Simulation of carbohydrates, from molecular docking to dynamics in water

Re-Emergence of Circulation of Seasonal Influenza during COVID-19 Pandemic in Russia and Receptor Specificity of New and Dominant Clade 3C.2a1b.2a.2 A(H3N2) Viruses in 2021-2022

The circulation of seasonal influenza in 2020-2021 around the world was drastically reduced after the start of the COVID-19 pandemic and the implementation of mitigation strategies. The influenza virus circulation reemerged in 2021-2022 with the global spread of the new genetic clade 3C.2a1b.2a.2 of A(H3N2) viruses. The purpose of this study was to characterize influenza viruses in the 2021-2022 season in Russia and to analyze the receptor specificity properties of the 3C.2a1b.2a.2 A(H3N2) viruses. Clinical influenza samples were collected at the local Sanitary-and-Epidemiological Centers of Rospotrebnadzor. Whole genome sequencing was performed using NGS. The receptor specificity of hemagglutinin was evaluated using molecular modeling and bio-layer interferometry. Clinical samples from 854 cases of influenza A and B were studied; A(H3N2) viruses were in the majority of the samples. All genetically studied A(H3N2) viruses belonged to the new genetic clade 3C.2a1b.2a.2. Molecular modeling analysis suggested a higher affinity of hemagglutinin of 3C.2a1b.2a.2. A(H3N2) viruses to the α2,6 human receptor. In vitro analysis using a trisaccharide 6'-Sialyl-N-acetyllactosamine receptor analog did not resolve the differences in the receptor specificity of 3C.2a1b.2a.2 clade viruses from viruses belonging to the 3C.2a1b.2a.1 clade. Further investigation of the A(H3N2) viruses is required for the evaluation of their possible adaptive advantages. Constant monitoring and characterization of influenza are critical for epidemiological analysis.

In silico evidence of antiviral activity against SARS-CoV-2 main protease of oligosaccharides from Porphyridium sp

The coronavirus pandemic (COVID-19) has created an urgent need to develop effective strategies for prevention and treatment. In this context, therapies against protease M^pro, a conserved viral target, would be essential to contain the spread of the virus and reduce mortality. Using combined techniques of structure modelling, in silico docking and pharmacokinetics prediction, many compounds from algae were tested for their ability to inhibit the SARS-CoV-2 main protease and compared to the recent recognized drug Paxlovid. The screening of 27 algal molecules including 15 oligosaccharides derived from sulfated and non-sulphated polysaccharides, eight pigments and four poly unsaturated fatty acids showed high affinities to interact with the protein active site. Best candidates showing high docking scores in comparison with the reference molecule were sulfated tri-, tetra- and penta-saccharides from Porphyridium sp. exopolysaccharides (SEP). Structural and energetic analyses over 100 ns MD simulation demonstrated high SEP fragments-M^pro complex stability. Pharmacokinetics predictions revealed the prospects of the identified molecules as potential drug candidates.

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.

Marine Sulfated Polysaccharides as Promising Antiviral Agents: A Comprehensive Report and Modeling Study Focusing on SARS CoV-2

SARS-CoV-2 (severe acute respiratory syndrome coronavirus-2) is a novel coronavirus strain that emerged at the end of 2019, causing millions of deaths so far. Despite enormous efforts being made through various drug discovery campaigns, there is still a desperate need for treatments with high efficacy and selectivity. Recently, marine sulfated polysaccharides (MSPs) have earned significant attention and are widely examined against many viral infections. This article attempted to produce a comprehensive report about MSPs from different marine sources alongside their antiviral effects against various viral species covering the last 25 years of research articles. Additionally, these reported MSPs were subjected to molecular docking and dynamic simulation experiments to ascertain potential interactions with both the receptor-binding domain (RBD) of SARS CoV-2's spike protein (S-protein) and human angiotensin-converting enzyme-2 (ACE2). The possible binding sites on both S-protein's RBD and ACE2 were determined based on how they bind to heparin, which has been reported to exhibit significant antiviral activity against SARS CoV-2 through binding to RBD, preventing the virus from affecting ACE2. Moreover, our modeling results illustrate that heparin can also bind to and block ACE2, acting as a competitor and protective agent against SARS CoV-2 infection. Nine of the investigated MSPs candidates exhibited promising results, taking into consideration the newly emerged SARS CoV-2 variants, of which five were not previously reported to exert antiviral activity against SARS CoV-2, including sulfated galactofucan (1), sulfated polymannuroguluronate (SPMG) (2), sulfated mannan (3), sulfated heterorhamnan (8), and chondroitin sulfate E (CS-E) (9). These results shed light on the importance of sulfated polysaccharides as potential SARS-CoV-2 inhibitors.

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.

Computational prediction method to decipher receptor-glycoligand interactions in plant immunity

Microbial and plant cell walls have been selected by the plant immune system as a source of microbe- and plant damage-associated molecular patterns (MAMPs/DAMPs) that are perceived by extracellular ectodomains (ECDs) of plant pattern recognition receptors (PRRs) triggering immune responses. From the vast number of ligands that PRRs can bind, those composed of carbohydrate moieties are poorly studied, and only a handful of PRR/glycan pairs have been determined. Here we present a computational screening method, based on the first step of molecular dynamics simulation, that is able to predict putative ECD-PRR/glycan interactions. This method has been developed and optimized with Arabidopsis LysM-PRR members CERK1 and LYK4, which are involved in the perception of fungal MAMPs, chitohexaose (1,4-β-d-(GlcNAc)6 ) and laminarihexaose (1,3-β-d-(Glc)6 ). Our in silico results predicted CERK1 interactions with 1,4-β-d-(GlcNAc)6 whilst discarding its direct binding by LYK4. In contrast, no direct interaction between CERK1/laminarihexaose was predicted by the model despite CERK1 being required for laminarihexaose immune activation, suggesting that CERK1 may act as a co-receptor for its recognition. These in silico results were validated by isothermal titration calorimetry binding assays between these MAMPs and recombinant ECDs-LysM-PRRs. The robustness of the developed computational screening method was further validated by predicting that CERK1 does not bind the DAMP 1,4-β-d-(Glc)6 (cellohexaose), and then probing that immune responses triggered by this DAMP were not impaired in the Arabidopsis cerk1 mutant. The computational predictive glycan/PRR binding method developed here might accelerate the discovery of protein-glycan interactions and provide information on immune responses activated by glycoligands.

