Assessing Genetic Algorithm-Based Docking Protocols for Prediction of Heparin Oligosaccharide Binding Geometries onto Proteins

Although molecular docking has evolved dramatically over the years, its application to glycosaminoglycans (GAGs) has remained challenging because of their intrinsic flexibility, highly anionic character and rather ill-defined site of binding on proteins. GAGs have been treated as either fully "rigid" or fully "flexible" in molecular docking. We reasoned that an intermediate semi-rigid docking (SRD) protocol may be better for the recapitulation of native heparin/heparan sulfate (Hp/HS) topologies. Herein, we study 18 Hp/HS-protein co-complexes containing chains from disaccharide to decasaccharide using genetic algorithm-based docking with rigid, semi-rigid, and flexible docking protocols. Our work reveals that rigid and semi-rigid protocols recapitulate native poses for longer chains (5→10 mers) significantly better than the flexible protocol, while 2→4-mer poses are better predicted using the semi-rigid approach. More importantly, the semi-rigid docking protocol is likely to perform better when no crystal structure information is available. We also present a new parameter for parsing selective versus non-selective GAG-protein systems, which relies on two computational parameters including consistency of binding (i.e., RMSD) and docking score (i.e., GOLD Score). The new semi-rigid protocol in combination with the new computational parameter is expected to be particularly useful in high-throughput screening of GAG sequences for identifying promising druggable targets as well as drug-like Hp/HS sequences.

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.

Effect of Sulfation on the Conformational Dynamics of Dermatan Sulfate Glycosaminoglycan: A Gaussian Accelerated Molecular Dynamics Study

Glycosaminoglycans (GAGs) are anionic biopolymers present on cell surfaces as a part of proteoglycans. The biological activities of GAGs depend on the sulfation pattern. In our study, we have considered three octadecasaccharide dermatan sulfate (DS) chains with increasing order of sulfation (dp6s, dp7s, and dp12s) to illuminate the role of sulfation on the GAG units and its chain conformation through 10 μs-long Gaussian accelerated molecular dynamics simulations. DS is composed of repeating disaccharide units of iduronic acid (IdoA) and N-acetylgalactosamine (N-GalNAc). Here, N-GalNAc is linked to IdoA via β(1-4), while IdoA is linked to N-GalNAc through α(1-3). With the increase in sulfation, the DS structure becomes more rigid and linear, as is evident from the distribution of root-mean-square deviations (RMSDs) and end-to-end distances. The tetrasaccharide linker region of the main chain shows a rigid conformation in terms of the glycosidic linkage. We have observed that upon sulfation (i.e., dp12s), the ring flip between two chair forms vanished for IdoA. The dynamic cross-correlation analysis reveals that the anticorrelation motions in dp12s are reduced significantly compared to dp6s or dp7s. An increase in sulfation generates relatively more stable hydrogen-bond networks, including water bridging with the neighboring monosaccharides. Despite the favorable linear structures of the GAG chains, our study also predicts few significant bendings related to the different puckering states, which may play a notable role in the function of the DS. The relation between the global conformation with the micro-level parameters such as puckering and water-mediated hydrogen bonds shapes the overall conformational space of GAGs. Overall, atomistic details of the DS chain provided in this study will help understand their functional and mechanical roles, besides developing new biomaterials.

Mechanistic Picture for Monomeric Human Fibroblast Growth Factor 1 Stabilization by Heparin Binding

