Coarse-Grained Model of Glycosaminoglycans

Dynamics of an aqueous suspension of short hyaluronic acid chains near a DPPC bilayer

The synergy between hyaluronic acid (HA) and lipid molecules plays a crucial role in synovial fluids, cell coatings, etc. Diseased cells in cancer and arthritis show changes in HA concentration and chain size, impacting the viscoelastic and mechanical properties of the cells. Although the solution behavior of HA is known in experiments, a molecular-level understanding of the role of HA in the dynamics at the interface of HA-water and the cellular boundary is lacking. Here, we perform atomistic molecular dynamics simulation of short HA chains in an explicit water solvent in the presence of a DPPC bilayer, relevant in pathological cases. We identify a stable interface between HA-water and the bilayer where the water molecules are in contact with the bilayer and the HA chains are located away without any direct contact. Both translation and rotation of the interfacial waters in contact with the lipid bilayer and translation of the HA chains exhibit subdiffusive behavior. The diffusive behavior sets in slightly away from the bilayer, where the diffusion coefficients of water and HA decrease monotonically with increase in HA concentration. On the contrary, the dependence on HA chain size is only marginal due to enhanced chain flexibility as their size increases.

Coarse-Grained Model of Glycosaminoglycans for Biomolecular Simulations

Proteoglycans contain glycosaminoglycans (GAGs) which are negatively charged linear polymers made of repeating disaccharide units of uronic acid and hexosamine units. They play vital roles in numerous physiological and pathological processes, particularly in governing cellular communication and attachment. Depending on their sulfonation state, acetylation, and glycosidic linkages, GAGs belong to different families. The high molecular weight, heterogeneity, and flexibility of GAGs hamper their characterization at atomic resolution, but this may be circumvented via coarse-grained (CG) approaches. In this work, we report a CG model for a library of common GAG types in their isolated or proteoglycan-linked states compatible with version 2.2 (v2.2) of the widely popular CG Martini force field. The model reproduces conformational and thermodynamic properties for a wide variety of GAGs, as well as matching structural and binding data for selected proteoglycan test systems. The parameters developed here may thus be employed to study a range of GAG-containing biomolecular systems, thereby benefiting from the efficiency and broad applicability of the Martini framework.

Ligand binding of interleukin-8: a comparison of glycosaminoglycans and acidic peptides

Recognition and binding of regulatory proteins to glycosaminoglycans (GAGs) from the extracellular matrix is a process of high biological importance. The interaction between negatively charged sulfate or carboxyl groups of the GAGs and clusters of basic amino acids on the protein is crucial in this binding process and it is believed that electrostatics represent the key factor for this interaction. However, given the rather undirected nature of electrostatics, it is important to achieve a clear understanding of its role in protein-GAG interactions and how specificity and selectivity in these systems can be achieved, when the classical key-lock binding motif is not applicable. Here, we compare protein binding of a highly charged heparin (HP) hexasaccharide with four de novo designed decapeptides of varying negative net charge. The charge density of these peptides was comparable to typical GAGs of the extracellular matrix. We used the regulatory protein interleukin-8 (IL-8) because its interactions with GAGs are well described. All four peptide ligands bind to the same epitope of IL-8 but show much weaker binding affinity as revealed in 1H-15N HSQC NMR titration experiments. Complementary molecular docking and molecular dynamics simulations revealed further atomistic details of the interaction mode of GAG versus peptide ligands. Overall, similar contributions to the binding energy and hydrogen bond formation are determined for HP and the highly charged peptides, suggesting that the entropic loss of the peptides upon binding likely account for the remarkably different affinity of GAG versus peptide ligands to IL-8.

