Molecular simulations of complex carbohydrates and glycoconjugates

Advancing plant cell wall modelling: Atomistic insights into cellulose, disordered cellulose, and hemicelluloses - A review

The complexity of plant cell walls on different hierarchical levels still impedes the detailed understanding of biosynthetic pathways, interferes with processing in industry and finally limits applicability of cellulose materials. While there exist many challenges to readily accessing these hierarchies at (sub-) angström resolution, the development of advanced computational methods has the potential to unravel important questions in this field. Here, we summarize the contributions of molecular dynamics simulations in advancing the understanding of the physico-chemical properties of natural fibres. We aim to present a comprehensive view of the advancements and insights gained from molecular dynamics simulations in the field of carbohydrate polymers research. The review holds immense value as a vital reference for researchers seeking to undertake atomistic simulations of plant cell wall constituents. Its significance extends beyond the realm of molecular modeling and chemistry, as it offers a pathway to develop a more profound comprehension of plant cell wall chemistry, interactions, and behavior. By delving into these fundamental aspects, the review provides invaluable insights into future perspectives for exploration. Researchers within the molecular modeling and carbohydrates community can greatly benefit from this resource, enabling them to make significant strides in unraveling the intricacies of plant cell wall dynamics.

Developing and Benchmarking Sulfate and Sulfamate Force Field Parameters via Ab Initio Molecular Dynamics Simulations To Accurately Model Glycosaminoglycan Electrostatic Interactions

Glycosaminoglycans (GAGs) are negatively charged polysaccharides found on cell surfaces, where they regulate transport pathways of foreign molecules toward the cell. The structural and functional diversity of GAGs is largely attributed to varied sulfation patterns along the polymer chains, which makes understanding their molecular recognition mechanisms crucial. Molecular dynamics (MD) simulations, thanks to their unmatched microscopic resolution, have the potential to be a reference tool for exploring the patterns responsible for biologically relevant interactions. However, the capability of molecular dynamics force fields used in biosimulations to accurately capture sulfation-specific interactions is not well established, partly due to the intrinsic properties of GAGs that pose challenges for most experimental techniques. In this work, we evaluate the performance of molecular dynamics force fields for sulfated GAGs by studying ion pairing of Ca2+ to sulfated moieties─N-methylsulfamate and methylsulfate─that resemble N- and O-sulfation found in GAGs, respectively. We tested available nonpolarizable (CHARMM36 and GLYCAM06) and explicitly polarizable (Drude and AMOEBA) force fields, and derived new implicitly polarizable models through charge scaling (prosECCo75 and GLYCAM-ECC75) that are consistent with our developed "charge-scaling" framework. The calcium-sulfamate/sulfate interaction free energy profiles obtained with the tested force fields were compared against reference ab initio molecular dynamics (AIMD) simulations, which serve as a robust alternative to experiments. AIMD simulations indicate that the preferential Ca2+ binding mode to sulfated GAG groups is solvent-shared pairing. Only our scaled-charge models agree satisfactorily with the AIMD data, while all other force fields exhibit poorer agreement, sometimes even qualitatively. Surprisingly, even explicitly polarizable force fields display a notable disagreement with the AIMD data, likely attributed to difficulties in their optimization and possible inherent limitations in depicting high-charge-density ion interactions accurately. Finally, the underperforming force fields lead to unrealistic aggregation of sulfated saccharides, which qualitatively disagrees with our understanding of the soft glycocalyx environment. Our results highlight the importance of accurately treating electronic polarization in MD simulations of sulfated GAGs and caution against over-reliance on currently available models without thorough validation and optimization.