Human fibroblast growth factor (FGF) 1 or hFGF1 is a member of the FGF family that is involved in various vital processes such as cell proliferation, cell differentiation, angiogenesis, and wound healing. hFGF1, which is associated with low stability in vivo, is known to be stabilized by binding heparin sulfate, a glycosaminoglycan that aids the protein in the activation of its cell surface receptor. The poor thermal and proteolytic stability of hFGF1 and the stabilizing role of heparin have long been observed experimentally; however, the mechanistic details of these phenomena are not well understood. Here, we have used microsecond-level equilibrium molecular dynamics (MD) simulations to quantitatively characterize the structural dynamics of monomeric hFGF1 in the presence and absence of heparin hexasaccharide. We have observed a conformational change in the heparin-binding pocket of hFGF1 that occurs only in the absence of heparin. Several intramolecular interactions were also identified within the heparin-binding pocket that form only when hFGF1 interacts with heparin. The loss of both intermolecular and intramolecular interactions in the absence of heparin plausibly leads to the observed conformational change. This conformational transition results in increased flexibility of the heparin-binding pocket and provides an explanation for the susceptibility of apo hFGF1 to proteolytic degradation and thermal instability. This study provides a glimpse into mechanistic details of the heparin-mediated stabilization of hFGF1 and encourages the use of microsecond-level MD in studying the effect of binding on protein structure and dynamics. In addition, the observed differential behavior of hFGF1 in the absence and presence of heparin provides an example, where microsecond-level all-atom MD simulations are necessary to see functionally relevant biomolecular phenomena that otherwise will not be observed on sub-microsecond time scales.

Comprehensive analysis of carbohydrate-protein recognition in the Protein Data Bank

Carbohydrate-protein interactions underpin wide-ranging aspects of biology. However, such interactions remain relatively unexplored in pharmaceutical and biotechnological applications, in part due to the challenges associated with their structural characterisation, both experimentally and computationally. Knowledge-based approaches have shown great success in the prediction of drug-protein and protein-protein interactions, although have not been comprehensively investigated for carbohydrate-protein interactions. In this work, carbohydrate-protein complexes from the Protein Data Bank were comprehensively obtained and analysed to identify patterns in how carbohydrate-protein interactions are mediated.

Sulfated Non-Saccharide Glycosaminoglycan Mimetics as Novel Drug Discovery Platform for Various Pathologies

Glycosaminoglycans (GAGs) are very complex, natural anionic polysaccharides. They are polymers of repeating disaccharide units of uronic acid and hexosamine residues. Owing to their template-free, spatiotemporally-controlled, and enzyme-mediated biosyntheses, GAGs possess enormous polydispersity, heterogeneity, and structural diversity which often translate into multiple biological roles. It is well documented that GAGs contribute to physiological and pathological processes by binding to proteins including serine proteases, serpins, chemokines, growth factors, and microbial proteins. Despite advances in the GAG field, the GAG-protein interface remains largely unexploited by drug discovery programs. Thus, Non-Saccharide Glycosaminoglycan Mimetics (NSGMs) have been rationally developed as a novel class of sulfated molecules that modulate GAG-protein interface to promote various biological outcomes of substantial benefit to human health. In this review, we describe the chemical, biochemical, and pharmacological aspects of recently reported NSGMs and highlight their therapeutic potentials as structurally and mechanistically novel anti-coagulants, anti-cancer agents, anti-emphysema agents, and anti-viral agents. We also describe the challenges that complicate their advancement and describe ongoing efforts to overcome these challenges with the aim of advancing the novel platform of NSGMs to clinical use.

Wnt Binding Affinity Prediction for Putative Frizzled-Type Cysteine-Rich Domains

Several proteins other than the frizzled receptors (Fzd) and the secreted Frizzled-related proteins (sFRP) contain Fzd-type cysteine-rich domains (CRD). We have termed these domains “putative Fzd-type CRDs”, as the relevance of Wnt signalling in the majority of these is unknown; the RORs, an exception to this, are well known for mediating non-canonical Wnt signalling. In this study, we have predicted the likely binding affinity of all Wnts for all putative Fzd-type CRDs. We applied both our previously determined Wnt‒Fzd CRD binding affinity prediction model, as well as a newly devised model wherein the lipid term was forced to contribute favourably to the predicted binding energy. The results obtained from our new model indicate that certain putative Fzd CRDs are much more likely to bind Wnts, in some cases exhibiting selectivity for specific Wnts. The results of this study inform the investigation of Wnt signalling modulation beyond Fzds and sFRPs.

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.

Design, synthesis, and biomedical applications of synthetic sulphated polysaccharides

This review summarizes the synthetic methods to sulphated polysaccharides, describes their compositional and structural diversity in regards to activity, and showcases their biomedical applications.

So you think computational approaches to understanding glycosaminoglycan-protein interactions are too dry and too rigid? Think again!

