long-timescale simulations. Regular MD simulations of similar manno-oligosaccharides were reported to fail reproduction of experimental data [150] . Replica-exchange approach implies running periodically swapped parallel replicas of the system at different temperatures. Ensemble of GM9 conformers sampled by this method was consistent with the NMR observables. Populated areas of density maps built for glycosidic linkages of Glc 1 Man 3 branch of GM9 ( Figure 3c ) were close to crystallographic conformations of a linear Glc 1 Man 3 tetrasaccharide (a GM9 determinant recognized by lectins) from PDB. Molecular dynamics simulations consider Newtonian motion equations to observe evolution of a system during a certain timespan. Conformation ensemble generation occurs via calculation of molecular trajectory at given temperature. Accuracy of calculation depends on the employed force field and sufficient conformational sampling. MD simulations are commonly used for interpretation and analysis of the NMR and X-ray observables in the context of carbohydrate 3D structure [139] . Enhanced molecular dynamics sampling technologies, such as replica-exchange MD (REMD) [140, 141] , Hamiltonian replica-exchange MD (HREX) [142] [143] [144] , multidimensional swarm-enhanced sampling MD (msesMD) [145, 146] , Gaussian accelerated MD (GAMD) [147, 148] have been reported. Density maps or energy maps built for a set of the glycosidic torsion angles (φ, ψ, ω) are a typical way to report conformational preferences of a glycan provided by population analysis of its MD trajectory. As a representative example, conformational characteristics of highly flexible branched oligosaccharide Glc1Man9GlcNAc2 (GM9) were investigated by explicit-water REMD study and validated using paramagnetism-assisted NMR spectroscopy [149] (Figure 3a,b) . Due to the structural complexity of GM9, adequate exploration of conformational space requires long-timescale simulations. Regular MD simulations of similar manno-oligosaccharides were reported to fail reproduction of experimental data [150] . Replica-exchange approach implies running periodically swapped parallel replicas of the system at different temperatures. Ensemble of GM9 conformers sampled by this method was consistent with the NMR observables. Populated areas of density maps built for glycosidic linkages of Glc1Man3 branch of GM9 ( Figure 3c ) were close to crystallographic conformations of a linear Glc1Man3 tetrasaccharide (a GM9 determinant recognized by lectins) from PDB. Force field (or potential energy function) is represented by atomistic parameter set obtained for a considered compound class. Potential energy value can be calculated as a sum of interaction potentials for bonded (covalent bond stretching, angle bending, proper torsions) and non-bonded (electrostatic and van der Waals interactions) terms, and can include other terms (e.g., improper torsions, solvation, hydrogen bonds [151] , nonconventional hydrogen bonds [101] , for protein-carbohydrate complexes-CH-π stacking interactions [152] [153] [154] [155] , CHI Carbohydrate Intrinsic (CHI) energy contribution [156, 157] ).

Several force fields developed for general representation of wide range of organic compounds (e.g., Allinger's MM2, MM3, MM4) can be applied to carbohydrate 3D modeling [151, 158, 159] . Of them, despite being a universal force field, MM3 [160, 161] still exhibits good performance on glycans [162] [163] [164] (Reviews), [165, 166] (exemplary Articles). However, a number of force fields specially tuned for carbohydrates have been developed (Figure 4 ). In Supplementary Table S2 , we provided citation metrics of articles reporting carbohydrate-dedicated and selected general force fields that could be applied to carbohydrate structure modeling. Unfortunately, usage of general force fields could not be adequately estimated via number of citations. Automated full-text analysis and retrieval of data, needed to confirm employment of force fields for carbohydrate molecules, is beyond the scope of this review. Nevertheless, statistical data obtained for general force fields supported in popular MD software packages (e.g., AMBER, CHARMM, GROMACS, Tinker) shows obsolescence of modern force fields above Allinger's ones, and MM3 in particular (see more detailed data, references to original publications and absolute values in Supplementary Table S2 ). Force field (or potential energy function) is represented by atomistic parameter set obtained for a considered compound class. Potential energy value can be calculated as a sum of interaction potentials for bonded (covalent bond stretching, angle bending, proper torsions) and non-bonded (electrostatic and van der Waals interactions) terms, and can include other terms (e.g., improper torsions, solvation, hydrogen bonds [151] , nonconventional hydrogen bonds [101] , for protein-carbohydrate complexes-CH-π stacking interactions [152] [153] [154] [155] , CHI Carbohydrate Intrinsic (CHI) energy contribution [156, 157] ).

Several force fields developed for general representation of wide range of organic compounds (e.g., Allinger's MM2, MM3, MM4) can be applied to carbohydrate 3D modeling [151, 158, 159] . Of them, despite being a universal force field, MM3 [160, 161] still exhibits good performance on glycans [162] [163] [164] (Reviews), [165, 166] (exemplary Articles). However, a number of force fields specially tuned for carbohydrates have been developed (Figure 4 ). In Supplementary Table S2 , we provided citation metrics of articles reporting carbohydrate-dedicated and selected general force fields that could be applied to carbohydrate structure modeling. Unfortunately, usage of general force fields could not be adequately estimated via number of citations. Automated full-text analysis and retrieval of data, needed to confirm employment of force fields for carbohydrate molecules, is beyond the scope of this review. Nevertheless, statistical data obtained for general force fields supported in popular MD software packages (e.g., AMBER, CHARMM, GROMACS, Tinker) shows obsolescence of modern force fields above Allinger's ones, and MM3 in particular (see more detailed data, references to original publications and absolute values in Supplementary Table S2 ). Detailed comparisons of all-chemical and dedicated force fields in a context of glycan modeling have been published [114, 139, 151, 167] . CHARMM36, GLYCAM06, GROMOS and OPLS-AA-SEI were reported as commonly used force fields for handling carbohydrate or glycoconjugate molecules. More details are provided in Figure 5 .

CHARMM36 force field with modern carbohydrate parameter table (C36 [168] ) was derived from CHARMM all-atom biomolecular force field [169, 170] . Currently, CHARMM36 parameterization features include monosaccharides in furanose [171] and pyranose [172] forms, glycosidic linkages between monosaccharides [171, 173] , complex carbohydrates and glycoproteins Detailed comparisons of all-chemical and dedicated force fields in a context of glycan modeling have been published [114, 139, 151, 167] . CHARMM36, GLYCAM06, GROMOS and OPLS-AA-SEI were reported as commonly used force fields for handling carbohydrate or glycoconjugate molecules. More details are provided in Figure 5 .

CHARMM36 force field with modern carbohydrate parameter table (C36 [168] ) was derived from CHARMM all-atom biomolecular force field [169, 170] . Currently, CHARMM36 parameterization features include monosaccharides in furanose [171] and pyranose [172] forms, glycosidic linkages between monosaccharides [171, 173] , complex carbohydrates and glycoproteins [174] , monosaccharide-linked sulfate and phosphate groups [175] , acyclic carbohydrates and alditols [171] , as well as carbohydrate simulations in aqueous solution [176] . [174] , monosaccharide-linked sulfate and phosphate groups [175] , acyclic carbohydrates and alditols [171] , as well as carbohydrate simulations in aqueous solution [176] . GLYCAM06 force field is compatible with carbohydrates of all ring sizes and conformations for both mono-and oligosaccharides built of residues common for mammalian glycans, such as widespread aldoses, N-acetylated amino-sugars, sialic, glucuronic and galacturonic acids [177] . Parameter set was extended to non-carbohydrate moieties such as lipids [178] , glycolipids [179, 180] , lipopolysaccharides [181] , proteins and nucleic acids. Parameterization of GLYCAM06 for glycosaminoglycans was reported [182] .

GROMOS represents a broad family of carbohydrate force fields. Having been a classic one since 2005, GROMOS 45A4 [183] parameter set is used for explicit-solvent simulation of hexopyranose-based saccharides. In the recent decade, several parameters of 45A4 were optimized in GROMOS 56ACARBO [184] including lipopolysaccharides [185] . GROMOS 53A6GLYC was improved for explicit-solvent simulations [186] and extended for glycoproteins [187] . GROMOS 56ACARBO_R [188] was designed to improve description of ring conformational equilibria in hexopyranose-based saccharide chains as compared to the previous 56ACARBO version. Another modification of 56ACARBO named 56ACARBO_CHT [189] was developed for chitosan and its derivatives. Recently, extensions of GROMOS 56ACARBO/CARBO_R parameter set were adapted towards charged, protonated and esterified urinates [190] and furanose-based carbohydrates [191] . GROMOS96 43A1 was reported to have good performance on glycan structure simulation in glycoproteins [192, 193] .

OPLS-AA scaling of electrostatic interactions (SEI) force field [194] consists of improved parameters for conformational changes associated with φ-ψ dihedrals combined with enhanced accuracy of QM relative energy calculation in carbohydrate molecules refined for OPLS-AA biomolecular force field [195, 196] . Additionally OPLS force field was improved for explicit-water simulations [197] .