Extension of the SUGRES-1P Coarse-Grained Model of Polysaccharides to Heparin

Heparin is an unbranched periodic polysaccharide composed of negatively charged monomers and involved in key biological processes, including anticoagulation, angiogenesis, and inflammation. Its structure and dynamics have been studied extensively using experimental as well as theoretical approaches. The conventional approach of computational chemistry applied to the analysis of biomolecules is all-atom molecular dynamics, which captures the interactions of individual atoms by solving Newton's equation of motion. An alternative is molecular dynamics simulations using coarse-grained models of biomacromolecules, which offer a reduction of the representation and consequently enable us to extend the time and size scale of simulations by orders of magnitude. In this work, we extend the UNIfied COarse-gRaiNed (UNICORN) model of biological macromolecules developed in our laboratory to heparin. We carried out extensive tests to estimate the optimal weights of energy terms of the effective energy function as well as the optimal Debye-Hückel screening factor for electrostatic interactions. We applied the model to study unbound heparin molecules of polymerization degree ranging from 6 to 68 residues. We compare the obtained coarse-grained heparin conformations with models obtained from X-ray diffraction studies of heparin. The SUGRES-1P force field was able to accurately predict the general shape and global characteristics of heparin molecules.

A One-Bead-Per-Saccharide (1BPS) Model for Glycosaminoglycans

Glycosaminoglycans (GAGs) are polysaccharide compounds that play key roles in various biological processes. GAGs are important structural components of cartilage and the extracellular matrix of the brain. Due to the large size of these polysaccharides, coarse-grained approaches are indispensable for modeling these biopolymers. We develop a one-bead-per-saccharide model of chondroitin sulfates and hyaluronic acid based on an existing three-bead-per-saccharide coarse-grained model. Our coarse graining is carried out by using iterative Boltzmann inversion (IBI), including an additional coupling potential to incorporate the correlation between dihedral angles. The predictions of the model are verified against those of the existing three-bead-per-saccharin model and the experimental radius of gyration for hyaluronic acid.

Solvent Model Benchmark for Molecular Dynamics of Glycosaminoglycans

In computational studies of glycosaminoglycans (GAGs), a group of anionic, periodic linear polysaccharides, so far there has been very little discussion about the role of solvent models in the molecular dynamics simulations of these molecules. Predominantly, the TIP3P water model is commonly used as one of the most popular explicit water models in general. However, there are numerous alternative explicit and implicit water models that are neglected in the computational research of GAGs. Since solvent-mediated interactions are particularly important for GAG dynamic and structural properties, it would be of great interest for the GAG community to establish the solvent model that is suited the best in terms of the quality of theoretically obtained GAG parameters and, at the same time, would be reasonably demanding in terms of computational resources required. In this study, heparin (HP) was simulated using five implicit and six explicit solvent models with the aim to find out how different solvent models influence HP's molecular descriptors in the molecular dynamics simulations. Here, we initiate the search for the most appropriate solvent representation for GAG systems and we hope to encourage other groups to contribute to this highly relevant subject.

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.

Atomic-Resolution Experimental Structural Biology and Molecular Dynamics Simulations of Hyaluronan and Its Complexes

This review summarizes the atomic-resolution structural biology of hyaluronan and its complexes available in the Protein Data Bank, as well as published studies of atomic-resolution explicit-solvent molecular dynamics simulations on these and other hyaluronan and hyaluronan-containing systems. Advances in accurate molecular mechanics force fields, simulation methods and software, and computer hardware have supported a recent flourish in such simulations, such that the simulation publications now outnumber the structural biology publications by an order of magnitude. In addition to supplementing the experimental structural biology with computed dynamic and thermodynamic information, the molecular dynamics studies provide a wealth of atomic-resolution information on hyaluronan-containing systems for which there is no atomic-resolution structural biology either available or possible. Examples of these summarized in this review include hyaluronan pairing with other hyaluronan molecules and glycosaminoglycans, with ions, with proteins and peptides, with lipids, and with drugs and drug-like molecules. Despite limitations imposed by present-day computing resources on system size and simulation timescale, atomic-resolution explicit-solvent molecular dynamics simulations have been able to contribute significant insight into hyaluronan's flexibility and capacity for intra- and intermolecular non-covalent interactions.

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.

How molecular modelling can better broaden the understanding of glycosylations

Glycosylations are among the most ubiquitous post-translational modifications (PTMs) in proteins, and the effects of their perturbations are seen in various diseases such as cancers, diabetes and arthritis to name a few. Yet they remain one of the most enigmatic aspects of protein structure and function. On the other hand, molecular modelling techniques have been rapidly bridging this knowledge gap since the last decade. In this review, we discuss how these techniques have proven to be indispensable for a better understanding of the role of glycosylations in glycoprotein structure and function.

