Docking glycosaminoglycans to proteins: analysis of solvent inclusion.
ABSTRACT: Glycosaminoglycans (GAGs) are anionic polysaccharides, which participate in key processes in the extracellular matrix by interactions with protein targets. Due to their charged nature, accurate consideration of electrostatic and water-mediated interactions is indispensable for understanding GAGs binding properties. However, solvent is often overlooked in molecular recognition studies. Here we analyze the abundance of solvent in GAG-protein interfaces and investigate the challenges of adding explicit solvent in GAG-protein docking experiments. We observe PDB GAG-protein interfaces being significantly more hydrated than protein-protein interfaces. Furthermore, by applying molecular dynamics approaches we estimate that about half of GAG-protein interactions are water-mediated. With a dataset of eleven GAG-protein complexes we analyze how solvent inclusion affects Autodock 3, eHiTs, MOE and FlexX docking. We develop an approach to de novo place explicit solvent into the binding site prior to docking, which uses the GRID program to predict positions of waters and to locate possible areas of solvent displacement upon ligand binding. To investigate how solvent placement affects docking performance, we compare these results with those obtained by taking into account information about the solvent position in the crystal structure. In general, we observe that inclusion of solvent improves the results obtained with these methods. Our data show that Autodock 3 performs best, though it experiences difficulties to quantitatively reproduce experimental data on specificity of heparin/heparan sulfate disaccharides binding to IL-8. Our work highlights the current challenges of introducing solvent in protein-GAGs recognition studies, which is crucial for exploiting the full potential of these molecules for rational engineering.
Project description:The role of the aldose reductase in type 2 diabetes is widely described. Therefore, it is of interest to identify plant derived compounds to inhibit its activity. We studied the protein-ligand interaction of 267 compounds from different parts of seven plants (Allium sativum, Coriandrum sativum, Dacus carota, Murrayyakoneigii, Eucalyptus, Calendula officinalis and Lycopersicon esculentum) with aldose reductase as the target protein. Molecular docking and re-scoring of top ten compounds (using GOLD, AutoDock Vina, eHiTS, PatchDock and MEDock) followed by rank-sum technique identified compound allium38 with high binding affinity for aldose reductase.
Project description:Glycosaminoglycans (GAGs) are linear, structurally diverse, conformationally complex carbohydrate polymers that may contain up to 200 monosaccharides. These characteristics present a challenge for studying GAG conformational thermodynamics at atomic resolution using existing experimental methods. Molecular dynamics (MD) simulations can overcome this challenge but are only feasible for short GAG polymers. To address this problem, we developed an algorithm that applies all conformational parameters contributing to GAG backbone flexibility (i.e., bond lengths, bond angles, and dihedral angles) from unbiased all-atom explicit-solvent MD simulations of short GAG polymers to rapidly construct models of GAGs of arbitrary length. The algorithm was used to generate non-sulfated chondroitin 10- and 20-mer ensembles which were compared to MD-generated ensembles for internal validation. End-to-end distance distributions in constructed and MD-generated ensembles have minimal differences, suggesting that our algorithm produces conformational ensembles that mimic the backbone flexibility seen in simulation. Non-sulfated chondroitin 100- and 200-mer ensembles were constructed within a day, demonstrating the efficiency of the algorithm and reduction in time and computational cost compared to simulation.
Project description: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.
Project description:The prevalence of salt bridges across protein binding interfaces is surprising given the significant costs of desolvating the two charged groups upon binding. These desolvation costs, which are difficult to examine using laboratory experiments, have been computed in previous studies using the Poisson-Boltzmann (PB) implicit solvent model. Here, for the first time, we directly compare the PB implicit solvent model with several explicit water models in computing the desolvation penalties of salt bridges across protein-protein interfaces. We report both overall agreement as well as significant differences between the implicit and explicit solvent results. These differences highlight challenges to be faced in the application of implicit solvent methods.
Project description:The biological function of interleukin-10 (IL-10), a pleiotropic cytokine with an essential role in inflammatory processes, is known to be affected by glycosaminoglycans (GAGs). GAGs are highly negatively charged polysaccharides and integral components of the extracellular matrix with important functions in the biology of many growth factors and cytokines. The molecular mechanism of the IL-10/GAG interaction is unclear. In particular, experimental evidence about IL-10/GAG binding sites is lacking, despite its importance for understanding the biological role of the interaction. Here, we report the experimental determination of a GAG binding site of IL-10. Although no co-crystal structure of the IL-10·GAG complex could be obtained, its structural characterization was possible by NMR spectroscopy. Chemical shift perturbations of IL-10 induced by GAG binding were used to narrow down the location of the binding site and to assess the affinity for different GAG molecules. Subsequent observation of NMR pseudocontact shifts of IL-10 and its heparin ligand, as induced by a protein-attached lanthanide spin label, provided structural restraints for the protein·ligand complex. Using these restraints, pseudocontact shift-based rigid body docking together with molecular dynamics simulations yielded a GAG binding model. The heparin binding site is located at the C-terminal end of helix D and the adjacent DE loop and coincides with a patch of positively charged residues involving arginines 102, 104, 106, and 107 and lysines 117 and 119. This study represents the first experimental characterization of the IL-10·GAG complex structure and provides the starting point for revealing the biological significance of the interaction of IL-10 with GAGs.