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.

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.

Conformational analysis of oligosaccharides and polysaccharides using molecular dynamics simulations

Complex carbohydrates usually have a large number of rotatable bonds and consequently a large number of theoretically possible conformations can be generated (combinatorial explosion). The application of systematic search methods for conformational analysis of carbohydrates is therefore limited to disaccharides and trisaccharides in a routine analysis. An alternative approach is to use Monte-Carlo methods or (high-temperature) molecular dynamics (MD) simulations to explore the conformational space of complex carbohydrates. This chapter describes how to use MD simulation data to perform a conformational analysis (conformational maps, hydrogen bonds) of oligosaccharides and how to build realistic 3D structures of large polysaccharides using Conformational Analysis Tools (CAT).

Designing "high-affinity, high-specificity" glycosaminoglycan sequences through computerized modeling

The prediction of high-affinity and/or high-specificity protein-glycosaminoglycan (GAG) interactions is an inherently difficult task, due to several factors including the shallow nature of the typical GAG-binding site and the inherent size, flexibility, diversity, and polydisperse nature of the GAG molecules. Here, we present a generally applicable methodology termed Combinatorial Library Virtual Screening (CVLS) that can identify potential high-affinity, high-specificity protein-GAG interactions from very large GAG combinatorial libraries and a suitable GAG-binding protein. We describe the CVLS approach along with the rationale behind it and provide validation for the method using the well-known antithrombin-thrombin-heparin system.

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

Impressive improvements in docking performance can be achieved by applying energy bonuses to poses in which glycan hydroxyl groups occupy positions otherwise preferred by bound waters. In addition, inclusion of glycosidic conformational energies allows unlikely glycan conformations to be appropriately penalized. A method for predicting the binding specificity of glycan-binding proteins has been developed, which is based on grafting glycan branches onto a minimal binding determinant in the binding site. Grafting can be used either to screen virtual libraries of glycans, such as the known glycome, or to identify docked poses of minimal binding determinants that are consistent with specificity data. The reviewed advances allow accurate modelling of carbohydrate-protein 3D co-complexes, but challenges remain in ranking the affinity of congeners.

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

Docking algorithms that aim to be applicable to a broad range of ligands suffer reduced accuracy because they are unable to incorporate ligand-specific conformational energies. Here, we develop a set of Carbohydrate Intrinsic (CHI) energy functions that quantify the conformational properties of oligosaccharides, based on the values of their glycosidic torsion angles. The relative energies predicted by the CHI energy functions mirror the conformational distributions of glycosidic linkages determined from a survey of oligosaccharide-protein complexes in the protein data bank. Addition of CHI energies to the standard docking scores in Autodock 3, 4.2, and Vina consistently improves pose ranking of oligosaccharides docked to a set of anticarbohydrate antibodies. The CHI energy functions are also independent of docking algorithm, and with minor modifications, may be incorporated into both theoretical modeling methods, and experimental NMR or X-ray structure refinement programs.

Structural insights into RbmA, a biofilm scaffolding protein of V. cholerae

V. cholerae can form sessile biofilms associated with abiotic surfaces, cyanobacteria, zoo-plankton, mollusks, or crustaceans. Along with the vibrio polysaccharide, secreted proteins of the rbm gene cluster are key to the biofilm ultrastructure. Here we provide a thorough structural characterization of RbmA, a protein involved in mediating cell-cell and cell-biofilm contacts. We correlate our structural findings with initial ligand specificity screening results, NMR protein-ligand interaction analysis, and complement our results with a full biocomputational study.

Mapping of heparin/heparan sulfate binding sites on αvβ3 integrin by molecular docking

Heparin/heparan sulfate interact with growth factors, chemokines, extracellular proteins, and receptors. Integrins are αβ heterodimers that serve as receptors for extracellular proteins, regulate cell behavior, and participate in extracellular matrix assembly. Heparin binds to RGD-dependent integrins (αIIbβ3, α5β1, αvβ3, and αvβ5) and to RGD-independent integrins (α4β1, αXβ2, and αMβ2), but their binding sites have not been located on integrins. We report the mapping of heparin binding sites on the ectodomain of αvβ3 integrin by molecular modeling. The surface of the ectodomain was scanned with small rigid probes mimicking the sulfated domains of heparan sulfate. Docking results were clustered into binding spots. The best results were selected for further docking simulations with heparin hexasaccharide. Six potential binding spots containing lysine and/or arginine residues were identified on the ectodomain of αvβ3 integrin. Heparin would mostly bind to the top of the genu domain, the Calf-I domain of the α subunit, and the top of the β subunit of RGD-dependent integrins. Three spots were close enough from each other on the integrin surface to form an extended binding site that could interact with heparin/heparan sulfate chains. Because heparin does not bind to the same integrin site as protein ligands, no steric hindrance prevents the formation of ternary complexes comprising the integrin, its protein ligand, and heparin/heparan sulfate. The basic amino acid residues predicted to interact with heparin are conserved in the sequences of RGD-dependent but not of RGD-independent integrins suggesting that heparin/heparan sulfate could bind to different sites on these two integrin subfamilies.