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

The future of plant lectinology: Advanced technologies and computational tools

Lectins play crucial roles in many biological processes and serve as tools in fields ranging from agriculture to biomedicine. While classical methods for lectin discovery and characterization were foundational for the field, they often lack sensitivity and throughput, limiting the detection of less abundant or weakly binding lectins, such as the stress-inducible or monovalent lectins. This review focuses on recent advancements in plant lectin research, particularly novel technologies that complement traditional approaches. Techniques such as glycan microarrays allow rapid assessment of lectin specificity across a diverse range of glycans by evaluating interactions with immobilized glycans on solid surfaces. Phage display libraries enable the identification of carbohydrate-mimetic peptides and the development of ligands for lectins by presenting diverse peptide libraries on bacteriophages. Genomic and transcriptomic analyses facilitate the exploration of the lectome in various plant species by scanning entire datasets to identify genes that contain lectin motifs-specific conserved amino acid sequences involved in carbohydrate recognition-and lectin domains, the larger structural regions that facilitate and stabilize these interactions. Additionally, computational methods-including molecular docking, molecular dynamics simulations, and machine learning pipelines-support predictions of lectin structures and binding properties, underpinning experimental efforts. These advanced techniques bring increased efficiency, accuracy, and a broader scope to lectin studies, with potential impacts across multiple fields. However, challenges such as data complexity and the need for experimental validation for computational methods remain. The future of lectin research will depend on the integration of these methods and the strengthening of interdisciplinarity to unlock the full potential of lectins.

Computational modeling of protein-carbohydrate interactions: Current trends and future challenges

The article leads the reader through an up-to-date presentation of the concepts, developments, and main applications of computational modeling to study protein-carbohydrate interactions. It follows with the presentation of some current issues and perspectives arising from the expected evolution of generic methodological developments in deep learning, immersive analytics, and virtual reality for molecular visualization and data management. Such methodological developments for macromolecular interactions would greatly benefit a wide range of scientific endeavors in the field of carbohydrate chemistry and biochemistry, including the following interrelated efforts dealing with highly crowded media, with examples concerning glycoside transferases, the extracellular matrix, and the exploration of interactions between complex carbohydrates and intrinsically disordered proteins.

Multifaceted Computational Modeling in Glycoscience

Glycoscience assembles all the scientific disciplines involved in studying various molecules and macromolecules containing carbohydrates and complex glycans. Such an ensemble involves one of the most extensive sets of molecules in quantity and occurrence since they occur in all microorganisms and higher organisms. Once the compositions and sequences of these molecules are established, the determination of their three-dimensional structural and dynamical features is a step toward understanding the molecular basis underlying their properties and functions. The range of the relevant computational methods capable of addressing such issues is anchored by the specificity of stereoelectronic effects from quantum chemistry to mesoscale modeling throughout molecular dynamics and mechanics and coarse-grained and docking calculations. The Review leads the reader through the detailed presentations of the applications of computational modeling. The illustrations cover carbohydrate-carbohydrate interactions, glycolipids, and N- and O-linked glycans, emphasizing their role in SARS-CoV-2. The presentation continues with the structure of polysaccharides in solution and solid-state and lipopolysaccharides in membranes. The full range of protein-carbohydrate interactions is presented, as exemplified by carbohydrate-active enzymes, transporters, lectins, antibodies, and glycosaminoglycan binding proteins. A final section features a list of 150 tools and databases to help address the many issues of structural glycobioinformatics.

Characterization of Sialic Acid Affinity of the Binding Domain of Mistletoe Lectin Isoform One