MDverse, shedding light on the dark matter of molecular dynamics simulations

The rise of open science and the absence of a global dedicated data repository for molecular dynamics (MD) simulations has led to the accumulation of MD files in generalist data repositories, constituting the dark matter of MD - data that is technically accessible, but neither indexed, curated, or easily searchable. Leveraging an original search strategy, we found and indexed about 250,000 files and 2000 datasets from Zenodo, Figshare and Open Science Framework. With a focus on files produced by the Gromacs MD software, we illustrate the potential offered by the mining of publicly available MD data. We identified systems with specific molecular composition and were able to characterize essential parameters of MD simulation such as temperature and simulation length, and could identify model resolution, such as all-atom and coarse-grain. Based on this analysis, we inferred metadata to propose a search engine prototype to explore the MD data. To continue in this direction, we call on the community to pursue the effort of sharing MD data, and to report and standardize metadata to reuse this valuable matter.

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.

The Conformation of Glycosidic Linkages According to Various Force Fields: Monte Carlo Modeling of Polysaccharides Based on Extrapolation of Short-Chain Properties

The conformational features of the glycosidic linkage are the most important variable to consider when studying di-, oligo-, and polysaccharide molecules using molecular dynamics (MD) simulations. The accuracy of the theoretical model describing this degree of freedom influences the quality of the results obtained from MD calculations based on this model. This article focuses on the following two issues related to the conformation of the glycosidic linkage. First, we describe the results of a comparative analysis of the predictions of three carbohydrate-dedicated classical force fields for MD simulations, namely, CHARMM, GLYCAM, and GROMOS, in the context of different parameters of structural and energetic nature related to the conformation of selected types of glycosidic linkages, α(1 → 4), β(1 → 3), and β(1 → 4), connecting glucopyranose units. This analysis revealed several differences, mainly concerning the energy levels of the secondary and tertiary conformers and the linkage flexibility within the dominant exo-syn conformation for α(1 → 4) and β(1 → 3) linkages. Some aspects of the comparative analysis also included the newly developed, carbohydrate-dedicated Martini 3 coarse-grained force field. Second, to overcome the time-scale problem associated with sampling slow degrees of freedom in polysaccharide chains during MD simulations, we developed a coarse-grained (CG) model based on the data from MD simulations and designed for Monte Carlo modeling. This model (CG MC) is based on information from simulations of short saccharide chains, effectively sampled in atomistic MD simulations, and is capable of extrapolating local conformational properties to the case of polysaccharides of arbitrary length. The CG MC model has the potential to estimate the conformations of very long polysaccharide chains, taking into account the influence of secondary and tertiary conformations of glycosidic linkages. With respect to the comparative analysis of force fields, the application of CG MC modeling showed that relatively small differences in the predictions of individual force fields with respect to a single glycosidic linkage accumulate when considering their effect on the structure of longer chains, leading to drastically different predictions with respect to parameters describing the polymer conformation, such as the persistence length.

Role of N343 glycosylation on the SARS-CoV-2 S RBD structure and co-receptor binding across variants of concern

Glycosylation of the SARS-CoV-2 spike (S) protein represents a key target for viral evolution because it affects both viral evasion and fitness. Successful variations in the glycan shield are difficult to achieve though, as protein glycosylation is also critical to folding and structural stability. Within this framework, the identification of glycosylation sites that are structurally dispensable can provide insight into the evolutionary mechanisms of the shield and inform immune surveillance. In this work, we show through over 45 μs of cumulative sampling from conventional and enhanced molecular dynamics (MD) simulations, how the structure of the immunodominant S receptor binding domain (RBD) is regulated by N-glycosylation at N343 and how this glycan's structural role changes from WHu-1, alpha (B.1.1.7), and beta (B.1.351), to the delta (B.1.617.2), and omicron (BA.1 and BA.2.86) variants. More specifically, we find that the amphipathic nature of the N-glycan is instrumental to preserve the structural integrity of the RBD hydrophobic core and that loss of glycosylation at N343 triggers a specific and consistent conformational change. We show how this change allosterically regulates the conformation of the receptor binding motif (RBM) in the WHu-1, alpha, and beta RBDs, but not in the delta and omicron variants, due to mutations that reinforce the RBD architecture. In support of these findings, we show that the binding of the RBD to monosialylated ganglioside co-receptors is highly dependent on N343 glycosylation in the WHu-1, but not in the delta RBD, and that affinity changes significantly across VoCs. Ultimately, the molecular and functional insight we provide in this work reinforces our understanding of the role of glycosylation in protein structure and function and it also allows us to identify the structural constraints within which the glycosylation site at N343 can become a hotspot for mutations in the SARS-CoV-2 S glycan shield.