Rapidly developing CHARMM Drude polarizable force field for carbohydrates based on classical Drude oscillator has to be mentioned. Parameter sets obtained for hexapyranoses [198] and their aqueous solutions [199] , aldopentafuranoses and methyl-aldopentafuranosides [200] , GLYCAM06 force field is compatible with carbohydrates of all ring sizes and conformations for both mono-and oligosaccharides built of residues common for mammalian glycans, such as widespread aldoses, N-acetylated amino-sugars, sialic, glucuronic and galacturonic acids [177] . Parameter set was extended to non-carbohydrate moieties such as lipids [178] , glycolipids [179, 180] , lipopolysaccharides [181] , proteins and nucleic acids. Parameterization of GLYCAM06 for glycosaminoglycans was reported [182] .

GROMOS represents a broad family of carbohydrate force fields. Having been a classic one since 2005, GROMOS 45A4 [183] parameter set is used for explicit-solvent simulation of hexopyranose-based saccharides. In the recent decade, several parameters of 45A4 were optimized in GROMOS 56A CARBO [184] including lipopolysaccharides [185] . GROMOS 53A6 GLYC was improved for explicit-solvent simulations [186] and extended for glycoproteins [187] . GROMOS 56A CARBO_R [188] was designed to improve description of ring conformational equilibria in hexopyranose-based saccharide chains as compared to the previous 56A CARBO version. Another modification of 56A CARBO named 56A CARBO_CHT [189] was developed for chitosan and its derivatives. Recently, extensions of GROMOS 56A CARBO / CARBO_R parameter set were adapted towards charged, protonated and esterified urinates [190] and furanose-based carbohydrates [191] . GROMOS96 43A1 was reported to have good performance on glycan structure simulation in glycoproteins [192, 193] .

OPLS-AA scaling of electrostatic interactions (SEI) force field [194] consists of improved parameters for conformational changes associated with ϕ-ψ dihedrals combined with enhanced accuracy of QM relative energy calculation in carbohydrate molecules refined for OPLS-AA biomolecular force field [195, 196] . Additionally OPLS force field was improved for explicit-water simulations [197] .

Rapidly developing CHARMM Drude polarizable force field for carbohydrates based on classical Drude oscillator has to be mentioned. Parameter sets obtained for hexapyranoses [198] and their aqueous solutions [199] , aldopentafuranoses and methyl-aldopentafuranosides [200] , carboxylate and N-acetylamine saccharide derivatives [201] , alditols [202] and glycosidic linkages [203] demonstrated significant improvement of QM data reproduction compared to CHARMM additive force field.

MARTINI coarse-grained (CG) force field [204] can be used alternatively to all-atom (AA) level simulations with advantage of modeling large carbohydrate systems (solutions of oligo-, polysaccharides, glycolipids [205] [206] [207] ) on a long time scale at reasonable computational cost. Blocked ring puckering (only 4 C 1 conformation is allowed) and restrictions on the anomeric effect and glycosidic bond flexibility cumulatively provide reduction of available degrees of freedom. Another CG model PITOMBA [208] for carbohydrate simulations was developed based on GROMOS 53A6 GLYC force field.

Docking methods for carbohydrate ligands utilize molecular modeling approaches for protein-carbohydrate complexes for initial geometry generation, conformational sampling, grafting, active site mapping and binding affinity estimation [129, 137, [209] [210] [211] . Accurate reproduction of experimental data requires application of particular scoring function parameterization (empirical, force fields or knowledge-based [212] ) and docking protocols, which depend on the interaction types present in a system (CH-π interactions, CHI-energy, hydrogen bonding, solvent model, influence of solvent molecules inclusion effects, charged moiety etc.) [8, [213] [214] [215] [216] [217] [218] [219] . Extension of several docking software packages to handle carbohydrate molecules was reported to improve modeling of biologically relevant systems such as lectin-glycan [220, 221] , GAG-protein [222] [223] [224] , or antibody-carbohydrate [225] .

Currently available web-based tools along with standalone software packages were developed to facilitate work with carbohydrate 3D structure. Versatile online services for in silico molecular modeling allow users to start from a user-friendly structure input, and to automatize further procedures (see Table 2 for references). GLYCAM-Web provides tools for glycan structure prediction, glycosylated protein 3D model generation, grafting and docking. CHARMM-GUI modeler offers options for 3D structure generation and modeling of glycans including N-/O-glycoproteins and glycolipids [226, 227] . Biological membranes can be simulated with the assistance of CHARMM-GUI Membrane Builder (by combining features of LPS and glycolipid CHARMM-GUI Modelers) and GNOMM (a tool for building lipopolysaccharide-rich membranes). Noteworthy standalone programming frameworks for structure modeling are Glycosylated (modeling of glycans, glycoproteins and glycosylation) and Rosetta Carbohydrate (loop modeling [228] , glycan-to-protein docking, and glycosylation modeling).

To build diverse saccharide 3D models online, one can use such tools as REStLESS and SWEET-II. doGlycans standalone framework can be used for preparation of the atomistic models of glycopolymers, glycolipids and glycoproteins. Complex polysaccharide 3D models can be generated via POLYS and CarbBuilder. Another special class of polysaccharide builders is dedicated to glycosaminoglycans (GAGs) which can be accessed using POLYS GAG-builder and GLYCAM-Web GAG-builder. Recently, another approach for building GAG molecules was reported [229] (exemplary data pipeline only). Unfortunately, application scope of the majority of the existing structure building and modeling services is limited to rigidly defined set of supported sugar residues, and lacks non-carbohydrate moiety support.

Tools for locating and identification of a carbohydrate moiety (e.g., pdb2linucs, GlyFinder, Glycan Reader) are useful for the atomic coordinate analysis and extraction of glycoproteins and protein-carbohydrate complexes deposited in Protein Data Bank (PDB). Automated molecular geometry processing facilities can be accessed via glycoinformatics tools designed for conformational data analysis (CAT, BFMP), nuclear Overhauser effect (NOE) calculation (MD2NOE, Distance Mapping) and 3D structural data analysis related to glycan moieties from PDB (GlyTorsion, GlyVicinity, GS-align).

In Table 2 , we summarized freely available tools for generation and processing carbohydrate 3D structural data and divided them into eight categories of application. a Web-service implies an automated pipeline for running a specific software (e.g., molecular modeling, structure building, carbohydrate coordinate extraction, format conversion). It results in 3D structural data output starting from primary structure input or atomic coordinate file upload. Web-tool is employed for 3D structural data processing and analysis without 3D structural data output; it is a simpler application designed primarily for statistics and visualization. Other types are self-explanatory.

Vast variety of methods provide information about 3D structure of individual glycans and glycan moieties of glycoproteins and protein-carbohydrate complexes ( Figure 6 ) [285, 286] . The following approaches are most utilized for 3D structural data validation [287] [288] [289] :

• Ccombination of carbohydrate simulated geometry data with X-ray crystallographic data analysis [225, 290] ; • Analysis of inter-glycosidic NMR spin couplings, which depend on glycosidic bond torsions [114, 291, 292] 

Vast variety of methods provide information about 3D structure of individual glycans and glycan moieties of glycoproteins and protein-carbohydrate complexes ( Figure 6 ) [285, 286] . The following approaches are most utilized for 3D structural data validation [287] [288] [289] :

• Ccombination of carbohydrate simulated geometry data with X-ray crystallographic data analysis [225, 290] ; • Analysis of inter-glycosidic NMR spin couplings, which depend on glycosidic bond torsions [114, 291, 292] ; • Deriving nuclear Overhauser effects (NOEs) from relative populations of the interatomic distances, with subsequent comparison to the experimental NOEs in solution [99, 293, 294] ; • Purely informatic detection of errors, such as incompatible atomic coordinates originating from incorrect processing or simulation [295] [296] [297] [298] ; • Simulation by other computational methods at higher levels of theory [102, 103, 105, 108] . Unfortunately, most of the data obtained on the basis of crystallographic experiments can dramatically differ from glycan conformations in solution or have poor resolution which needs further adjustment [299, 300] . Moreover, not all of the objects of interest can be obtained as a single crystal. Electron cryo-microscopy gains popularity for carbohydrate 3D structural research [301] , however, this method requires additional refinement procedures due to resolution restrictions of the obtained density Unfortunately, most of the data obtained on the basis of crystallographic experiments can dramatically differ from glycan conformations in solution or have poor resolution which needs further adjustment [299, 300] . Moreover, not all of the objects of interest can be obtained as a single crystal.