Sialic acid (Sia) is considered as one of the most important biomolecules of life since its derivatives and terminal orientations on cell membranes and macromolecules play a major role in many biological and pathological processes. To date, there is only a limited number of active molecules that can selectively bind to Sia and this limitation has made the study of this glycan challenging. The lectin superfamily is a well-known family of glycan binding proteins, which encompasses many strong glycan binding peptides with diverse glycan affinities. Mistletoe lectin (ML) is considered one of the most active members of lectin family which was initially classified in early studies as a galactose binding lectin; more recent studies have suggested that the peptide can also actively bind to Sia. However, the details with respect to Sia binding of ML and the domain responsible for this binding are left unanswered because no comprehensive studies have been instigated. In this study, we sought to identify the binding domain responsible for the sialic acid affinity of mistletoe lectin isoform I (MLI) in comparison to the binding activity of elderberry lectin isoform I (SNA), which has long been identified as a potent Sia binding lectin. In order to execute this, we performed computational carbohydrate-protein docking for MLB and SNA with Neu5Ac and β-Galactose. We further analyzed the coding sequence of both lectins and identified their glycan binding domains, which were later cloned upstream and downstream to green fluorescent protein (GFP) and expressed in Escherichia coli (E. coli). Finally, the glycan affinity of the expressed fusion proteins was assessed by using different biochemical and cell-based assays and the Sia binding domains were identified.

Development and Evaluation of GlycanDock: A Protein-Glycoligand Docking Refinement Algorithm in Rosetta

Carbohydrate chains are ubiquitous in the complex molecular processes of life. These highly diverse chains are recognized by a variety of protein receptors, enabling glycans to regulate many biological functions. High-resolution structures of protein-glycoligand complexes reveal the atomic details necessary to understand this level of molecular recognition and inform application-focused scientific and engineering pursuits. When experimental challenges hinder high-throughput determination of quality structures, computational tools can, in principle, fill the gap. In this work, we introduce GlycanDock, a residue-centric protein-glycoligand docking refinement algorithm developed within the Rosetta macromolecular modeling and design software suite. We performed a benchmark docking assessment using a set of 109 experimentally determined protein-glycoligand complexes as well as 62 unbound protein structures. The GlycanDock algorithm can sample and discriminate among protein-glycoligand models of native-like structural accuracy with statistical reliability from starting structures of up to 7 Å root-mean-square deviation in the glycoligand ring atoms. We show that GlycanDock-refined models qualitatively replicated the known binding specificity of a bacterial carbohydrate-binding module. Finally, we present a protein-glycoligand docking pipeline for generating putative protein-glycoligand complexes when only the glycoligand sequence and unbound protein structure are known. In combination with other carbohydrate modeling tools, the GlycanDock docking refinement algorithm will accelerate research in the glycosciences.

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.

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

Norovirus is a major pathogen of nonbacterial acute gastroenteritis in humans and animals. Carbohydrate recognition between norovirus capsid proteins and Lewis antigens is considered to play a critical role in initiating infection of eukaryotic cells. In this article, we first report a detailed atomistic simulation study of the norovirus capsid protein in complex with the Lewis antigen based on ab initio QM/MM combined with MD-FEP simulations. To understand the mechanistic details of ligand binding, we analyzed and compared the carbohydrate recognition mechanism of the wild-type P domain protein with a mutant protein. Small structural differences between two capsid proteins are observed on the weak interaction site of residue 389, which is located on the solvent exposed surface of the P domain. To further clarify affinity differences in ligand binding, we directly evaluated free energy changes of the ligand binding process. Although the mutant protein loses its interaction energy with the Lewis antigen, this small amount of energy penalty is compensated for by an increase in the solvation stability, which is induced by structural reorganization at the ligand binding site on the protein surface. As a sum of these opposite energy components, the mutant P domain obtains a slightly enhanced binding affinity for the Lewis antigen. The present computational study clearly demonstrated that a detailed free energy balance of the interaction energy between the capsid protein and the surrounding aqueous solvent is the mechanistic basis of carbohydrate recognition in the norovirus capsid protein.

Sialic Acids in Nonenveloped Virus Infections

Sialic acid-based glycoconjugates cover the surfaces of many different cell types, defining key properties of the cell surface such as overall charge or likely interaction partners. Because of this prominence, sialic acids play prominent roles in mediating attachment and entry to viruses belonging to many different families. In this review, we first describe how interactions between viruses and sialic acid-based glycan structures can be identified and characterized using a range of techniques. We then highlight interactions between sialic acids and virus capsid proteins in four different viruses, and discuss what these interactions have taught us about sialic acid engagement and opportunities to interfere with binding.