MDverse: Shedding Light on the Dark Matter of Molecular Dynamics Simulations

The rise of open science and the absence of a global dedicated data repository for molecular dynamics (MD) simulations has led to the accumulation of MD files in generalist data repositories, constituting the dark matter of MD - data that is technically accessible, but neither indexed, curated, or easily searchable. Leveraging an original search strategy, we found and indexed about 250,000 files and 2,000 datasets from Zenodo, Figshare and Open Science Framework. With a focus on files produced by the Gromacs MD software, we illustrate the potential offered by the mining of publicly available MD data. We identified systems with specific molecular composition and were able to characterize essential parameters of MD simulation such as temperature and simulation length, and could identify model resolution, such as all-atom and coarse-grain. Based on this analysis, we inferred metadata to propose a search engine prototype to explore the MD data. To continue in this direction, we call on the community to pursue the effort of sharing MD data, and to report and standardize metadata to reuse this valuable matter.

Glycans modulate lipid binding in Lili-Mip lipocalin protein: insights from molecular simulations and protein network analyses

The unique viviparous Pacific Beetle cockroaches provide nutrition to their embryo by secreting milk proteins Lili-Mip, a lipid-binding glycoprotein that crystallises in-vivo. The resolved in-vivo crystal structure of variably glycosylated Lili-Mip shows a classical Lipocalin fold with an eight-stranded antiparallel beta-barrel enclosing a fatty acid. The availability of physiologically unaltered glycoprotein structure makes Lili-Mip a very attractive model system to investigate the role of glycans on protein structure, dynamics, and function. Towards that end, we have employed all-atom molecular dynamics simulations on various glycosylated stages of a bound and free Lili-Mip protein and characterised the impact of glycans and the bound lipid on the dynamics of this glycoconjugate. Our work provides important molecular-level mechanistic insights into the role of glycans in the nutrient storage function of the Lili-Mip protein. Our analyses show that the glycans stabilise spatially proximal residues and regulate the low amplitude opening motions of the residues at the entrance of the binding pocket. Glycans also preserve the native orientation and conformational flexibility of the ligand. However, we find that either deglycosylation or glycosylation with high-mannose and paucimannose on the core glycans, which better mimic the natural insect glycosylation state, significantly affects the conformation and dynamics. A simple but effective distance- and correlation-based network analysis of the protein also reveals the key residues regulating the barrel's architecture and ligand binding characteristics in response to glycosylation.

Many Roles of Carbohydrates: A Computational Spotlight on the Coronavirus S Protein Binding

Glycosylation is one of the post-translational modifications with more than 50% of human proteins being glycosylated. The exact nature and chemical composition of glycans are inaccessible to X-ray or cryo-electron microscopy imaging techniques. Therefore, computational modeling studies and molecular dynamics must be used as a "computational microscope". The spike (S) protein of SARS-CoV-2 is heavily glycosylated, and a few glycans play a more functional role "beyond shielding". In this mini-review, we discuss computational investigations of the roles of specific S-protein and ACE2 glycans in the overall ACE2-S protein binding. We highlight different functions of specific glycans demonstrated in myriad computational models and simulations in the context of the SARS-CoV-2 virus binding to the receptor. We also discuss interactions between glycocalyx and the S protein, which may be utilized to design prophylactic polysaccharide-based therapeutics targeting the S protein. In addition, we underline the recent emergence of coronavirus variants and their impact on the S protein and its glycans.