Glycosaminoglycans (GAGs) play key roles in virtually all biologic responses through their interaction with proteins. A major challenge in understanding these roles is their massive structural complexity. Computational approaches are extremely useful in navigating this bottleneck and, in some cases, the only avenue to gain comprehensive insight. We discuss the state-of-the-art on computational approaches and present a flowchart to help answer most basic, and some advanced, questions on GAG-protein interactions. For example, firstly, does my protein bind to GAGs?; secondly, where does the GAG bind?; thirdly, does my protein preferentially recognize a particular GAG type?; fourthly, what is the most optimal GAG chain length?; fifthly, what is the structure of the most favored GAG sequence?; and finally, is my GAG-protein system 'specific', 'non-specific', or a combination of both? Recent advances show the field is now poised to enable a non-computational researcher perform advanced experiments through the availability of various tools and online servers.

Computational drill down on FGF1-heparin interactions through methodological evaluation

Glycosaminoglycans (GAGs) exhibit a key role in cellular communication processes through interactions with target proteins of the extracellular matrix (ECM). The sandwich-like interaction established between Fibroblast growth factor (FGF) and heparin (HE) represents quite a peculiar protein-GAG-protein system, which has been both structurally and functionally intensively studied. The molecular recognition characteristics of this system have been exploited in various computational studies in order to deepen understanding of GAG-protein interactions. Here, we drill down on the interactions established in this peculiar macromolecular complex by analyzing the applicability of docking techniques and molecular dynamics (MD)-based approaches, and we dissect the molecular recognition properties exhibited by FGF towards a series of HE derivatives. We examine the sensitivity of MM-GBSA free energy calculations in terms of receptor conformational space sampling and changes in the ligand structures. Furthermore, we investigate its predictive power in combination with other computational methods, namely the well-established Autodock3 (AD3) and dynamic molecular docking (DMD), a targeted MD-based docking method specifically developed to account for flexibility and solvent in computer simulations of protein-GAG systems. Our results show that a site-mapping approach can be effectively combined with AD3 and DMD calculations to accurately reproduce available experimental data and, furthermore, to determine specific GAG recognition patterns. This study deepens our understanding of the applicability of available theoretical approaches to the investigation of molecular recognition in protein-GAG systems.

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.

CSAR 2014: A Benchmark Exercise Using Unpublished Data from Pharma

The 2014 CSAR Benchmark Exercise was the last community-wide exercise that was conducted by the group at the University of Michigan, Ann Arbor. For this event, GlaxoSmithKline (GSK) donated unpublished crystal structures and affinity data from in-house projects. Three targets were used: tRNA (m1G37) methyltransferase (TrmD), Spleen Tyrosine Kinase (SYK), and Factor Xa (FXa). A particularly strong feature of the GSK data is its large size, which lends greater statistical significance to comparisons between different methods. In Phase 1 of the CSAR 2014 Exercise, participants were given several protein-ligand complexes and asked to identify the one near-native pose from among 200 decoys provided by CSAR. Though decoys were requested by the community, we found that they complicated our analysis. We could not discern whether poor predictions were failures of the chosen method or an incompatibility between the participant's method and the setup protocol we used. This problem is inherent to decoys, and we strongly advise against their use. In Phase 2, participants had to dock and rank/score a set of small molecules given only the SMILES strings of the ligands and a protein structure with a different ligand bound. Overall, docking was a success for most participants, much better in Phase 2 than in Phase 1. However, scoring was a greater challenge. No particular approach to docking and scoring had an edge, and successful methods included empirical, knowledge-based, machine-learning, shape-fitting, and even those with solvation and entropy terms. Several groups were successful in ranking TrmD and/or SYK, but ranking FXa ligands was intractable for all participants. Methods that were able to dock well across all submitted systems include MDock,1 Glide-XP,2 PLANTS,3 Wilma,4 Gold,5 SMINA,6 Glide-XP2/PELE,7 FlexX,8 and MedusaDock.9 In fact, the submission based on Glide-XP2/PELE7 cross-docked all ligands to many crystal structures, and it was particularly impressive to see success across an ensemble of protein structures for multiple targets. For scoring/ranking, submissions that showed statistically significant achievement include MDock1 using ITScore1,10 with a flexible-ligand term,11 SMINA6 using Autodock-Vina,12,13 FlexX8 using HYDE,14 and Glide-XP2 using XP DockScore2 with and without ROCS15 shape similarity.16 Of course, these results are for only three protein targets, and many more systems need to be investigated to truly identify which approaches are more successful than others. Furthermore, our exercise is not a competition.