Molecular Dynamics Approaches Dissect Molecular Mechanisms Underlying Methylene Blue-Glycosaminoglycan Interactions

Glycosaminoglycans (GAGs) are a class of periodic anionic linear polysaccharides involved in a number of biologically relevant processes in the extracellular matrix via interactions with various types of molecules including proteins, peptides and small organic molecules. The metachromatic dye methylene blue (MB) is a GAG binding agent. This molecule possesses a tricyclic, monocationic phenothiazine ring system, while the terminal methyl groups attached to the nitrogen atoms bear the most positive charges of the cation and, therefore, represent potential binding sites for negatively charged GAGs. In this study, we rigorously explored molecular mechanisms underlying these interactions for several GAG types: heparin, heparan and chondroitin sulfates. We found that GAG-MB interactions are predominantly electrostatically driven, with the particularly important role of sulfate groups. MB oligomeric stack formation was favored in the presence of GAGs. Furthermore, the impact of MB binding on the conformation of GAGs was also evaluated. The novel results allow for better quantitative analytics of GAG composition in the studied biochemical systems using MB dye as a GAG-specific marker. Our data add to the knowledge on small molecule-GAG interactions and could be potentially useful for novel developments in drug design and putative disease therapies in which GAGs are involved.

Elucidating the Binding Mode between Heparin and Inflammatory Cytokines by Molecular Modeling

Heparan sulfate (HS) interacts with a broad spectrum of inflammatory cytokines, thereby modulating their biological activities. It is believed that there is a structural-functional correlation between each protein and sugar sequences in the HS polysaccharides, however, the information in this regard is limited. In this study, we compared the binding of four inflammatory cytokines (CCL8, IL-1beta, IL-2 and IL-6) to immobilized heparin by an SPR analysis. To define the molecular base of the binding, we used a heparin pentasaccharide as representative structure to dock into the 3D-molecular structure of the cytokines. The results show a discrepancy in KD values obtained by SPR analysis and theoretical calculation, pointing to the importance to apply more than one method when describing affinity between proteins and HS. By cluster analysis of the complex formed between the pentasaccharide and cytokines, we have identified several groups in heparin forming strong hydrogen bonds with all four cytokines, which is a significant finding. This molecular and conformational information should be valuable for rational design of HS/heparin-mimetics to interfere cytokine-HS interactions.

Modeling Protein-Glycosaminoglycan Complexes: Does the Size Matter?

Docking glycosaminoglycans (GAGs) has been challenging because of the complex nature of these long periodic linear and negatively charged polysaccharides. Although standard docking tools like Autodock3 are successful when docking GAGs up to hexameric length, they experience challenges to properly dock longer GAGs. Similar limitations concern other docking approaches typically developed for docking ligands of limited size to proteins. At the same time, most of more advanced docking approaches are challenging for a user who is inexperienced with complex in silico methodologies. In this work, we evaluate the binding energies of complexes with different lengths of GAGs using all-atom molecular dynamics simulations. Based on this analysis, we propose a new docking protocol for long GAGs that consists of conventional docking of short GAGs and further elongation with the use of a coarse-grained representation of the GAG parts not being in direct contact with its protein receptor. This method automated by a simple script is straightforward to use within the Autodock3 framework but also useful in combination with other standard docking tools. We believe that this method with some minor case-specific modifications could also be used for docking other linear charged polymers.