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.

Glycoproteomic landscape and structural dynamics of TIM family immune checkpoints enabled by mucinase SmE

Mucin-domain glycoproteins are densely O-glycosylated and play critical roles in a host of biological functions. In particular, the T cell immunoglobulin and mucin-domain containing family of proteins (TIM-1, -3, -4) decorate immune cells and act as key regulators in cellular immunity. However, their dense O-glycosylation remains enigmatic, primarily due to the challenges associated with studying mucin domains. Here, we demonstrate that the mucinase SmE has a unique ability to cleave at residues bearing very complex glycans. SmE enables improved mass spectrometric analysis of several mucins, including the entire TIM family. With this information in-hand, we perform molecular dynamics (MD) simulations of TIM-3 and -4 to understand how glycosylation affects structural features of these proteins. Finally, we use these models to investigate the functional relevance of glycosylation for TIM-3 function and ligand binding. Overall, we present a powerful workflow to better understand the detailed molecular structures and functions of the mucinome.

Docking and Molecular Dynamics Simulations Clarify Binding Sites for Interactions of Novel Marine Sulfated Glycans with SARS-CoV-2 Spike Glycoprotein

The entry of SARS-CoV-2 into the host cell is mediated by its S-glycoprotein (SGP). Sulfated glycans bind to the SGP receptor-binding domain (RBD), which forms a ternary complex with its receptor angiotensin converting enzyme 2. Here, we have conducted a thorough and systematic computational study of the binding of four oligosaccharide building blocks from novel marine sulfated glycans (isolated from Pentacta pygmaea and Isostichopus badionotus) to the non-glycosylated and glycosylated RBD. Blind docking studies using three docking programs identified five potential cryptic binding sites. Extensive site-targeted docking and molecular dynamics simulations using two force fields confirmed only two binding sites (Sites 1 and 5) for these novel, highly charged sulfated glycans, which were also confirmed by previously published reports. This work showed the structural features and key interactions driving ligand binding. A previous study predicted Site 2 to be a potential binding site, which was not observed here. The use of several molecular modeling approaches gave a comprehensive assessment. The detailed comparative study utilizing multiple modeling approaches is the first of its kind for novel glycan-SGP interaction characterization. This study provided insights into the key structural features of these novel glycans as they are considered for development as potential therapeutics.

Dissecting the structural heterogeneity of proteins by native mass spectrometry

A single gene yields many forms of proteins via combinations of posttranscriptional/posttranslational modifications. Proteins also fold into higher-order structures and interact with other molecules. The combined molecular diversity leads to the heterogeneity of proteins that manifests as distinct phenotypes. Structural biology has generated vast amounts of data, effectively enabling accurate structural prediction by computational methods. However, structures are often obtained heterologously under homogeneous states in vitro. The lack of native heterogeneity under cellular context creates challenges in precisely connecting the structural data to phenotypes. Mass spectrometry (MS) based proteomics methods can profile proteome composition of complex biological samples. Most MS methods follow the "bottom-up" approach, which denatures and digests proteins into short peptide fragments for ease of detection. Coupled with chemical biology approaches, higher-order structures can be probed via incorporation of covalent labels on native proteins that are maintained at the peptide level. Alternatively, native MS follows the "top-down" approach and directly analyzes intact proteins under nondenaturing conditions. Various tandem MS activation methods can dissect the intact proteins for in-depth structural elucidation. Herein, we review recent native MS applications for characterizing heterogeneous samples, including proteins binding to mixtures of ligands, homo/hetero-complexes with varying stoichiometry, intrinsically disordered proteins with dynamic conformations, glycoprotein complexes with mixed modification states, and active membrane protein complexes in near-native membrane environments. We summarize the benefits, challenges, and ongoing developments in native MS, with the hope to demonstrate an emerging technology that complements other tools by filling the knowledge gaps in understanding the molecular heterogeneity of proteins.