Antibody-Carbohydrate Recognition from Docked Ensembles Using the AutoMap Procedure

Carbohydrate-protein recognition is vital to many processes in health and disease. In particular, elucidation of the structural basis of carbohydrate binding is important to the development of oligosaccharides and oligosaccharide mimetics as vaccines for infectious diseases and cancer. Computational structural techniques are valuable for the study of carbohydrate-protein recognition due to the challenges associated with experimental determination of carbohydrate-protein complexes. AutoMap is a computer program that we have developed to study protein-ligand recognition. AutoMap determines the interactions taking place in a set of highly ranked poses obtained from molecular docking and processes these to identify the protein residues most likely to be involved in interactions. In this protocol, we describe the use of AutoMap and illustrate its suitability for studying antibody recognition of the Lewis Y tetrasaccharide, which is a potential cancer vaccine antigen.

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.

The carbohydrate-binding promiscuity of Euonymus europaeus lectin is predicted to involve a single binding site

Euonymus europaeus lectin (EEL) is a carbohydrate-binding protein derived from the fruit of the European spindle tree. EEL was first identified for its erythrocyte agglutinating properties and specificity for B and H blood groups. However, a detailed molecular picture of the structural basis of carbohydrate recognition by EEL remains to be developed. In this study, we performed fluorescence titrations of a range of carbohydrates against EEL. Binding of EEL to a wide range of carbohydrates was observed, including a series of blood group-related carbohydrates, mannosides, chitotriose and sialic acid. Affinity was strongest for carbohydrates with H-related structures and the B trisaccharide. A homology model of EEL was produced from templates identified using the HHPred server, which employs hidden Markov models (HMMs) to identify templates. The HMM approach identified that the best templates for EEL were proteins featuring a ricin B-like (R-type) fold. Separate templates were used to model the core and binding site regions of the lectin. Through the use of constrained docking and spatial comparison with a template ligand, binding modes for the carbohydrate ligands were predicted. A relationship between the experimental binding energies and the computed binding energies of the selected docked poses was determined and optimized. Collectively, our results suggest that EEL utilizes a single site for recognition of carbohydrates terminating in a variety of monosaccharides.

Toward a robust computational screening strategy for identifying glycosaminoglycan sequences that display high specificity for target proteins

Glycosaminoglycans (GAGs) interact with many proteins to regulate processes such as hemostasis, cell adhesion, growth and differentiation and viral infection. Yet, majority of these interactions remain poorly understood at a molecular level. A major reason for this state is the phenomenal structural diversity of GAGs, which has precluded analysis of specificity of their interactions. We had earlier presented a computational protocol for predicting "high-specificity" GAG sequences based on combinatorial virtual library screening (CVLS) technology. In this work, we expand the robustness of this technology through rigorous studies of parameters affecting GAG recognition of proteins, especially antithrombin and thrombin. The CVLS approach involves automated construction of a virtual library of all possible oligosaccharide sequences (di- to octasaccharide) followed by a two-step selection strategy consisting of "affinity" (GOLD score) and "specificity" (consistency of binding) filters. We find that "specificity" features are optimally evaluated using 100 genetic algorithm experiments, 100,000 evolutions and variable docking radius from 10 Å (disaccharide) to 14 Å (hexasaccharide). The results highlight critical interactions in H/HS oligosaccharides that govern specificity. Application of CVLS technology to the antithrombin-heparin system indicates that the minimal "specificity" element is the GlcAp(1 → 4)GlcNp2S3S disaccharide of heparin. The CVLS technology affords a simple, intuitive framework for the design of longer GAG sequences that can exhibit high "specificity" without resorting to exhaustive screening of millions of theoretical sequences.