Electron cryo-microscopy gains popularity for carbohydrate 3D structural research [301] , however, this method requires additional refinement procedures due to resolution restrictions of the obtained density maps [302] [303] [304] . Recently, cryo-EM data were used for the refinement of SARS-CoV-2 spike glycoprotein stucture using Privateer (see Table 3 for references) software [305, 306] .

Van Beusekom et al., illustrated [295] quality improvement of the PDB glycan structure model with incorrect (1-6)-linked fucose annotation, poor fit to the electron density, and missing (1-3)-linked fucose (Figure 7a ) with the help of PDB-REDO ( Figure 7b ) and CARP (Figure 7d ) tools (see Table 3 for references). Structure model obtained by PDB-REDO treatment was further manually inspected ( Figure 7c ): corrections were made for acetylamino group geometry, distorted (1-6)-linked fucose ring conformation, and (1-3)-linked fucose residue was added. Despite successful automated resolution of residue annotation problem and poor electron density refinement, complete revision could not be achieved without manual intervention. maps [302] [303] [304] . Recently, cryo-EM data were used for the refinement of SARS-CoV-2 spike glycoprotein stucture using Privateer (see Table 3 for references) software [305, 306] . Van Beusekom et al., illustrated [295] quality improvement of the PDB glycan structure model with incorrect (1-6)-linked fucose annotation, poor fit to the electron density, and missing (1-3)-linked fucose (Figure 7a ) with the help of PDB-REDO ( Figure 7b ) and CARP (Figure 7d ) tools (see Table 3 for references). Structure model obtained by PDB-REDO treatment was further manually inspected (Figure 7c ): corrections were made for acetylamino group geometry, distorted (1-6)-linked fucose ring conformation, and (1-3)-linked fucose residue was added. Despite successful automated resolution of residue annotation problem and poor electron density refinement, complete revision could not be achieved without manual intervention. NMR techniques are a powerful approach to investigate conformational and dynamic behavior of carbohydrate moieties in biomolecules [307] [308] [309] [310] . However, the nature of NOE enhancement factor has been hampering obtaining the sufficient number of distance restrains [99] . In the case of saccharides with their multiple rotatable bonds, the stable 3D structure was difficult to define, making molecular modeling essential for this class of compounds. Adjustment of experimental conditions helped to overcome the mentioned limitation and to reproduce crystal structures of oligosaccharides by modeling with NOE-derived distance restraints [100, 101] . NMR techniques are a powerful approach to investigate conformational and dynamic behavior of carbohydrate moieties in biomolecules [307] [308] [309] [310] . However, the nature of NOE enhancement factor has been hampering obtaining the sufficient number of distance restrains [99] . In the case of saccharides with their multiple rotatable bonds, the stable 3D structure was difficult to define, making molecular modeling essential for this class of compounds. Adjustment of experimental conditions helped to overcome the mentioned limitation and to reproduce crystal structures of oligosaccharides by modeling with NOE-derived distance restraints [100, 101] .

Since there is no direct way to derive detailed three-dimensional representation from the observed NOE intensities, additional molecular modeling protocols are required to establish comprehensive view of conformational space at the atomic level [311] [312] [313] . Frank et al., demonstrated conformation filtering based on the observed NOE obtained by molecular dynamics in explicit solvent [314] . As a representative example, Figure 8 depicts 1 H-1 H spatial contacts and conformation selection criteria illustrated by Moraxella catarrhalis lgt2∆ bacterium heptasaccharide, which adopts an unusual conformation. Since there is no direct way to derive detailed three-dimensional representation from the observed NOE intensities, additional molecular modeling protocols are required to establish comprehensive view of conformational space at the atomic level [311] [312] [313] . Frank et al., demonstrated conformation filtering based on the observed NOE obtained by molecular dynamics in explicit solvent [314] . As a representative example, Figure 8 depicts 1 H-1 H spatial contacts and conformation selection criteria illustrated by Moraxella catarrhalis lgt2Δ bacterium heptasaccharide, which adopts an unusual conformation. 

Protein Data Bank (PDB) [315] and Cambridge Structural Database (CSD) [316] are historically considered the main repositories of experimentally determined carbohydrate three-dimensional structures. CSD is reported to deposit over 4000 crystal structures of oligosaccharides [93] . Unlike Cambridge Structural Database, Protein Data Bank provides open access to the entire structural archive. Carbohydrate moieties deposited in PDB are usually represented as covalently bound to protein or imply non-covalently bound protein-carbohydrate complex formation [302] . According to recent reports, as at November 18, 2019 Protein Data Bank contained ~13500 carbohydrate structures representing ~9.4% of total database records [317] .

Despite being a valuable source of 3D structural data for glycoscientists, PDB lacks convenient search facilities for glycan structures. Some projects have developed data-mining tools capable of retrieving bioglycan molecular geometry data from PDB: Glycan Reader (GlycanStructure.org) [260, 261] (http://www.glycanstructure.org/), pdb2linucs (GLYCOSCIENCES.de) [47, 259, 318] (http://www.glycosciences.de/database/start.php?action=form_pdb_data), GlycoNAVI TCarp [61] (https://glyconavi.org/TCarp/) (https://gitlab.com/glyconavi/pdb2glycan) and GlyFinder (GLYCAM-Web) [257, 258] (https://dev.glycam.org/portal/gf_home/).

Another issue of concern related to Protein Data Bank is large proportion of errors in deposited coordinates, leading to requirement for a thorough checkup and development of data remediation services [319] . Commonly occurring problems associated with nomenclature, poor glycan geometry, linkage errors, missing or surplus atoms can seriously decline the quality of the obtained 3D structures [300, 320, 321] . Using Privateer software, it was discovered [299] , [301] that PDB deposits significant number of erroneous N-glycosylated structures with pyranose ring distortions, considering preferred adoption of 4 C1 conformation for D-sugars and 1 C4 conformation for L-sugars ( Figure 9 ). In most cases, poor electron density of carbohydrate moiety results in anomalous high-energy pyranose ring conformations (envelopes, half-chairs, boats, skew boats, etc.). To obtain a reasonable structure model, experimental data refinement programs should be applied to derive geometric restraints for sugar monomers. Notably, despite a cryo-EM method has a resolution limit 

Protein Data Bank (PDB) [315] and Cambridge Structural Database (CSD) [316] are historically considered the main repositories of experimentally determined carbohydrate three-dimensional structures. CSD is reported to deposit over 4000 crystal structures of oligosaccharides [93] . Unlike Cambridge Structural Database, Protein Data Bank provides open access to the entire structural archive. Carbohydrate moieties deposited in PDB are usually represented as covalently bound to protein or imply non-covalently bound protein-carbohydrate complex formation [302] . According to recent reports, as at November 18, 2019 Protein Data Bank contained~13500 carbohydrate structures representing~9.4% of total database records [317] .

Despite being a valuable source of 3D structural data for glycoscientists, PDB lacks convenient search facilities for glycan structures. Some projects have developed data-mining tools capable of retrieving bioglycan molecular geometry data from PDB: Glycan Reader (GlycanStructure.org) [260, 261] (http://www.glycanstructure.org/), pdb2linucs (GLYCOSCIENCES.de) [47, 259, 318] (http://www. glycosciences.de/database/start.php?action=form_pdb_data), GlycoNAVI TCarp [61] (https://glyconavi. org/TCarp/) (https://gitlab.com/glyconavi/pdb2glycan) and GlyFinder (GLYCAM-Web) [257, 258] (https://dev.glycam.org/portal/gf_home/).

Another issue of concern related to Protein Data Bank is large proportion of errors in deposited coordinates, leading to requirement for a thorough checkup and development of data remediation services [319] . Commonly occurring problems associated with nomenclature, poor glycan geometry, linkage errors, missing or surplus atoms can seriously decline the quality of the obtained 3D structures [300, 320, 321] . Using Privateer software, it was discovered [299] , [301] that PDB deposits significant number of erroneous N-glycosylated structures with pyranose ring distortions, considering preferred adoption of 4 C 1 conformation for D-sugars and 1 C 4 conformation for L-sugars ( Figure 9 ). In most cases, poor electron density of carbohydrate moiety results in anomalous high-energy pyranose ring conformations (envelopes, half-chairs, boats, skew boats, etc.). To obtain a reasonable structure model, experimental data refinement programs should be applied to derive geometric restraints for sugar monomers. Notably, despite a cryo-EM method has a resolution limit disadvantage, observed results indicate larger content of atypical conformations solved by X-ray crystallography, as compared to cryo-EM data.

disadvantage, observed results indicate larger content of atypical conformations solved by X-ray crystallography, as compared to cryo-EM data.