Glycosaminoglycans: What Remains To Be Deciphered?

Glycosaminoglycans (GAGs) are complex polysaccharides exhibiting a vast structural diversity and fulfilling various functions mediated by thousands of interactions in the extracellular matrix, at the cell surface, and within the cells where they have been detected in the nucleus. It is known that the chemical groups attached to GAGs and GAG conformations comprise "glycocodes" that are not yet fully deciphered. The molecular context also matters for GAG structures and functions, and the influence of the structure and functions of the proteoglycan core proteins on sulfated GAGs and vice versa warrants further investigation. The lack of dedicated bioinformatic tools for mining GAG data sets contributes to a partial characterization of the structural and functional landscape and interactions of GAGs. These pending issues will benefit from the development of new approaches reviewed here, namely (i) the synthesis of GAG oligosaccharides to build large and diverse GAG libraries, (ii) GAG analysis and sequencing by mass spectrometry (e.g., ion mobility-mass spectrometry), gas-phase infrared spectroscopy, recognition tunnelling nanopores, and molecular modeling to identify bioactive GAG sequences, biophysical methods to investigate binding interfaces, and to expand our knowledge and understanding of glycocodes governing GAG molecular recognition, and (iii) artificial intelligence for in-depth investigation of GAGomic data sets and their integration with proteomics.

Glycosidase mechanisms: Sugar conformations and reactivity in endo- and exo-acting enzymes

The enzymatic breakdown of carbohydrates plays a critical role in several biological events and enables the development of sustainable processes to obtain bioproducts and biofuels. In this scenario, the design of efficient inhibitors for glycosidases that can act as drug targets and the engineering of carbohydrate-active enzymes with tailored catalytic properties is of remarkable importance. To guide rational approaches, it is necessary to elucidate enzyme molecular mechanisms, in particular understanding how the microenvironment modulates the conformational space explored by the substrate. Computer simulations, especially those based on ab initio methods, have provided a suitable atomic description of carbohydrate conformations and catalytic reactions in several glycosidase families. In this review, we will focus on how the active-site topology (pocket or cleft) and mode of cleavage (endo or exo) can affect the catalytic mechanisms adopted by glycosidases, in particular the substrate conformations along the reaction coordinate.

Glycoproteomic landscape and structural dynamics of TIM family immune checkpoints enabled by mucinase SmE

Mucin-domain glycoproteins are densely O-glycosylated and play critical roles in a host of biological functions. In particular, the T cell immunoglobulin and mucin-domain containing family of proteins (TIM-1, -3, -4) decorate immune cells and act as key checkpoint inhibitors in cancer. However, their dense O-glycosylation remains enigmatic both in terms of glycoproteomic landscape and structural dynamics, primarily due to the challenges associated with studying mucin domains. Here, we present a mucinase (SmE) and demonstrate its ability to selectively cleave along the mucin glycoprotein backbone, similar to others of its kind. Unlike other mucinases, though, SmE harbors the unique ability to cleave at residues bearing extremely complex glycans which enabled improved mass spectrometric analysis of several mucins, including the entire TIM family. With this information in-hand, we performed molecular dynamics (MD) simulations of TIM-3 and -4 to demonstrate how glycosylation affects structural features of these proteins. Overall, we present a powerful workflow to better understand the detailed molecular structures of the mucinome.

