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

Computational toolbox for the analysis of protein-glycan interactions

Protein-glycan interactions play pivotal roles in numerous biological processes, ranging from cellular recognition to immune response modulation. Understanding the intricate details of these interactions is crucial for deciphering the molecular mechanisms underlying various physiological and pathological conditions. Computational techniques have emerged as powerful tools that can help in drawing, building and visualising complex biomolecules and provide insights into their dynamic behaviour at atomic and molecular levels. This review provides an overview of the main computational tools useful for studying biomolecular systems, particularly glycans, both in free state and in complex with proteins, also with reference to the principles, methodologies, and applications of all-atom molecular dynamics simulations. Herein, we focused on the programs that are generally employed for preparing protein and glycan input files to execute molecular dynamics simulations and analyse the corresponding results. The presented computational toolbox represents a valuable resource for researchers studying protein-glycan interactions and incorporates advanced computational methods for building, visualising and predicting protein/glycan structures, modelling protein-ligand complexes, and analyse MD outcomes. Moreover, selected case studies have been reported to highlight the importance of computational tools in studying protein-glycan systems, revealing the capability of these tools to provide valuable insights into the binding kinetics, energetics, and structural determinants that govern specific molecular interactions.

A periodic table of monosaccharides

It is important to recognize the great diversity of monosaccharides commonly encountered in animals, plants, and microbes, as well as to organize them in a visually interesting style that also emphasizes their similarities and relatedness. This article discusses the nature of building blocks, monosaccharides, and monosaccharide derivatives-terms commonly used in discussing "glycomolecules" found in nature. To aid in awareness of monosaccharide diversity, here is presented a Periodic Table of Monosaccharides. The rationale is given for construction of the Table and the selection of 103 monosaccharides, which is largely based on those presented in the KEGG and SNFG websites of monosaccharides, and includes room to enlarge as new discoveries are made. The Table should have educational value and is intended to capture the attention and foster imagination of those not very familiar with glycosciences, and encourage researchers to delve deeper into this fascinating area.

Introduction of a human- and keyboard-friendly N-glycan nomenclature

In the beginning was the word. But there were no words for N-glycans, at least, no simple words. Next to chemical formulas, the IUPAC code can be regarded as the best, most reliable and yet immediately comprehensible annotation of oligosaccharide structures of any type from any source. When it comes to N-glycans, the venerable IUPAC code has, however, been widely supplanted by highly simplified terms for N-glycans that count the number of antennae or certain components such as galactoses, sialic acids and fucoses and give only limited room for exact structure description. The highly illustrative - and fortunately now standardized - cartoon depictions gained much ground during the last years. By their very nature, cartoons can neither be written nor spoken. The underlying machine codes (e.g., GlycoCT, WURCS) are definitely not intended for direct use in human communication. So, one might feel the need for a simple, yet intelligible and precise system for alphanumeric descriptions of the hundreds and thousands of N-glycan structures. Here, we present a system that describes N-glycans by defining their terminal elements. To minimize redundancy and length of terms, the common elements of N-glycans are taken as granted. The preset reading order facilitates definition of positional isomers. The combination with elements of the condensed IUPAC code allows to describe even rather complex structural elements. Thus, this "proglycan" coding could be the missing link between drawn structures and software-oriented representations of N-glycan structures. On top, it may greatly facilitate keyboard-based mining for glycan substructures in glycan repositories.

GlycoDraw: a python implementation for generating high-quality glycan figures

Glycans are essential to all scales of biology, with their intricate structures being crucial for their biological functions. The structural complexity of glycans is communicated through simplified and unified visual representations according to the Symbol Nomenclature for Glycans (SNFGs) guidelines adopted by the community. Here, we introduce GlycoDraw, a Python-native implementation for high-throughput generation of high-quality, SNFG-compliant glycan figures with flexible display options. GlycoDraw is released as part of our glycan analysis ecosystem, glycowork, facilitating integration into existing workflows by enabling fully automated annotation of glycan-related figures and thus assisting the analysis of e.g. differential abundance data or glycomics mass spectra.

Protein glycosylation and glycoinformatics for novel biomarker discovery in neurodegenerative diseases

Glycosylation is a common post-translational modification of brain proteins including cell surface adhesion molecules, synaptic proteins, receptors and channels, as well as intracellular proteins, with implications in brain development and functions. Using advanced state-of-the-art glycomics and glycoproteomics technologies in conjunction with glycoinformatics resources, characteristic glycosylation profiles in brain tissues are increasingly reported in the literature and growing evidence shows deregulation of glycosylation in central nervous system disorders, including aging associated neurodegenerative diseases. Glycan signatures characteristic of brain tissue are also frequently described in cerebrospinal fluid due to its enrichment in brain-derived molecules. A detailed structural analysis of brain and cerebrospinal fluid glycans collected in publications in healthy and neurodegenerative conditions was undertaken and data was compiled to create a browsable dedicated set in the GlyConnect database of glycoproteins (https://glyconnect.expasy.org/brain). The shared molecular composition of cerebrospinal fluid with brain enhances the likelihood of novel glycobiomarker discovery for neurodegeneration, which may aid in unveiling disease mechanisms, therefore, providing with novel therapeutic targets as well as diagnostic and progression monitoring tools.