Exceptions for the relevancy of high-energy conformations were found in complexes involving carbohydrate-active enzymes, which force pyranose ring distortion enabling catalytic transformation of a carbohydrate substrate via transition states (e.g., glycosydic bond hydrolysis) [322] . Fushinobu has performed glycosidic torsion analysis for a set of PDB entries of crystal structure complexes bound to ligands bearing lacto-N-biose I (LNB, both α-and β-anomers) disaccharide unit presented in type-1 antigens. The study was supported by GlycoMaps DB (see Table 1 for references) [323] . Obtained φ-ψ data for LNBs bound to various proteins was plotted against corresponding free energy maps. Distortion of the energetically favored ring conformation strongly depended on substrate catalytic and recognition mechanisms. To date, existing tools for carbohydrate structural error detection and correction in PDB files (Table 3 ) cannot be used directly as an integral part of Protein Data Bank. Nevertheless, initiative aimed at improvement of quality at wwPDB was carried out via collaboration with glycoscience community in July 2020 [324] (https://www.wwpdb.org/documentation/carbohydrate-remediation). It includes data annotation and validation of carbohydrate-containing records. Exceptions for the relevancy of high-energy conformations were found in complexes involving carbohydrate-active enzymes, which force pyranose ring distortion enabling catalytic transformation of a carbohydrate substrate via transition states (e.g., glycosydic bond hydrolysis) [322] . Fushinobu has performed glycosidic torsion analysis for a set of PDB entries of crystal structure complexes bound to ligands bearing lacto-N-biose I (LNB, both αand β-anomers) disaccharide unit presented in type-1 antigens. The study was supported by GlycoMaps DB (see Table 1 for references) [323] . Obtained ϕ-ψ data for LNBs bound to various proteins was plotted against corresponding free energy maps. Distortion of the energetically favored ring conformation strongly depended on substrate catalytic and recognition mechanisms.

To date, existing tools for carbohydrate structural error detection and correction in PDB files (Table 3 ) cannot be used directly as an integral part of Protein Data Bank. Nevertheless, initiative aimed at improvement of quality at wwPDB was carried out via collaboration with glycoscience community in July 2020 [324] (https://www.wwpdb.org/documentation/carbohydrate-remediation). It includes data annotation and validation of carbohydrate-containing records.

Proportion of carbohydrate-containing structures in PDB has been recently reported in [302] . Figure 10 presents our analysis of data published in the framework of Protein Data Bank carbohydrate remediation project. 14117 PDB entries from carbohydrate remediation list (https://cdn.rcsb.org/ wwpdb/docs/documentation/carbohydrateRemediation/PDB_carbohydrate_list.list) were sorted by release year and plotted against the growth of PDB structures released annually (https://www.rcsb.org/ stats/growth/growth-released-structures) (as on August 10, 2020; 167,327 PDB entries were available). Obtained results indicated that~8.4% of PDB records contained a carbohydrate moiety. Additionally, each PDBx/mmCIF file corresponding to PDB ID from carbohydrate remediation list was parsed to reveal the presence of N-or O-glycosylation site annotations, which resulted in~4.2% (7076 N-glycosylated entries) and 0.2% (362 O-glycosylated entries) of total database records. A few S-and C-glycans (24 entries in total) were neglected.

Statistics on glycans in Protein Data Bank was reported [259, 302, 317, 325] , as well as tools that could facilitate collection of statistical data (Glycan Reader [70, 260, 261] , GlyFinder [258] , pdb2linucs and pdb-care [326] Proportion of carbohydrate-containing structures in PDB has been recently reported in [302] . Figure 10 presents our analysis of data published in the framework of Protein Data Bank carbohydrate remediation project. 14117 PDB entries from carbohydrate remediation list (https://cdn.rcsb.org/wwpdb/docs/documentation/carbohydrateRemediation/PDB_carbohydrate_lis t.list) were sorted by release year and plotted against the growth of PDB structures released annually (https://www.rcsb.org/stats/growth/growth-released-structures) (as on August 10, 2020; 167,327 PDB entries were available). Obtained results indicated that ~8.4% of PDB records contained a carbohydrate moiety. Additionally, each PDBx/mmCIF file corresponding to PDB ID from carbohydrate remediation list was parsed to reveal the presence of N-or O-glycosylation site annotations, which resulted in ~4.2% (7076 N-glycosylated entries) and 0.2% (362 O-glycosylated entries) of total database records. A few S-and C-glycans (24 entries in total) were neglected.

Statistics on glycans in Protein Data Bank was reported [259, 302, 317, 325] , as well as tools that could facilitate collection of statistical data (Glycan Reader [70, 260, 261] , GlyFinder [258] , pdb2linucs and pdb-care [326] ). 

Carbohydrate structure visualization in publications and computer interfaces is extremely important in terms of perception universality, unambiguity, and machine-readability. Hence, carbohydrate input [335] [336] [337] and visualization [338, 339] tools are actively developing. Feature comparison of glycan sketchers, builders and viewers (occasionally including 3D ones) was reported in a recently published review [340] . In our review, we gave more emphasis to 3D visualization approaches.

Being informative to represent glycan primary structure, most of graphical input tools such as GlycanBuilder [341] , DrawRINGS [342] , SugarSketcher [343] , DrawGlycan-SNFG [344, 345] and GlycoGlyph [337] are inappropriate for obtaining 3D structural models and their visualization due to lack of underlying modeling and insufficient data conversion functionality.

At present, glycan 3D molecular models can be built in user-friendly software allowing constructing glycans from individual saccharide components. Free web-tools, such as GLYCAM-Web, CHARMM-GUI, POLYS glycan builder, GAG-builder, SWEET-II should be noted (more references are listed in Table 2 ). A few commercial molecular modeling software is equipped with special plugins for glycan 3D structure building based on a list of predefined monosaccharide templates, e.g., Sugar Builder tool in HyperChem (http://www.hyper.com/?tabid=360) software [346] or Azahar [235] plugin in PyMol package (Schrödinger software) (https://pymol.org/2/) [347] .

To render 3D glycan structure and its conformational features, it should be recorded using a notation which includes atomic coordinates, such as MOL [348] or PDB [349] . All-atom visualization based on atomic coordinates is supported by the majority of existing molecular modeling software. Several carbohydrate structure databases utilize interactive 3D visualization using open-source software engines. As one of the pioneers, GLYCOSCIENCES.de portal developed PDB2MultiGIF [350] (http: //www.glycosciences.de/modeling/pdb2mgif/) visualization pipeline which generates an animated image of 3D model from a PDB file using RasMol [351] (http://www.openrasmol.org/). RasMol visualization was included in W3-SWEET [263] (ancestor of SWEET-II) pipeline developed by same project. Nowadays, more advanced interactive visualization applications have been developed for carbohydrate 3D molecule presentation. Jmol/JSmol [352] (http://www.openrasmol.org/) visualization applet is useful to display 3D models of carbohydrate molecules applied in numerous projects, such as CSDB, GLYCOSCIENCES.de, GLYCAM-Web and EK3D (see references in Table 1 ). NGL [353, 354] (http://nglviewer.org/), LiteMol [355] (https://www.litemol.org/) and Mol* [356] (https://www.rcsb.org/ news?year=2020&article=5efe0f606378d876901146f8) (https://molstar.org/) 3D viewers are handy for processing macromolecular PDB data stored in glycoproteomics databases (UniLectin3D, Glycan Binding Site DB, ProCarbDB, GlycoNAVI, ProCaff, etc.; see references in Table 1 ) and general proteomics repositories such as PDB [315] (http://www.wwpdb.org/), UniProtKB [357] (https://www.uniprot.org/) or SWISS-MODEL [90] (https://swissmodel.expasy.org/repository).

NGL viewer was developed mainly for convenient protein macromolecule structure processing. It allows only ball-stick representation for small molecules or non-peptide fragments, such as saccharide residues. LiteMol (and its successor, Mol*) viewer could be applied for the visualization of an arbitrary glycan with facility of highlighting carbohydrate fragments or displaying specific interactions in protein-carbohydrate complex structure. Due to these features, it was implemented in multiple carbohydrate structure databases (e.g., CSDB, Glyco3D, MatrixDB, and EPS-DB).

Despite the absence of the experimental 3D structural data, a number of carbohydrate databases have opportunity to simulate 3D atomic coordinates for deposited or inputted compounds from primary structure owing to tools developed by glycoinformatics community. CSDB (REStLESS API [265] ), GLYCOSCIENCES.de (SWEET-II [264, 350] ) and GLYCAM-Web (http://glycam.org/) portals make it possible to generate 3D atomic coordinates recorded in PDB (all) and MOL (CSDB) file formats. POLYS developed by Glyco3D project enables the construction of polysaccharides in PDB format; it was introduced in MatrixDB and EPS-DB databases. More details are provided in Table 2 .