Glycan Conformation in the Heavily Glycosylated Protein, CEACAM1

Glycans attached to glycoproteins can contribute to stability, mediate interactions with other proteins, and initiate signal transduction. Glycan conformation, which is critical to these processes, is highly variable and often depicted as sampling a multitude of conformers. These conformers can be generated by molecular dynamics simulations, and more inclusively by accelerated molecular dynamics, as well as other extended sampling methods. However, experimental assessments of the contribution that various conformers make to a native ensemble are rare. Here, we use long-range pseudo-contact shifts (PCSs) of NMR resonances from an isotopically labeled glycoprotein to identify preferred conformations of its glycans. The N-terminal domain from human Carcinoembryonic Antigen Cell Adhesion Molecule 1, hCEACAM1-Ig1, was used as the model glycoprotein in this study. It has been engineered to include a lanthanide-ion-binding loop that generates PCSs, as well as a homogeneous set of three ¹³C-labeled N-glycans. Analysis of the PCSs indicates that preferred glycan conformers have extensive contacts with the protein surface. Factors leading to this preference appear to include interactions between N-acetyl methyls of GlcNAc residues and hydrophobic surface pockets on the protein surface.

Synthetic Approaches to Break the Chemical Shift Degeneracy of Glycans

NMR spectroscopy is the leading technique for determining glycans' three-dimensional structure and dynamic in solution as well as a fundamental tool to study protein-glycan interactions. To overcome the severe chemical shift degeneracy of these compounds, synthetic probes carrying NMR-active nuclei (e. g., 13 C or 19 F) or lanthanide tags have been proposed. These elegant strategies permitted to simplify the complex NMR analysis of unlabeled analogues, shining light on glycans' conformational aspects and interaction with proteins. Here, we highlight some key achievements in the synthesis of specifically labeled glycan probes and their contribution towards the fundamental understanding of glycans.

Exploration, Representation, and Rationalization of the Conformational Phase Space of N-Glycans

Despite their fundamental biological relevance, structure-property relationships in N-glycans are fundamentally lacking, and their highly multidimensional compositional and conformational phase spaces remain largely unexplored. The torsional flexibility of the glycosidic linkages and the ring dynamics result in wide, rugged free-energy landscapes that are difficult to sample in molecular dynamics simulations. We show that a novel enhanced-sampling scheme combining replica exchange with solute and collective-variable tempering, enabling transitions over all relevant energy barriers, delivers converged distributions of solvated N-glycan conformers. Several dimensionality-reduction algorithms are compared and employed to generate conformational free-energy maps in two dimensions. Together with an originally developed conformation-based nomenclature scheme that uniquely identifies glycan conformers, our modeling procedure is applied to reveal the effect of chemical substitutions on the conformational ensemble of selected high-mannose-type and complex glycans. Moreover, the structure-prediction capabilities of two commonly used glycan force fields are assessed via the theoretical prediction of experimentally available nuclear magnetic resonance J-coupling constants. The results especially confirm the key role of ω and ψ torsion angles in discriminating between different conformational states and suggest an intriguing correlation between the torsional and ring-puckering degrees of freedom that may be biologically relevant.

Extension of the CHARMM Classical Drude Polarizable Force Field to N- and O-Linked Glycopeptides and Glycoproteins

Molecular dynamic simulations are an effective tool to study complex molecular systems and are contingent upon the availability of an accurate and reliable molecular mechanics force field. The Drude polarizable force field, which allows for the explicit treatment of electronic polarization in a computationally efficient fashion, has been shown to reproduce experimental properties that were difficult or impossible to reproduce with the CHARMM additive force field, including peptide folding cooperativity, RNA hairpin structures, and DNA base flipping. Glycoproteins are essential components of glycoconjugate vaccines, antibodies, and many pharmaceutically important molecules, and an accurate polarizable force field that includes compatibility between the protein and carbohydrate aspect of the force field is essential to study these types of systems. In this work, we present an extension of the Drude polarizable force field to glycoproteins, including both N- and O-linked species. Parameter optimization focused on the dihedral terms using a reweighting protocol targeting NMR solution J-coupling data for model glycopeptides. Validation of the model include eight model glycopeptides and four glycoproteins with multiple N- and O-linked glycosylations. The new glycoprotein carbohydrate force field can be used in conjunction with the remainder of Drude polarizable force field through a variety of MD simulation programs including GROMACS, OPENMM, NAMD, and CHARMM and may be accessed through the Drude Prepper module in the CHARMM-GUI.