Theory and Practice of Coarse-Grained Molecular Dynamics of Biologically Important Systems

Molecular dynamics with coarse-grained models is nowadays extensively used to simulate biomolecular systems at large time and size scales, compared to those accessible to all-atom molecular dynamics. In this review article, we describe the physical basis of coarse-grained molecular dynamics, the coarse-grained force fields, the equations of motion and the respective numerical integration algorithms, and selected practical applications of coarse-grained molecular dynamics. We demonstrate that the motion of coarse-grained sites is governed by the potential of mean force and the friction and stochastic forces, resulting from integrating out the secondary degrees of freedom. Consequently, Langevin dynamics is a natural means of describing the motion of a system at the coarse-grained level and the potential of mean force is the physical basis of the coarse-grained force fields. Moreover, the choice of coarse-grained variables and the fact that coarse-grained sites often do not have spherical symmetry implies a non-diagonal inertia tensor. We describe selected coarse-grained models used in molecular dynamics simulations, including the most popular MARTINI model developed by Marrink's group and the UNICORN model of biological macromolecules developed in our laboratory. We conclude by discussing examples of the application of coarse-grained molecular dynamics to study biologically important processes.

Computational insights into heparin-small molecule interactions: Evaluation of the balance between stacking and non-stacking binding modes

Glycosaminoglycans (GAGs), anionic periodic linear polysaccharides, are involved in a manifold of key biochemical processes ongoing in the extracellular matrix via establishing direct intermolecular interactions with diverse classes of biopolymers as well as with bioactive small molecules. Due to their acidic nature, they are capable of binding positively charged ligands, which, in turn could affect their binding with protein and peptide targets, modulating a number of physiologically important signaling pathways. Therefore, it is of great significance to improve our understanding on the molecular basis underlying GAG-small molecule interactions. In this study, we applied in silico approaches (molecular dynamics and free energy calculations) complemented with circular dichroism and absorption spectroscopy to characterize the complex formation between heparin, one of the principal members of GAG family, and twenty different cationic ligands including therapeutic drugs, alkaloids and organic dyes. In particular, the oligomerization propensity of ligands prior to heparin binding, binding free energy parameters, effects of the ionic strength are rigorously described. Based on the performed analysis, the ligands are classified into three main groups depending on their heparin binding and oligomerization properties. The computational data agree and provide rationale for the corresponding experimental findings, contributing to the general knowledge of the physico-chemical nature of ligand-GAG intermolecular interactions.

A Bittersweet Computational Journey among Glycosaminoglycans

Glycosaminoglycans (GAGs) are linear polysaccharides. In proteoglycans (PGs), they are attached to a core protein. GAGs and PGs can be found as free molecules, associated with the extracellular matrix or expressed on the cell membrane. They play a role in the regulation of a wide array of physiological and pathological processes by binding to different proteins, thus modulating their structure and function, and their concentration and availability in the microenvironment. Unfortunately, the enormous structural diversity of GAGs/PGs has hampered the development of dedicated analytical technologies and experimental models. Similarly, computational approaches (in particular, molecular modeling, docking and dynamics simulations) have not been fully exploited in glycobiology, despite their potential to demystify the complexity of GAGs/PGs at a structural and functional level. Here, we review the state-of-the art of computational approaches to studying GAGs/PGs with the aim of pointing out the “bitter” and “sweet” aspects of this field of research. Furthermore, we attempt to bridge the gap between bioinformatics and glycobiology, which have so far been kept apart by conceptual and technical differences. For this purpose, we provide computational scientists and glycobiologists with the fundamentals of these two fields of research, with the aim of creating opportunities for their combined exploitation, and thereby contributing to a substantial improvement in scientific knowledge.

Investigation of the structure of regulatory proteins interacting with glycosaminoglycans by combining NMR spectroscopy and molecular modeling - the beginning of a wonderful friendship

The interaction of regulatory proteins with extracellular matrix or cell surface-anchored glycosaminoglycans (GAGs) plays important roles in molecular recognition, wound healing, growth, inflammation and many other processes. In spite of their high biological relevance, protein-GAG complexes are significantly underrepresented in structural databases because standard tools for structure determination experience difficulties in studying these complexes. Co-crystallization with subsequent X-ray analysis is hampered by the high flexibility of GAGs. NMR spectroscopy experiences difficulties related to the periodic nature of the GAGs and the sparse proton network between protein and GAG with distances that typically exceed the detection limit of nuclear Overhauser enhancement spectroscopy. In contrast, computer modeling tools have advanced over the last years delivering specific protein-GAG docking approaches successfully complemented with molecular dynamics (MD)-based analysis. Especially the combination of NMR spectroscopy in solution providing sparse structural constraints with molecular docking and MD simulations represents a useful synergy of forces to describe the structure of protein-GAG complexes. Here we review recent methodological progress in this field and bring up examples where the combination of new NMR methods along with cutting-edge modeling has yielded detailed structural information on complexes of highly relevant cytokines with GAGs.