Atomic coordinates and all-atom molecular models have not been popular in publications due to a lack of human readability. First attempts [358, 359] of prof. Kuttel et al., to visualize carbohydrate molecules in an efficient and simple way were made by developing PaperChain and Twister graphic algorithms as a part of CarboHydra [360] and Visual Molecular Dynamics [361] software packages. Later, group of prof. Pé rez suggested to restrict visualized molecule to skeletal atoms via conditional cycle plane coloring in accordance with the color code adopted in SNFG [338] visualization scheme (SweetUnityMol software [362] , Figure 11a ). Another UnityMol visualization approach called Umbrella Visualization [363, 364] was tailored for N-glycan structures. Azahar plugin for PyMol [235] affords cartoon models with polygons and rods. Several solutions for convenient visualization came up with the development of SNFG notation [339] . Thus, group of prof. Woods proposed to combine molecular structure elements with 3D SNFG icons (Figure 12a ). Such convenient visualization technique was integrated in LiteMol (Figure 12b ) [365] and Mol* (Figure 12c) [324, 356] . 3D SNFG visualization plugins are available via Visual Molecular Dynamics platform [366] (http://glycam.org/docs/othertoolsservice/ 2016/06/03/3d-symbol-nomenclature-for-glycans-3d-snfg/) and UCSF Chimera [367] visualization software Tangram plugin (https://github.com/insilichem/tangram_snfg). Designed as part of CCP4mg [368] molecular-graphics software, Glycoblocks [369] representation of monosacchrides uses shapes and colors, identical to those in SNFG (Figure 12d ). Available as PyMol plugin developed by Widmalm group (http://www.organ.su.se/gw/doku.php?id=3dcfg), 3D-CFG representation [370] based on CFG notation [371] (often referred to as a predecessor of SNFG) should also be noted as earlier approach to interpretation of carbohydrate 3D structures based on a symbol library.

Considering efficiency and usability of 3D representation based on SNFG concept, which grows popular among glycoscientists, the development of alternative solutions in carbohydrate 3D structure representations has a potential for application in glycoinformatics projects. Support of colored residues in 3D structures implemented via JSmol on GLYCOSCIENCES.de portal was reported [47] (Figure 11b) . Similarly, CSDB project has developed a 3D viewer (http://csdb.glycoscience.ru/database/core/show_ 3d.php?csdb=-3)aDManp(1-3)[Ac(1-2)?DGlcpN(1-6)]bDGal?(1-) with carbohydrate residue coloring according to the SNFG notation in the framework of a modeling module based on REStLESS API. In this tool, user can visualize input structure with help of sticks, balls and sticks, or van der Waals spheres (Figure 11c ). Options for aglycone moiety (white) and pseudo-atoms (polymeric repeats, blue caps) are supported (Figure 11d ). Int. J. Mol. Sci. 2020, 21, x FOR PEER REVIEW 28 of 48 

Development of glycoinformatics resources makes great impact on treating enormous masses of data sets produced by glyco-related research. Tools for carbohydrate 3D structural information retrieval provide a framework for experimental and computational data quality validation. Data sources based on conformational ensemble generation and analysis assist structure-function and structure-activity relationship prediction of biologically relevant bioglycans and glycoconjugates. In this review, we have summarized existing facilities on working with glycan spatial features that can provide harmonious network of structural databases, web-services, tools and standalone software 

Development of glycoinformatics resources makes great impact on treating enormous masses of data sets produced by glyco-related research. Tools for carbohydrate 3D structural information retrieval provide a framework for experimental and computational data quality validation. Data sources based on conformational ensemble generation and analysis assist structure-function and structure-activity relationship prediction of biologically relevant bioglycans and glycoconjugates. In this review, we have summarized existing facilities on working with glycan spatial features that can provide harmonious network of structural databases, web-services, tools and standalone software for modeling and processing structural data. Further advances in this field will help building better understanding of glycan participation in biological processes and supply glycoscience community with user-friendly access to voluminous data collections. Funding: The work with carbohydrate molecular modeling and PDB data was funded by Russian Foundation for Basic Research grant 18-04-00094. The work with structural databases, glycoinformatic tools and visualization was funded by Russian Science Foundation grant 18-14-00098.

The authors declare no conflict of interest.

Three 

Structural Aspects of Carbohydrates and the Relation with their Biological Properties

Biological roles of glycans

Applications of Molecular Dynamics Simulations in Immunology: A Useful Computational Method in Aiding Vaccine Design

The Role of Molecular Modeling in Predicting Carbohydrate Antigen Conformation and Understanding Vaccine Immunogenicity. In Carbohydrate-Based Vaccines: From Concept to Clinic

Evaluation of Selected Classical Force Fields for Alchemical Binding Free Energy Calculations of Protein-Carbohydrate Complexes

Computational glycoscience: Characterizing the spatial and temporal properties of glycans and glycan-protein complexes

Calculating Binding Free Energies for Protein-Carbohydrate Complexes

Predicting the Structures of Glycans, Glycoproteins, and Their Complexes

Computational Docking as a Tool for the Rational Design of Carbohydrate-Based Drugs

Synthetic glycoscapes: Addressing the structural and functional complexity of the glycocalyx

Function and 3D Structure of the N-Glycans on Glycoproteins

The GlySpace Alliance: Toward a collaborative global glycoinformatics community

The GlyCosmos Portal: A unified and comprehensive web resource for the glycosciences

GlyGen data model and processing workflow

Japan Consortium for Glycobiology and Glycotechnology Database

An accessible glycan structure repository

MIRAGE: The minimum information required for a glycomics experiment

A focused microarray approach to functional glycomics: Transcriptional regulation of the glycome

RINGS: A Web Resource of Tools for Analyzing Glycomics Data. In A Practical Guide to Using Glycomics Databases

Representing glycophenotypes: Semantic unification of glycobiology resources for disease discovery

Understanding the glycome: An interactive view of glycosylation from glycocompositions to glycoepitopes

The Glycome Analytics Platform: An integrative framework for glycobioinformatics

Glycosylation in health and disease

Glycoenzymes in Glycan Analysis and Synthesis

Computational Glycobiology: Mechanistic Studies of Carbohydrate-Active Enzymes and Implication for Inhibitor Design

The current structural glycome landscape and emerging technologies

Carbohydrate structure database merged from bacterial, archaeal, plant and fungal parts

Bridging isolated islands in the sea of data

Databases and Associated Tools for Glycomics and Glycoproteomics

Recent advances in glycoinformatic platforms for glycomics and glycoproteomics

Databases and Bioinformatic Tools for Glycobiology and Glycoproteomics

UniCarbKB: Building a knowledge platform for glycoproteomics

The Complex Carbohydrate Structure Database

EUROCarbDB: An open-access platform for glycoinformatics

GlycomeDB-Integration of open-access carbohydrate structure databases

A portal for querying across the digital world of carbohydrate sequences

Glycoconjugate Data Bank: Structures-An annotated glycan structure database and N-glycan primary structure verification service

A new curated relational database of glycoprotein glycan structures and their biological sources

A curated relational database of glycoprotein glycan structures and their biological sources. 2003 update

Bacterial carbohydrate structure database 3: Principles and realization

A new curated database on glycosyltransferases. Glycobiology

New database of bacterial carbohydrate structures

An Internet portal to support glycomics and glycobiology research

Glycan data retrieval and analysis using GLYCOSCIENCES. de applications

DB: An annotated data collection linking glycomics and proteomics data

Glyco3D: A Portal for Structural Glycosciences

Glyco3D: A Suite of Interlinked Databases of 3D Structures of Complex Carbohydrates, Lectins, Antibodies, and Glycosyltransferases

An annotated data base of 3 dimensional structures of polysaccharides

EK3D: An E. coli K antigen 3-dimensional structure database

3DSDSCAR-A three dimensional structural database for sialic acid-containing carbohydrates through molecular dynamics simulation

Hema Thanka Christlet, T. Three Dimensional Structures of Carbohydrates and Glycoinformatics: An Overview

the extracellular matrix interaction database

the extracellular matrix interaction database: Updated content, a new navigator and expanded functionalities

Integration of new data with a focus on glycosaminoglycan interactions

The exopolysaccharide properties and structures database: EPS-DB. Application to bacterial exopolysaccharides

Glycan Microarray Database and analysis toolset

Advancing glycomics: Implementation strategies at the consortium for functional glycomics

Glycan Array Data Management at Consortium for Functional Glycomics

Databases for glycoconjugates (GlyCosmos Glycoproteins and Glycolipids, GlycoProtDB, GlycoNAVI:TCarp, GlycoPOST). Glycoforum 2020

Integration of Glycoscience Data in GlyCosmos Using Semantic Web Technologies

The GlyCosmos Web Portal: Glycan structures, glycogenes, glycoproteins, pathways, diseases and more! In Program and Abstracts for 2018 Annual Meeting of the Society for Glycobiology

SugarBindDB, a resource of glycan-mediated host-pathogen interactions

GlyConnect: Glycoproteomics Goes Visual, Interactive, and Analytical

ProGlycProt V2.0, a repository of experimentally validated glycoproteins and protein glycosyltransferases of prokaryotes