Glycoinformatics in the Artificial Intelligence Era

Artificial intelligence (AI) methods have been and are now being increasingly integrated in prediction software implemented in bioinformatics and its glycoscience branch known as glycoinformatics. AI techniques have evolved in the past decades, and their applications in glycoscience are not yet widespread. This limited use is partly explained by the peculiarities of glyco-data that are notoriously hard to produce and analyze. Nonetheless, as time goes, the accumulation of glycomics, glycoproteomics, and glycan-binding data has reached a point where even the most recent deep learning methods can provide predictors with good performance. We discuss the historical development of the application of various AI methods in the broader field of glycoinformatics. A particular focus is placed on shining a light on challenges in glyco-data handling, contextualized by lessons learnt from related disciplines. Ending on the discussion of state-of-the-art deep learning approaches in glycoinformatics, we also envision the future of glycoinformatics, including development that need to occur in order to truly unleash the capabilities of glycoscience in the systems biology era.

CSDB/SNFG Structure Editor: An Online Glycan Builder with 2D and 3D Structure Visualization

This article describes features, usage, and application of an CSDB/SNFG Structure Editor, a new online tool for quick and intuitive input of carbohydrate and derivative structures using Symbol Nomenclature for Glycans (SNFG). The Editor is built on a platform of the Carbohydrate Structure Database (CSDB) and relies on its online services via the dedicated web-API. The Editor allows building of oligo- and polymeric glycan structures and supports most features of natural glycans, such as underdetermined structures, alternative branches, repeating subunits, SMILES specification of atypical monomers, and others. The vocabulary of building blocks contains 600+ monomeric residues, including 327 monosaccharides. Support for SMILES allows input and visualization of chemical structures of virtually unlimited complexity. On the other hand, the interface follows the recognized GlycanBuilder style easy to novice users. The export feature includes support for CSDB Linear, GlycoCT, WURCS, SweetDB, and Glycam notations, SMILES codes, MOL/PDB atomic coordinate formats, raster and vector SNFG images, and on-the-fly visualization as 2D structural formulas and 3D molecular models. Integration of the Editor into any web-based glycoinformatics project is straightforward and simple, similarly to any other modern JavaScript application.

Molecular dynamic simulation: Structural insights of multi-stranded curdlan in aqueous solution

In this work, by using molecular dynamic simulation we provide microscale structure information which helps to reveal the molecular mechanisms concerning the multi-chain conformational behavior of short curdlan. Through simulations starting with different conformations of curldan dodecasaccharides, it is found that the right-handed triple helix is thermodynamically the most stable conformation in aqueous solutions, which is well maintained and stabilized by an inter-strand hydrogen bonding network of the C₂ hydroxyls. Unlike any predicted forms, the inter-strand hydrogen bonds exhibit a left-handed double helix pattern with preferred global orientations. Temperature REMD results suggest that the formation of triple helix is temperature sensitive, but the already formed triple helix is not. Investigation of curdlan with numbers of repeating units from 3 to 12 captures a critical value of 6, which in a way elucidates the relationship between the formation of triple helix and the chain length.

Oligator: a flexible interface to draw oligosaccharide structures and generate their theoretical tandem mass spectra

SUMMARY

Oligator is software designed to assist scientists in their exploration of MS/MS experiments, especially for oligosaccharides bearing unreferenced chemical substitutions. Through a graphical interface, users have the total flexibility to build a candidate glycan structure and produce the corresponding theoretical MS/MS spectrum in accordance with the usual ion nomenclature. The structural information is saved using standard notations, in text format, which facilitates the capitalization and exchange of data as well as any other processing of the information.

AVAILABILITY

Source code and user manual are freely available at https://github.com/vlollier/oligator.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Solving the structural puzzle of bacterial glycome

The analysis of the bacterial glycome (glycomics) is among the complex 'omics' analysis owing to the inherent difficulties in structural and functional characterization of glycans. The complexity and variability of bacterial glycans, spanning from simple carbohydrates to complex glycolipids, glycopeptides and glycoproteins, make their study a challenging research area. The last two decades have witnessed tremendous advances and development of highly sophisticated methods, in combination with optimized protocols and hyphenate techniques for the understanding of structure, conformations, dynamics and organization of microbial glycans. We here present an overview of the novel approaches that have massively improved our understanding of the carbohydrate-based world of bacteria.

Three-Dimensional Structures of Carbohydrates and Where to Find Them

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.