Glycomics@ExPASy: Bridging the Gap

Glycomics@ExPASy (https://www.expasy.org/glycomics) is the glycomics tab of ExPASy, the server of SIB Swiss Institute of Bioinformatics. It was created in 2016 to centralize web-based glycoinformatics resources developed within an international network of glycoscientists. The hosted collection currently includes mainly databases and tools created and maintained at SIB but also links to a range of reference resources popular in the glycomics community. The philosophy of our toolbox is that it should be {glycoscientist AND protein scientist}-friendly with the aim of (1) popularizing the use of bioinformatics in glycobiology and (2) emphasizing the relationship between glycobiology and protein-oriented bioinformatics resources. The scarcity of data bridging these two disciplines led us to design tools as interactive as possible based on database connectivity to facilitate data exploration and support hypothesis building. Glycomics@ExPASy was designed, and is developed, with a long-term vision in close collaboration with glycoscientists to meet as closely as possible the growing needs of the community for glycoinformatics.

Predicting the Structures of Glycans, Glycoproteins, and Their Complexes

Complex carbohydrates are ubiquitous in nature, and together with proteins and nucleic acids they comprise the building blocks of life. But unlike proteins and nucleic acids, carbohydrates form nonlinear polymers, and they are not characterized by robust secondary or tertiary structures but rather by distributions of well-defined conformational states. Their molecular flexibility means that oligosaccharides are often refractory to crystallization, and nuclear magnetic resonance (NMR) spectroscopy augmented by molecular dynamics (MD) simulation is the leading method for their characterization in solution. The biological importance of carbohydrate-protein interactions, in organismal development as well as in disease, places urgency on the creation of innovative experimental and theoretical methods that can predict the specificity of such interactions and quantify their strengths. Additionally, the emerging realization that protein glycosylation impacts protein function and immunogenicity places the ability to define the mechanisms by which glycosylation impacts these features at the forefront of carbohydrate modeling. This review will discuss the relevant theoretical approaches to studying the three-dimensional structures of this fascinating class of molecules and interactions, with reference to the relevant experimental data and techniques that are key for validation of the theoretical predictions.

A combined computational-experimental approach to define the structural origin of antibody recognition of sialyl-Tn, a tumor-associated carbohydrate antigen

Anti-carbohydrate monoclonal antibodies (mAbs) hold great promise as cancer therapeutics and diagnostics. However, their specificity can be mixed, and detailed characterization is problematic, because antibody-glycan complexes are challenging to crystallize. Here, we developed a generalizable approach employing high-throughput techniques for characterizing the structure and specificity of such mAbs, and applied it to the mAb TKH2 developed against the tumor-associated carbohydrate antigen sialyl-Tn (STn). The mAb specificity was defined by apparent K_D values determined by quantitative glycan microarray screening. Key residues in the antibody combining site were identified by site-directed mutagenesis, and the glycan-antigen contact surface was defined using saturation transfer difference NMR (STD-NMR). These features were then employed as metrics for selecting the optimal 3D-model of the antibody-glycan complex, out of thousands plausible options generated by automated docking and molecular dynamics simulation. STn-specificity was further validated by computationally screening of the selected antibody 3D-model against the human sialyl-Tn-glycome. This computational-experimental approach would allow rational design of potent antibodies targeting carbohydrates.

Computational Chemistry Tools in Glycobiology: Modelling of Carbohydrate–Protein Interactions

Molecular modelling provides a major impact in the field of glycosciences, helping in the characterisation of the molecular basis of the recognition between lectins from pathogens and human glycoconjugates, and in the design of glycocompounds with anti-infectious properties. The conformational properties of oligosaccharides are complex, and therefore, the simulation of these properties is a challenging task. Indeed, the development of suitable force fields is required for the proper simulation of important problems in glycobiology, such as the interatomic interactions responsible for oligosaccharide and glycoprotein dynamics, including O-linkages in oligo- and polysaccharides, and N- and O-linkages in glycoproteins. The computational description of representative examples is discussed, herein, related to biologically active oligosaccharides and their interaction with lectins and other proteins, and the new routes open for the design of glycocompounds with promising biological activities.