ProGlycProt: A repository of experimentally characterized prokaryotic glycoproteins

ProCarbDB: A database of carbohydrate-binding proteins

ProCaff: Protein-carbohydrate complex binding affinity database

A Systematic Analysis of Protein-Carbohydrate Interactions in the PDB

A Database of Known and Modelled Carbohydrate-Binding Protein Structures with Sequence-Based Prediction Tools

Imberty, A. UniLectin3D, a database of carbohydrate binding proteins with curated information on 3D structures and interacting ligands

Structural Database for Lectins and the UniLectin Web Platform

The Lectin Frontier Database (LfDB), and data generation based on frontal affinity chromatography

Lectindb: A plant lectin database

The integrated database of carbohydrate antigens and antibodies

GlycoEpitope: A database of carbohydrate epitopes and antibodies

Using Databases and Web Resources for Glycomics Research

GlycoCD: A repository for carbohydrate-related CD antigens

SACS-Self-maintaining database of antibody crystal structure information

The structural antibody database

The carbohydrate-active enzymes database (CAZy) in 2013

The Carbohydrate-Active EnZymes database (CAZy): An expert resource for Glycogenomics

The CAZy Database/the Carbohydrate-Active Enzyme (CAZy) Database: Principles and Usage Guidelines. In A Practical Guide to Using Glycomics Databases

An information repository of protein post-translational modification

-year anniversary of a resource for post-translational modification of proteins

dbPTM in 2019: Exploring disease association and cross-talk of post-translational modifications

The SWISS-MODEL Repository of annotated three-dimensional protein structure homology models

The SWISS-MODEL Repository and associated resources

The SWISS-MODEL Repository-New features and functionality

A database of the accessible conformational space of glycosidic linkages

Glycan fragment database: A database of PDB-based glycan 3D structures

Diffraction and Crystallography of Oligosaccharides and Polysaccharides

Glycan structures and their interactions with proteins. A NMR view

Spin ballet for sweet encounters: Saturation-transfer difference NMR and X-ray crystallography complement each other in the elucidation of protein-glycan interactions

NMR of glycans: Shedding new light on old problems

Insights into Carbohydrate Recognition by 3D Structure Determination of Protein-Carbohydrate Complexes Using NMR

An Excellent Tool to Understand RNA and Carbohydrate Recognition by Proteins

Molecular Insights into DC-SIGN Binding to Self-Antigens: The Interaction with the Blood Group A/B Antigens

A Secondary Structural Element in a Wide Range of Fucosylated Glycoepitopes

Stabilization of Branched Oligosaccharides: Lewisx Benefits from a Nonconventional C-H···O Hydrogen Bond

Is N-acetyl-d-glucosamine a rigid 4C1 chair? Glycobiology

Specific rotation of monosaccharides: A global property bringing local information

Conformational analysis of xylobiose by DFT quantum mechanics

Stereoelectronic Effects Impact Glycan Recognition

Use of Circular Statistics To Model αMan-(1→2)-αMan and αMan-(1→3)-α/βMan O-Glycosidic Linkage Conformation in 13C-Labeled Disaccharides and High-Mannose Oligosaccharides

The Carbohydrate-Binding Site in Galectin-3 Is Preorganized To Recognize a Sugarlike Framework of Oxygens: Ultra-High-Resolution Structures and Water Dynamics

An NMR and MD study of complexes of bacteriophage lambda lysozyme with tetra-and hexa-N-acetylchitohexaose

Modeling of Oligosaccharides within Glycoproteins from Free-Energy Landscapes

Conformational Populations of β-(1→4) O-Glycosidic Linkages Using Redundant NMR J-Couplings and Circular Statistics

Shaping up for structural glycomics: A predictive protocol for oligosaccharide conformational analysis applied to N-linked glycans

Conformational Analysis of Carbohydrates-A Historical Overview

Predicting Carbohydrate 3D Structures Using Theoretical Methods

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

Computerized Models of Carbohydrates

Computerized Molecular Modeling of Carbohydrates

Application of molecular dynamics simulation in food carbohydrate research-A review

Monte Carlo-based searching as a tool to study carbohydrate structure

Combined Monte Carlo/torsion-angle molecular dynamics for ensemble modeling of proteins, nucleic acids and carbohydrates

Structures and energies of D-galactose and galabiose conformers as calculated by ab initio and semiempirical methods

Ring Puckering: A Metric for Evaluating the Accuracy of AM1, PM3, PM3CARB-1, and SCC-DFTB Carbohydrate QM/MM Simulations

AM1/d-CB1: A Semiempirical Model for QM/MM Simulations of Chemical Glycobiology Systems

Evaluating AM1/d-CB1 for Chemical Glycobiology QM/MM Simulations

Correlated ab initio quantum chemical calculations of di-and trisaccharide conformations

Conformational analysis of cellobiose by electronic structure theories

DFT Energy Optimization of a Large Carbohydrate: Cyclomaltohexaicosaose (CA-26)

Ab Initio Study of Molecular Interactions in Cellulose Iα

Aqueous-Phase Conformations of Lactose, Maltose, and Sucrose and the Assessment of Low-Cost DFT Methods with the DSCONF Set of Conformers for the Three Disaccharides

Computational analysis of carbohydrate recognition based on hybrid QM/MM modeling: A case study of norovirus capsid protein in complex with Lewis antigen

Structure and conformation of α-, β-and γ-cyclodextrin in solution: Theoretical approaches and experimental validation

Reaction Mechanisms in Carbohydrate-Active Enzymes: Glycoside Hydrolases and Glycosyltransferases. Insights from ab Initio Quantum Mechanics/Molecular Mechanics Dynamic Simulations

Atomistic insight into the catalytic mechanism of glycosyltransferases by combined quantum mechanics/molecular mechanics (QM/MM) methods

Twisting of glycosidic bonds by hydrolases

The ONIOM Method and Its Applications

Scopus database: A review

Bioinformatics and molecular modeling in glycobiology

Simulation of Carbohydrates, from Molecular Docking to Dynamics in Water

Chapter 1-Carbohydrate-Protein Interactions: Molecular Modeling Insights

Molecular simulations of carbohydrates and protein-carbohydrate interactions: Motivation, issues and prospects

The Conformational Properties of Methyl α-(2,8)-Di/Trisialosides and Their N-Acyl Analogues: Implications for Anti-Neisseria meningitidis B Vaccine Design

Conformational flexibility of N-glycans in solution studied by REMD simulations

Conformational Properties of α-or β-(1→6)-Linked Oligosaccharides: Hamiltonian Replica Exchange MD Simulations and NMR Experiments

Enhanced conformational sampling of carbohydrates by Hamiltonian replica-exchange simulation

Influence of Solvent and Intramolecular Hydrogen Bonding on the Conformational Properties of O-Linked Glycopeptides

Identification of Rare Lewis Oligosaccharide Conformers in Aqueous Solution Using Enhanced Sampling Molecular Dynamics

Ring Puckering Landscapes of Glycosaminoglycan-Related Monosaccharides from Molecular Dynamics Simulations

The mechanism of high affinity pentasaccharide binding to antithrombin, insights from Gaussian accelerated molecular dynamics simulations

Comparison of Carbohydrate Force Fields Using Gaussian Accelerated Molecular Dynamics Simulations and Development of Force Field Parameters for Heparin-Analogue Pentasaccharides

Conformational Analysis of a High-Mannose-Type Oligosaccharide Displaying Glucosyl Determinant Recognised by Molecular Chaperones Using NMR-Validated Molecular Dynamics Simulation

Exploration of Conformational Spaces of High-Mannose-Type Oligosaccharides by an NMR-Validated Simulation

Carbohydrate force fields

Dispersion interactions of carbohydrates with condensate aromatic moieties: Theoretical study on the CH-π interaction additive properties

Carbohydrate-Aromatic Interactions in Proteins

The Dependence of Carbohydrate-Aromatic Interaction Strengths on the Structure of the Carbohydrate

Carbohydrate-Protein aromatic ring interactions beyond CH/π interactions: A Protein Data Bank survey and quantum chemical calculations

Importance of ligand conformational energies in carbohydrate docking: Sorting the wheat from the chaff

Improving Glycosidic Angles during Carbohydrate Docking

11-Molecular Modeling in Glycoscience

Disaccharide conformational maps: Adiabaticity in analogues with variable ring shapes

Molecular mechanics. The MM3 force field for hydrocarbons. 1

A molecular mechanics force field (MM3) for alcohols and ethers

Comparative performance of MM3(92) and two TINKER™ MM3 versions for the modeling of carbohydrates