Evaluation of replica exchange with repulsive scaling approach for docking glycosaminoglycans

Glycosaminoglycans (GAGs), long linear periodic anionic polysaccharides, are key molecules in the extracellular matrix (ECM). Therefore, deciphering their role in the biologically relevant context is important for fundamental understanding of the processes ongoing in ECM and for establishing new strategies in the regenerative medicine. Although GAGs represent a number of computational challenges, molecular docking is a powerful tool for analysis of their interactions. Despite the recent development of GAG-specific docking approaches, there is plenty of room for improvement. Here, replica exchange molecular dynamics with repulsive scaling (REMD-RS) recently proved to be a successful approach for protein-protein complexes, was applied to dock GAGs. In this method, effective pairwise radii are increased in different Hamiltonian replicas. REMD-RS is shown to be an attractive alternative to classical docking approaches for GAGs. This work contributes to setting up of GAG-specific computational protocols and provides new insights into the nature of these biological systems.

GAG-DB, the New Interface of the Three-Dimensional Landscape of Glycosaminoglycans

Glycosaminoglycans (GAGs) are complex linear polysaccharides. GAG-DB is a curated database that classifies the three-dimensional features of the six mammalian GAGs (chondroitin sulfate, dermatan sulfate, heparin, heparan sulfate, hyaluronan, and keratan sulfate) and their oligosaccharides complexed with proteins. The entries are structures of GAG and GAG-protein complexes determined by X-ray single-crystal diffraction methods, X-ray fiber diffractometry, solution NMR spectroscopy, and scattering data often associated with molecular modeling. We designed the database architecture and the navigation tools to query the database with the Protein Data Bank (PDB), UniProtKB, and GlyTouCan (universal glycan repository) identifiers. Special attention was devoted to the description of the bound glycan ligands using simple graphical representation and numerical format for cross-referencing to other databases in glycoscience and functional data. GAG-DB provides detailed information on GAGs, their bound protein ligands, and features their interactions using several open access applications. Binding covers interactions between monosaccharides and protein monosaccharide units and the evaluation of quaternary structure. GAG-DB is freely available.

Glycosaminoglycan-Protein Interactions: The First Draft of the Glycosaminoglycan Interactome

The six mammalian glycosaminoglycans (GAGs), chondroitin sulfate, dermatan sulfate, heparin, heparan sulfate, hyaluronan, and keratan sulfate, are linear polysaccharides. Except for hyaluronan, they are sulfated to various extent, and covalently attached to proteins to form proteoglycans. GAGs interact with growth factors, morphogens, chemokines, extracellular matrix proteins and their bioactive fragments, receptors, lipoproteins, and pathogens. These interactions mediate their functions, from embryonic development to extracellular matrix assembly and regulation of cell signaling in various physiological and pathological contexts such as angiogenesis, cancer, neurodegenerative diseases, and infections. We give an overview of GAG-protein interactions (i.e., specificity and chemical features of GAG- and protein-binding sequences), and review the available GAG-protein interaction networks. We also provide the first comprehensive draft of the GAG interactome composed of 832 biomolecules (827 proteins and five GAGs) and 932 protein-GAG interactions. This network is a scaffold, which in the future should integrate structures of GAG-protein complexes, quantitative data of the abundance of GAGs in tissues to build tissue-specific interactomes, and GAG interactions with metal ions such as calcium, which plays a major role in the assembly of the extracellular matrix and its interactions with cells. This contextualized interactome will be useful to identify druggable GAG-protein interactions for therapeutic purpose.