Receptor uptake arrays for vitamin B12, siderophores, and glycans shape bacterial communities

Molecular variants of vitamin B₁₂, siderophores, and glycans occur. To take up variant forms, bacteria may express an array of receptors. The gut microbe Bacteroides thetaiotaomicron has three different receptors to take up variants of vitamin B₁₂ and 88 receptors to take up various glycans. The design of receptor arrays reflects key processes that shape cellular evolution. Competition may focus each species on a subset of the available nutrient diversity. Some gut bacteria can take up only a narrow range of carbohydrates, whereas species such as B. thetaiotaomicron can digest many different complex glycans. Comparison of different nutrients, habitats, and genomes provides opportunity to test hypotheses about the breadth of receptor arrays. Another important process concerns fluctuations in nutrient availability. Such fluctuations enhance the value of cellular sensors, which gain information about environmental availability and adjust receptor deployment. Bacteria often adjust receptor expression in response to fluctuations of particular carbohydrate food sources. Some species may adjust expression of uptake receptors for specific siderophores. How do cells use sensor information to control the response to fluctuations? This question about regulatory wiring relates to problems that arise in control theory and artificial intelligence. Control theory clarifies how to analyze environmental fluctuations in relation to the design of sensors and response systems. Recent advances in deep learning studies of artificial intelligence focus on the architecture of regulatory wiring and the ways in which complex control networks represent and classify environmental states. I emphasize the similar design problems that arise in cellular evolution, control theory, and artificial intelligence. I connect those broad conceptual aspects to many testable hypotheses for bacterial uptake of vitamin B₁₂, siderophores, and glycans.

Cytotoxic protein from the mushroom Coprinus comatus possesses a unique mode for glycan binding and specificity

Glycans possess significant chemical diversity; glycan binding proteins (GBPs) recognize specific glycans to translate their structures to functions in various physiological and pathological processes. Therefore, the discovery and characterization of novel GBPs and characterization of glycan-GBP interactions are significant to provide potential targets for therapeutic intervention of many diseases. Here, we report the biochemical, functional, and structural characterization of a 130-amino-acid protein, Y3, from the mushroom Coprinus comatus Biochemical studies of recombinant Y3 from a yeast expression system demonstrated the protein is a unique GBP. Additionally, we show that Y3 exhibits selective and potent cytotoxicity toward human T-cell leukemia Jurkat cells compared with a panel of cancer cell lines via inducing caspase-dependent apoptosis. Screening of a glycan array demonstrated GalNAcβ1-4(Fucα1-3)GlcNAc (LDNF) as a specific Y3-binding ligand. To provide a structural basis for function, the crystal structure was solved to a resolution of 1.2 Å, revealing a single-domain αβα-sandwich motif. Two monomers were dimerized to form a large 10-stranded, antiparallel β-sheet flanked by α-helices on each side, representing a unique oligomerization mode among GBPs. A large glycan binding pocket extends into the dimeric interface, and docking of LDNF identified key residues for glycan interactions. Disruption of residues predicted to be involved in LDNF/Y3 interactions resulted in the significant loss of binding to Jurkat T-cells and severely impaired their cytotoxicity. Collectively, these results demonstrate Y3 to be a GBP with selective cytotoxicity toward human T-cell leukemia cells and indicate its potential use in cancer diagnosis and treatment.

Calculations of the absolute binding free energies for Ralstonia solanacearum lectins bound with methyl-α-l-fucoside at molecular mechanical and quantum mechanical/molecular mechanical levels

In this work, both a molecular mechanical (MM) method and a hybrid quantum mechanical/molecular mechanical (QM/MM) method have been applied in the study of the binding affinities of methyl-α-l-fucoside to Ralstonia solanacearum lectins.

Glycans Confer Specificity to the Recognition of Ganglioside Receptors by Botulinum Neurotoxin A