Comparison of different force fields for the study of disaccharides

Conformational analysis of furanoside-containing mono-and oligosaccharides

Additive effects in the modeling of oligosaccharides with mm3 at high dielectric constants: An approach to the 'multiple minimum problem'

mm3 Potential energy surfaces of trisaccharide models of λ-, µ-, and ν-carrageenans

Force fields and scoring functions for carbohydrate simulation

CHARMM Force Field Files

All-Atom Empirical Potential for Molecular Modeling and Dynamics Studies of Proteins

Extending the treatment of backbone energetics in protein force fields: Limitations of gas-phase quantum mechanics in reproducing protein conformational distributions in molecular dynamics simulations

CHARMM Additive All-Atom Force Field for Glycosidic Linkages between Hexopyranoses

Additive empirical force field for hexopyranose monosaccharides

CHARMM Additive All-Atom Force Field for Glycosidic Linkages in Carbohydrates Involving Furanoses

CHARMM Additive All-Atom Force Field for Carbohydrate Derivatives and Its Utility in Polysaccharide and Carbohydrate-Protein Modeling

CHARMM Additive All-Atom Force Field for Phosphate and Sulfate Linked to Carbohydrates

Kirkwood-Buff-Derived Alcohol Parameters for Aqueous Carbohydrates and Their Application to Preferential Interaction Coefficient Calculations of Proteins

GLYCAM06: A generalizable biomolecular force field. Carbohydrates

Extension of the GLYCAM06 biomolecular force field to lipids, lipid bilayers and glycolipids

Atomic-resolution conformational analysis of the GM3 ganglioside in a lipid bilayer and its implications for ganglioside-protein recognition at membrane surfaces

Molecular Dynamics Simulations of Membrane-and Protein-Bound Glycolipids Using GLYCAM

A Glycam-Based Force Field for Simulations of Lipopolysaccharide Membranes: Parametrization and Validation

Extension and validation of the GLYCAM force field parameters for modeling glycosaminoglycans

A new GROMOS force field for hexopyranose-based carbohydrates

A reoptimized GROMOS force field for hexopyranose-based carbohydrates accounting for the relative free energies of ring conformers, anomers, epimers, hydroxymethyl rotamers, and glycosidic linkage conformers

The Effect of Temperature, Cations, and Number of Acyl Chains on the Lamellar to Non-Lamellar Transition in Lipid-A Membranes: A Microscopic View

GROMOS 53A6GLYC, an Improved GROMOS Force Field for Hexopyranose-Based Carbohydrates

Extension and validation of the GROMOS 53A6glyc parameter set for glycoproteins

Revision of the GROMOS 56A6CARBO force field: Improving the description of ring-conformational equilibria in hexopyranose-based carbohydrates chains

Modification of 56ACARBO force field for molecular dynamic calculations of chitosan and its derivatives

Extension of the GROMOS 56a6CARBO/CARBO_R Force Field for Charged, Protonated, and Esterified Uronates

A GROMOS Force Field for Furanose-Based Carbohydrates

GROMOS96 43a1 performance on the characterization of glycoprotein conformational ensembles through molecular dynamics simulations

GROMOS96 43a1 performance in predicting oligosaccharide conformational ensembles within glycoproteins

An improved OPLS-AA force field for carbohydrates

OPLS all-atom force field for carbohydrates

Development and Testing of the OPLS All-Atom Force Field on Conformational Energetics and Properties of Organic Liquids

Optimizing Nonbonded Interactions of the OPLS Force Field for Aqueous Solutions of Carbohydrates: How to Capture Both Thermodynamics and Dynamics

Polarizable Empirical Force Field for Hexopyranose Monosaccharides Based on the Classical Drude Oscillator

Proper balance of solvent-solute and solute-solute interactions in the treatment of the diffusion of glucose using the Drude polarizable force field

CHARMM Drude Polarizable Force Field for Aldopentofuranoses and Methyl-aldopentofuranosides

Drude Polarizable Force Field Parametrization of Carboxylate and N-Acetyl Amine Carbohydrate Derivatives

Polarizable Empirical Force Field for Acyclic Polyalcohols Based on the Classical Drude Oscillator

CHARMM Drude Polarizable Force Field for Glycosidic Linkages Involving Pyranoses and Furanoses

Martini Coarse-Grained Force Field: Extension to Carbohydrates

Overcoming the Limitations of the MARTINI Force Field in Simulations of Polysaccharides

Extending the Martini Coarse-Grained Forcefield to N-Glycans

Martini Force Field Parameters for Glycolipids

PITOMBA: Parameter Interface for Oligosaccharide Molecules Based on Atoms

Modelling of carbohydrate-aromatic interactions: Ab initio energeticsand force field performance

Stacking interactions between carbohydrate and protein quantified by combination of theoretical and experimental methods

Applying Pose Clustering and MD Simulations To Eliminate False Positives in Molecular Docking

Scoring functions and their evaluation methods for protein-ligand docking: Recent advances and future directions

Aromatic-Carbohydrate Interactions: An NMR and Computational Study of Model Systems

A Gibbs free energy correlation for automated docking of carbohydrates

Protein-Carbohydrate Interactions

Docking glycosaminoglycans to proteins: Analysis of solvent inclusion

Flexibility and Explicit Solvent in Molecular-Dynamics-Based Docking of Protein-Glycosaminoglycan Systems

Mannobiose Binding Induces Changes in Hydrogen Bonding and Protonation States of Acidic Residues in Concanavalin a As Revealed by Neutron Crystallography

Improvements, trends, and new ideas in molecular docking: 2012-2013 in review

Recognition of selected monosaccharides by Pseudomonas aeruginosa Lectin II analyzed by molecular dynamics and free energy calculations

in silico Mutagenesis and Docking Study of Ralstonia solanacearum RSL Lectin: Performance of Docking Software to Predict Saccharide Binding

Finding a Needle in a Haystack: Development of a Combinatorial Virtual Screening Approach for Identifying High Specificity Heparin/Heparan Sulfate Sequence(s)

Computational analysis of interactions in structurally available protein-glycosaminoglycan complexes

Identification and characterization of a glycosaminoglycan binding site on interleukin-10 via molecular simulation methods

A. in silico analysis of antibody-carbohydrate interactions and its application to xenoreactive antibodies

CHARMM-GUI Input Generator for NAMD, GROMACS, AMBER, OpenMM, and CHARMM/OpenMM Simulations Using the CHARMM36 Additive Force Field

CHARMM-GUI supports the Amber force fields

Residue-centric modeling and design of saccharide and glycoconjugate structures

Efficient Construction of Atomic-Resolution Models of Non-Sulfated Chondroitin Glycosaminoglycan Using Molecular Dynamics Data

CHARMM-GUI Glycan Modeler for modeling and simulation of carbohydrates and glycoconjugates

Glycosylator: A Python framework for the rapid modeling of glycans

The Rosetta All-Atom Energy Function for Macromolecular Modeling and Design

Novel sampling strategies and a coarse-grained score function for docking homomers, flexible heteromers, and oligosaccharides using Rosetta in CAPRI rounds 37-45

Macromolecular modeling and design in Rosetta: Recent methods and frameworks

Azahar: A PyMOL plugin for construction, visualization and analysis of glycan molecules

Automatic conformation prediction of carbohydrates using a genetic algorithm

Rapid Generation of a Representative Ensemble of N-Glycan Conformations

The use of a genetic algorithm search for molecular mechanics (MM3)-based conformational analysis of oligosaccharides

Sugar Folding: A Novel Structural Prediction Tool for Oligosaccharides and Polysaccharides 1

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

A tool for the prediction of structures of complex sugars

Computational Study of the Conformational Structures of Saccharides in Solution Based on J Couplings and the "Fast Sugar Structure Prediction Software

GlyProt: in silico glycosylation of proteins

Macromolecular structure determination using X-rays, neutrons and electrons: Recent developments in Phenix

Computational screening of the human TF-glycome provides a structural definition for the specificity of anti-tumor antibody JAA-F11

Presentation, presentation, presentation! Molecular-level insight into linker effects on glycan array screening data

Recent advances in employing molecular modelling to determine the specificity of glycan-binding proteins

Gly-Spec: A webtool for predicting glycan specificity by integrating glycan array screening data and 3D structure

Combining 3D structure with glycan array data provides insight into the origin of glycan specificity

Automated builder and database of protein/membrane complexes for molecular dynamics simulations

Membrane Builder for mixed bilayers and its application to yeast membranes

CHARMM-GUI Membrane Builder toward realistic biological membrane simulations

Modeling and Simulation of Bacterial Outer Membranes with Lipopolysaccharides and Enterobacterial Common Antigen

The gram-negative outer membrane modeler: Automated building of lipopolysaccharide-rich bacterial outer membranes in four force fields

Maker: An Online Tool for Generating Equilibrated Micelles as Direct Input for Molecular Dynamics Simulations

Probabilistic identification of saccharide moieties in biomolecules and their protein complexes