Extending the Martini Coarse-Grained Force Field to N-Glycans

Glycans play a vital role in a large number of cellular processes. Their complex and flexible nature hampers structure-function studies using experimental techniques. Molecular dynamics (MD) simulations can help in understanding dynamic aspects of glycans if the force field parameters used can reproduce key experimentally observed properties. Here, we present optimized coarse-grained (CG) Martini force field parameters for N-glycans, calibrated against experimentally derived binding affinities for lectins. The CG bonded parameters were obtained from atomistic (ATM) simulations for different glycan topologies including high mannose and complex glycans with various branching patterns. In the CG model, additional elastic networks are shown to improve maintenance of the overall conformational distribution. Solvation free energies and octanol-water partition coefficients were also calculated for various N-glycan disaccharide combinations. When using standard Martini nonbonded parameters, we observed that glycans spontaneously aggregated in the solution and required down-scaling of their interactions for reproduction of ATM model radial distribution functions. We also optimized the nonbonded interactions for glycans interacting with seven lectin candidates and show that a relatively modest scaling down of the glycan-protein interactions can reproduce free energies obtained from experimental studies. These parameters should be of use in studying the role of glycans in various glycoproteins and carbohydrate binding proteins as well as their complexes, while benefiting from the efficiency of CG sampling.

Analysis of Procollagen C-Proteinase Enhancer-1/Glycosaminoglycan Binding Sites and of the Potential Role of Calcium Ions in the Interaction

In this study, we characterize the interactions between the extracellular matrix protein, procollagen C-proteinase enhancer-1 (PCPE-1), and glycosaminoglycans (GAGs), which are linear anionic periodic polysaccharides. We applied molecular modeling approaches to build a structural model of full-length PCPE-1, which is not experimentally available, to predict GAG binding poses for various GAG lengths, types and sulfation patterns, and to determine the effect of calcium ions on the binding. The computational data are analyzed and discussed in the context of the experimental results previously obtained using surface plasmon resonance binding assays. We also provide experimental data on PCPE-1/GAG interactions obtained using inhibition assays with GAG oligosaccharides ranging from disaccharides to octadecasaccharides. Our results predict the localization of GAG-binding sites at the amino acid residue level onto PCPE-1 and is the first attempt to describe the effects of ions on protein-GAG binding using modeling approaches. In addition, this study allows us to get deeper insights into the in silico methodology challenges and limitations when applied to GAG-protein interactions.

NMR and molecular modeling reveal specificity of the interactions between CXCL14 and glycosaminoglycans

CXCL14, chemokine (C-X-C motif) ligand 14, is a novel highly conserved chemokine with unique features. Despite exhibiting the typical chemokine fold, it has a very short N-terminus of just two amino acid residues responsible for chemokine receptor activation. CXCL14 actively participates in homeostatic immune surveillance of skin and mucosae, is linked to metabolic disorders and fibrotic lung diseases and possesses strong anti-angiogenic properties in early tumor development. In this work, we investigated the interaction of CXCL14 with various glycosaminoglycans (GAGs) by nuclear magnetic resonance spectroscopy, microscale thermophoresis, analytical heparin (HE) affinity chromatography and in silico approaches to understand the molecular basis of GAG-binding. We observed different GAG-binding modes specific for the GAG type used in the study. In particular, the CXCL14 epitope for HE suggests a binding pose distinguishable from the ones of the other GAGs investigated (hyaluronic acid, chondroitin sulfate-A/C, −D, dermatan sulfate). This observation is also supported by computational methods that included molecular docking, molecular dynamics and free energy calculations. Based on our results, we suggest that distinct GAG sulfation patterns confer specificity beyond simple electrostatic interactions usually considered to represent the driving forces in protein–GAG interactions. The CXCL14–GAG system represents a promising approach to investigate the specificity of GAG–protein interactions, which represents an important topic for developing the rational approaches to novel strategies in regenerative medicine.

Docking software performance in protein-glycosaminoglycan systems

We present a benchmarking study for protein-glycosaminoglycan systems with eight docking programs: Dock, rDock, ClusPro, PLANTS, HADDOCK, Hex, SwissDock and ATTRACT. We used a non-redundant representative dataset of 28 protein-glycosaminoglycan complexes with experimentally available structures, where a glycosaminoglycan ligand was longer than a trimer. Overall, the ligand binding poses could be correctly predicted in many cases by the tested docking programs, however the ranks of the docking poses are often poorly assigned. Our results suggest that Dock program performs best in terms of the pose placement, has the most suitable scoring function, and its performance did not depend on the ligand size. This suggests that the implementation of the electrostatics as well as the shape complementarity procedure in Dock are the most suitable for docking glycosaminoglycan ligands. We also analyzed how free energy patterns of the benchmarking complexes affect the performance of the evaluated docking software.