The highly poisonous botulinum neurotoxins, produced by the bacterium Clostridium botulinum, act on their hosts by a high-affinity association to two receptors on neuronal cell surfaces as the first step of invasion. The glycan motifs of gangliosides serve as initial coreceptors for these protein complexes, whereby a membrane protein receptor is bound. Herein we set out to characterize the carbohydrate minimal binding epitope of the botulinum neurotoxin serotype A. By means of ligand-based NMR spectroscopy, X-ray crystallography, computer simulations, and isothermal titration calorimetry, a screening of ganglioside analogues together with a detailed characterization of various carbohydrate ligand complexes with the toxin were accomplished. We show that the representation of the glycan epitope to the protein affects the details of binding. Notably, both branches of the oligosaccharide GD1a can associate to botulinum neurotoxin serotype A when expressed as individual trisaccharides. It is, however, the terminal branch of GD1a as well as this trisaccharide motif alone, corresponding to the sialyl-Thomsen-Friedenreich antigen, that represents the active ligand epitope, and these compounds bind to the neurotoxin with a high degree of predisposition but with low affinities. This finding does not correlate with the oligosaccharide moieties having a strong contribution to the total affinity, which was expected to be the case. We here propose that the glycan part of the ganglioside receptors mainly provides abundance and specificity, whereas the interaction with the membrane itself and protein receptor brings about the strong total binding of the toxin to the neuronal membrane.

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

Understanding the dynamics of protein-ligand interactions, which lie at the heart of host-pathogen recognition, represents a crucial step to clarify the molecular determinants implicated in binding events, as well as to optimize the design of new molecules with therapeutic aims. Over the last decade, advances in complementary biophysical and spectroscopic methods permitted us to deeply dissect the fine structural details of biologically relevant molecular recognition processes with high resolution. This Review focuses on the development and use of modern nuclear magnetic resonance (NMR) techniques to dissect binding events. These spectroscopic methods, complementing X-ray crystallography and molecular modeling methodologies, will be taken into account as indispensable tools to provide a complete picture of protein-glycoconjugate binding mechanisms related to biomedicine applications against infectious diseases.

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

Defining how a glycan-binding protein (GBP) specifically selects its cognate glycan from among the ensemble of glycans within the cellular glycome is an area of intense study. Powerful insight into recognition mechanisms can be gained from 3D structures of GBPs complexed to glycans; however, such structures remain difficult to obtain experimentally. Here an automated 3D structure generation technique, called computational carbohydrate grafting, is combined with the wealth of specificity information available from glycan array screening. Integration of the array data with modeling and crystallography allows generation of putative co-complex structures that can be objectively assessed and iteratively altered until a high level of agreement with experiment is achieved. Given an accurate model of the co-complexes, grafting is also able to discern which binding determinants are active when multiple potential determinants are present within a glycan. In some cases, induced fit in the protein or glycan was necessary to explain the observed specificity, while in other examples a revised definition of the minimal binding determinants was required. When applied to a collection of 10 GBP-glycan complexes, for which crystallographic and array data have been reported, grafting provided a structural rationalization for the binding specificity of >90% of 1223 arrayed glycans. A webtool that enables researchers to perform computational carbohydrate grafting is available at www.glycam.org/gr (accessed 03 March 2016).

Vina-Carb: Improving Glycosidic Angles during Carbohydrate Docking

Molecular docking programs are primarily designed to align rigid, drug-like fragments into the binding sites of macromolecules and frequently display poor performance when applied to flexible carbohydrate molecules. A critical source of flexibility within an oligosaccharide is the glycosidic linkages. Recently, Carbohydrate Intrinsic (CHI) energy functions were reported that attempt to quantify the glycosidic torsion angle preferences. In the present work, the CHI-energy functions have been incorporated into the AutoDock Vina (ADV) scoring function, subsequently termed Vina-Carb (VC). Two user-adjustable parameters have been introduced, namely, a CHI- energy weight term (chi_coeff) that affects the magnitude of the CHI-energy penalty and a CHI-cutoff term (chi_cutoff) that negates CHI-energy penalties below a specified value. A data set consisting of 101 protein-carbohydrate complexes and 29 apoprotein structures was used in the development and testing of VC, including antibodies, lectins, and carbohydrate binding modules. Accounting for the intramolecular energies of the glycosidic linkages in the oligosaccharides during docking led VC to produce acceptable structures within the top five ranked poses in 74% of the systems tested, compared to a success rate of 55% for ADV. An enzyme system was employed in order to illustrate the potential application of VC to proteins that may distort glycosidic linkages of carbohydrate ligands upon binding. VC represents a significant step toward accurately predicting the structures of protein-carbohydrate complexes. Furthermore, the described approach is conceptually applicable to any class of ligands that populate well-defined conformational states.