New Online Tools for Locating and Curating Carbohydrate Structures in wwPDB

Tools to Find Glycoproteins in the Protein Data Bank and Generate Realistic 3D Structures for Them

Data mining the protein data bank: Automatic detection and assignment of carbohydrate structures

Glycan reader: Automated sugar identification and simulation preparation for carbohydrates and glycoproteins

Glycan Reader is improved to recognize most sugar types and chemical modifications in the Protein Data Bank

Vattulainen, I. doGlycans-Tools for Preparing Carbohydrate Structures for Atomistic Simulations of Glycoproteins, Glycolipids, and Carbohydrate Polymers for GROMACS

SWEET-WWW-based rapid 3D construction of oligo-and polysaccharides

Automated translation of glycan sequences from residue-based notation to SMILES and atomic coordinates

A molecular builder for carbohydrates: Application to polysaccharides and complex carbohydrates

0: An open source software package for building three-dimensional structures of polysaccharides

An Adjustable Tool for Building 3D Molecular Structures of Carbohydrates for Molecular Simulation

Software for building molecular models of complex oligoand polysaccharide structures

A pipeline to translate glycosaminoglycan sequences into 3D models. Application to the exploration of glycosaminoglycan conformational space

A web-tool for modeling 3D structures of glycosaminoglycans

SLICK-Scoring and Energy Functions for Protein−Carbohydrate Interactions

BALLDock/SLICK: A New Method for Protein-Carbohydrate Docking

The HADDOCK2.2 Web Server: User-Friendly Integrative Modeling of Biomolecular Complexes

Docking Server for the Identification of Heparin Binding Sites on Proteins

So you think computational approaches to understanding glycosaminoglycan-protein interactions are too dry and too rigid? Think again!

Predicting glycosaminoglycan surface protein interactions and implications for studying axonal growth

Improved Docking of Sulfated Sugars Using QM-derived Scoring Functions

Conformational Analysis of Oligosaccharides and Polysaccharides Using Molecular Dynamics Simulations

A Method for Discretizing and Visualizing Pyranose Conformations

Direct NOE simulation from long MD trajectories

GS-align for glycan structure alignment and similarity measurement

Analysis of carbohydrate 3D structures derived from the PDB

Statistical Analysis of Amino Acids in the Vicinity of Carbohydrate Residues Performed by GlyVicinity

Rules of Engagement of Protein-Glycoconjugate Interactions: A Molecular View Achievable by using NMR Spectroscopy and Molecular Modeling

Conformational Studies of Oligosaccharides

Structure, Conformation, and Dynamics of Bioactive Oligosaccharides: Theoretical Approaches and Experimental Validations

Conformational Studies of Oligosaccharides and Glycopeptides: Complementarity of NMR, X-ray Crystallography, and Molecular Modelling

Analysis and validation of carbohydrate three-dimensional structures

An efficient use of X-ray information, homology modeling, molecular dynamics and knowledge-based docking techniques to predict protein-monosaccharide complexes

Chapter 3 Developments in the Karplus Equation as they Relate to the NMR Coupling Constants of Carbohydrates

A perspective on the primary and three-dimensional structures of carbohydrates

NMR Structure Determination of a Segmentally Labeled Glycoprotein Using In vitro Glycosylation

NMR structural biology of sulfated glycans

Making glycoproteins a little bit sweeter with PDB-REDO

Automatically Fixing Errors in Glycoprotein Structures with Rosetta

Leveraging glycomics data in glycoprotein 3D structure validation with Privateer

Current developments in Coot for macromolecular model building of Electron Cryo-microscopy and Crystallographic Data

Carbohydrate anomalies in the PDB

Numerous severely twisted N-acetylglucosamine conformations found in the protein databank

Structural glycobiology in the age of electron cryo-microscopy

Strategies for carbohydrate model building, refinement and validation

Structures of Ebola virus GP and sGP in complex with therapeutic antibodies

Cryo-EM structure of a native, fully glycosylated, cleaved HIV-1 envelope trimer

Cross-neutralization of SARS-CoV-2 by a human monoclonal SARS-CoV antibody

Structure, Function, and Antigenicity of the SARS-CoV-2 Spike Glycoprotein

NMR spectroscopy in the study of carbohydrates: Characterizing the structural complexity

Recent advances in the application of NMR methods to uncover the conformation and recognition features of glycans. In Carbohydrate Chemistry

The recognition of glycans by protein receptors. Insights from NMR spectroscopy

Novel NMR Avenues to Explore the Conformation and Interactions of Glycans

Delineating the conformational flexibility of trisaccharides from NMR spectroscopy experiments and computer simulations

Conformational flexibility of the pentasaccharide LNF-2 deduced from NMR spectroscopy and molecular dynamics simulations

Molecular Conformations of Di-, Tri-, and Tetra-α-(2→8)-Linked Sialic Acid from NMR Spectroscopy and MD Simulations

An unusual carbohydrate conformation is evident in Moraxella catarrhalis oligosaccharides

Protein Data Bank (PDB): The single global macromolecular structure archive

The Cambridge Structural Database

Current Status of Carbohydrates Information in the Protein Data Bank

Data Mining the PDB for Glyco-Related Data

Carbohydrate structure: The rocky road to automation

Building meaningful models of glycoproteins

Carbohydrate 3D structure validation

Dissecting conformational contributions to glycosidase catalysis and inhibition

Conformations of the type-1 lacto-N-biose I unit in protein complex structures

Collaborating with Glycoscience Community To Improve Data Representation of Carbohydrates in the Protein Data Bank

Building and rebuilding N-glycans in protein structure models

pdb-care (PDB carbohydrate residue check): A program to support annotation of complex carbohydrate structures in PDB files

Crystallography & NMR System: A New Software Suite for Macromolecular Structure Determination

Version 1.2 of the Crystallography and NMR system

Tools to Assist Determination and Validation of Carbohydrate 3D Structure Data

Compatible topologies and parameters for NMR structure determination of carbohydrates by simulated annealing

Structural analysis of glycoproteins: Building N-linked glycans with Coot

Software for the conformational validation of carbohydrate structures

MotiveValidator: Interactive web-based validation of ligand and residue structure in biomolecular complexes

Database of up-to-date validation results for ligands and non-standard residues from the Protein Data Bank

A Practical Guide to Using Glycomics Databases

Implementation of GlycanBuilder to draw a wide variety of ambiguous glycans

A glycan visualizing, drawing and naming application

Symbol nomenclature for graphical representations of glycans

Updates to the Symbol Nomenclature For Glycans (SNFG) Guidelines

Computational tools for drawing, building and displaying carbohydrates: A visual guide

The GlycanBuilder and GlycoWorkbench glycoinformatics tools: Updates and new developments

The RINGS Resource for Glycome Informatics Analysis and Data Mining on the Web

SugarSketcher: Quick and intuitive online glycan drawing

DrawGlycan-SNFG: A robust tool to render glycans and glycopeptides with fragmentation information

DrawGlycan-SNFG and gpAnnotate: Rendering glycans and annotating glycopeptide mass spectra

The PyMOL Molecular Graphics System

Description of several chemical structure file formats used by computer programs developed at Molecular Design Limited

Protein Data Bank Contents Guide: Atomic Coordinate Entry Format Description. Brookhaven Natl. Lab

A Web Tool to Create Animated Images of Molecules

Biomolecular graphics for all

Fast and Scriptable Molecular Graphics in Web Browsers without Java3D. Nat. Prec

A web application for molecular visualization

NGL viewer: Web-based molecular graphics for large complexes

LiteMol suite: Interactive web-based visualization of large-scale macromolecular structure data

Mol: Towards a Common Library and Tools for Web Molecular Graphics

UniProt: A worldwide hub of protein knowledge

Eborn, I. Techniques for visualization of carbohydrate molecules

Visualisation of cyclic and multi-branched molecules with VMD

Rendering Carbohydrate Cartoons

VMD: Visual molecular dynamics

Three-dimensional representations of complex carbohydrates and polysaccharides-SweetUnityMol: A video game-based computer graphic software

New visualization of dynamical flexibility of N-Glycans: Umbrella Visualization in UnityMol

Umbrella Visualization: A method of analysis dedicated to glycan flexibility with UnityMol

Rapidly Display Glycan Symbols in 3D Structures: 3D-SNFG in LiteMol

3D implementation of the symbol nomenclature for graphical representation of glycans

UCSF Chimera-A visualization system for exploratory research and analysis

Presenting your structures: The CCP4mg molecular-graphics software

Glycoblocks: A schematic three-dimensional representation for glycans and their interactions

Glycan synthesis, structure, and dynamics: A selection

Symbol nomenclature for glycan representation

Development of Carbohydrate Nomenclature and Representation. In A Practical Guide to Using Glycomics Databases