Perspective on computational simulations of glycosaminoglycans

Glycosaminoglycans (GAGs) represent a formidable frontier for chemists, biochemists, biologists, medicinal chemists and drug delivery specialists because of massive structural complexity. GAGs are arguably the most complex, natural linear biopolymers with theoretical diversity orders of magnitude higher than proteins and nucleic acids. Yet, this diversity remains generally untapped. Computational approaches offer major routes to understand GAG structure and dynamics so as to enable novel applications of these biopolymers. In fact, computational algorithms, softwares, online tools and techniques have reached a level of sophistication that help understand atomistic details of conformational variation and protein recognition of individual GAG sequences. This review describes current approaches and challenges in computational study of GAGs. It presents a history of major findings since the earliest mention of GAGs (the 1960s), the development of parameters and force fields specific for GAGs, and the application of these tools in understanding GAG structure-function relationship. This review also presents a section on how to perform simulation of GAGs, which is directed toward researchers interested in entering this promising field with potential to impact therapy.

Modeling large protein-glycosaminoglycan complexes using a fragment-based approach

Glycosaminoglycans (GAGs), a major constituent of the extracellular matrix, participate in cell-signaling by binding specific proteins. Structural data on protein-GAG interactions are crucial to understand and modulate these signaling processes, with potential applications in regenerative medicine. However, experimental and theoretical approaches used to study GAG-protein systems are challenged by GAGs high flexibility limiting the conformational sampling above a certain size, and by the scarcity of GAG-specific docking tools compared to protein-protein or protein-drug docking approaches. We present for the first time an automated fragment-based method for docking GAGs on a protein binding site. In this approach, trimeric GAG fragments are flexibly docked to the protein, assembled based on their spacial overlap, and refined by molecular dynamics. The method appeared more successful than the classical full-ligand approach for most of 13 tested complexes with known structure. The approach is particularly promising for docking of long GAG chains, which represents a bottleneck for classical docking approaches applied to these systems. © 2019 Wiley Periodicals, Inc.

Flory-type theories of polymer chains under different external stimuli

In this Review, we present a critical analysis of various applications of the Flory-type theories to a theoretical description of the conformational behavior of single polymer chains in dilute polymer solutions under a few external stimuli. Different theoretical models of flexible polymer chains in the supercritical fluid are discussed and analysed. Different points of view on the conformational behavior of the polymer chain near the liquid-gas transition critical point of the solvent are presented. A theoretical description of the co-solvent-induced coil-globule transitions within the implicit-solvent-explicit-co-solvent models is discussed. Several explicit-solvent-explicit-co-solvent theoretical models of the coil-to-globule-to-coil transition of the polymer chain in a mixture of good solvents (co-nonsolvency) are analysed and compared with each other. Finally, a new theoretical model of the conformational behavior of the dielectric polymer chain under the external constant electric field in the dilute polymer solution with an explicit account for the many-body dipole correlations is discussed. The polymer chain collapse induced by many-body dipole correlations of monomers in the context of statistical thermodynamics of dielectric polymers is analysed.

Multiscale modeling of glycosaminoglycan structure and dynamics: current methods and challenges

Glycosaminoglycans are long unbranched and complex polysaccharides that are an essential component of mammalian extracellular matrices. Characterization of their molecular structure, dynamics and interactions are essential to understand important biological phenomena in health and disease, and will lead to novel therapeutics and medical devices. However, this has proven to be a challenge experimentally and theoretical techniques are needed to develop new hypotheses, and interpret experiments. This review aims to examine the current theoretical (rather than experimental) methods used by researchers to investigate glycosaminoglycan structure, dynamics and interactions, from the monosaccharide to the macromolecular scale. It will consider techniques such as quantum mechanics, molecular mechanics, molecular dynamics, coarse graining and docking.

Glycosaminoglycanomics: where we are

Glycosaminoglycans regulate numerous physiopathological processes such as development, angiogenesis, innate immunity, cancer and neurodegenerative diseases. Cell surface GAGs are involved in cell-cell and cell-matrix interactions, cell adhesion and signaling, and host-pathogen interactions. GAGs contribute to the assembly of the extracellular matrix and heparan sulfate chains are able to sequester growth factors in the ECM. Their biological activities are regulated by their interactions with proteins. The structural heterogeneity of GAGs, mostly due to chemical modifications occurring during and after their synthesis, makes the development of analytical techniques for their profiling in cells, tissues, and biological fluids, and of computational tools for mining GAG-protein interaction data very challenging. We give here an overview of the experimental approaches used in glycosaminoglycomics, of the major GAG-protein interactomes characterized so far, and of the computational tools and databases available to analyze and store GAG structures and interactions.

Computational analysis of interactions in structurally available protein-glycosaminoglycan complexes

Glycosaminoglycans represent a class of linear anionic periodic polysaccharides, which play a key role in a variety of biological processes in the extracellular matrix via interactions with their protein targets. Computationally, glycosaminoglycans are very challenging due to their high flexibility, periodicity and electrostatics-driven nature of the interactions with their protein counterparts. In this work, we carry out a detailed computational characterization of the interactions in protein-glycosaminoglycan complexes from the Protein Data Bank (PDB), which are split into two subsets accounting for their intrinsic nature: non-enzymatic-protein-glycosaminoglycan and enzyme-glycosaminoglycan complexes. We apply molecular dynamics to analyze the differences in these two subsets in terms of flexibility, retainment of the native interactions in the simulations, free energy components of binding and contributions of protein residue types to glycosaminoglycan binding. Furthermore, we systematically demonstrate that protein electrostatic potential calculations, previously found to be successful for glycosaminoglycan binding sites prediction for individual systems, are in general very useful for proposing protein surface regions as putative glycosaminoglycan binding sites, which can be further used for local docking calculations with these particular polysaccharides. Finally, the performance of six different docking programs (Autodock 3, Autodock Vina, MOE, eHiTS, FlexX and Glide), some of which proved to perform well for particular protein-glycosaminoglycan complexes in previous work, is evaluated on the complete protein-glycosaminoglycan data set from the PDB. This work contributes to widen our knowledge of protein-glycosaminoglycan molecular recognition and could be useful to steer a choice of the strategies to be applied in theoretical studies of these systems.

A Simple Method for Discovering Druggable, Specific Glycosaminoglycan-Protein Systems. Elucidation of Key Principles from Heparin/Heparan Sulfate-Binding Proteins

Glycosaminoglycans (GAGs) affect human physiology and pathology by modulating more than 500 proteins. GAG-protein interactions are generally assumed to be ionic and nonspecific, but specific interactions do exist. Here, we present a simple method to identify the GAG-binding site (GBS) on proteins that in turn helps predict high specific GAG-protein systems. Contrary to contemporary thinking, we found that the electrostatic potential at basic arginine and lysine residues neither identifies the GBS consistently, nor its specificity. GBSs are better identified by considering the potential at neutral hydrogen bond donors such as asparagine or glutamine sidechains. Our studies also reveal that an unusual constellation of ionic and non-ionic residues in the binding site leads to specificity. Nature engineers the local environment of Asn45 of antithrombin, Gln255 of 3-O-sulfotransferase 3, Gln163 and Asn167 of 3-O-sulfotransferase 1 and Asn27 of basic fibroblast growth factor in the respective GBSs to induce specificity. Such residues are distinct from other uncharged residues on the same protein structure in possessing a significantly higher electrostatic potential, resultant from the local topology. In contrast, uncharged residues on nonspecific GBSs such as thrombin and serum albumin possess a diffuse spread of electrostatic potential. Our findings also contradict the paradigm that GAG-binding sites are simply a collection of contiguous Arg/Lys residues. Our work demonstrates the basis for discovering specifically interacting and druggable GAG-protein systems based on the structure of protein alone, without requiring access to any structure-function relationship data.