Project description:The interactions between glycosaminoglycans (GAGs), important components of the extracellular matrix, and proteins such as growth factors and chemokines play critical roles in cellular regulation processes. Therefore, the design of GAG derivatives for the development of innovative materials with bio-like properties in terms of their interaction with regulatory proteins is of great interest for tissue engineering and regenerative medicine. Previous work on the chemokine interleukin-8 (IL-8) has focused on its interaction with heparin and heparan sulfate, which regulate chemokine function. However, the extracellular matrix contains other GAGs, such as hyaluronic acid (HA), dermatan sulfate (DS) and chondroitin sulfate (CS), which have so far not been characterized in terms of their distinct molecular recognition properties towards IL-8 in relation to their length and sulfation patterns. NMR and molecular modeling have been in great part the methods of choice to study the structural and recognition properties of GAGs and their protein complexes. However, separately these methods have challenges to cope with the high degree of similarity and flexibility that GAGs exhibit. In this work, we combine fluorescence spectroscopy, NMR experiments, docking and molecular dynamics simulations to study the configurational and recognition properties of IL-8 towards a series of HA and CS derivatives and DS. We analyze the effects of GAG length and sulfation patterns in binding strength and specificity, and the influence of GAG binding on IL-8 dimer formation. Our results highlight the importance of combining experimental and theoretical approaches to obtain a better understanding of the molecular recognition properties of GAG-protein systems.
Project description:Molecular recognition plays a fundamental role in all biological processes, and that is why great efforts have been made to understand and predict protein-ligand interactions. Finding a molecule that can potentially bind to a target protein is particularly essential in drug discovery and still remains an expensive and time-consuming task. In silico, tools are frequently used to screen molecular libraries to identify new lead compounds, and if protein structure is known, various protein-ligand docking programs can be used. The aim of docking procedure is to predict correct poses of ligand in the binding site of the protein as well as to score them according to the strength of interaction in a reasonable time frame. The purpose of our studies was to present the novel consensus approach to predict both protein-ligand complex structure and its corresponding binding affinity. Our method used as the input the results from seven docking programs (Surflex, LigandFit, Glide, GOLD, FlexX, eHiTS, and AutoDock) that are widely used for docking of ligands. We evaluated it on the extensive benchmark dataset of 1300 protein-ligands pairs from refined PDBbind database for which the structural and affinity data was available. We compared independently its ability of proper scoring and posing to the previously proposed methods. In most cases, our method is able to dock properly approximately 20% of pairs more than docking methods on average, and over 10% of pairs more than the best single program. The RMSD value of the predicted complex conformation versus its native one is reduced by a factor of 0.5 Å. Finally, we were able to increase the Pearson correlation of the predicted binding affinity in comparison with the experimental value up to 0.5.
Project description:We report a computational model for the assembly of HIV-1 Gag into immature viral particles at the plasma membrane. To reproduce experimental structural and kinetic properties of assembly, a process occurring on the order of minutes, a coarse-grained representation consisting of a single particle per Gag molecule is developed. The model uses information relating the functional interfaces implicated in Gag assembly, results from cryo electron-tomography, and biophysical measurements from fluorescence microscopy, such as the dynamics of Gag assembly at single virions. These experimental constraints eliminated many classes of potential interactions, and narrowed the model to a single interaction scheme with two non-equivalent interfaces acting to form Gags into a hexamer, and a third interface acting to link hexamers together. This model was able to form into a hexameric structure with correct lattice spacing and reproduced biologically relevant growth rates. We explored the effect of genomic RNA seeding punctum growth, finding that RNA may be a factor in locally concentrating Gags to initiate assembly. The simulation results infer that completion of assembly cannot be governed simply by Gag binding kinetics. However the addition of membrane curvature suggests that budding of the virion from the plasma membrane could factor into slowing incorporation of Gag at an assembly site resulting in virions of the same size and number of Gag molecules independent of Gag concentration or the time taken to complete assembly. To corroborate the results of our simulation model, we developed an analytic model for Gag assembly finding good agreement with the simulation results.
Project description:Chemokines interact with glycosaminoglycans (GAGs) at the cellular surface and to specific cell-surface receptors to activate signaling pathways. The GAG interaction allows the formation of a chemotactic gradient of chemokine required for cell haptotaxis and chemokine oligomerization. Poxviruses encode secreted chemokine-binding proteins with no sequence similarity to their cellular counterparts to modulate the host immune system. The E163 protein from ectromelia virus, the causative agent of mousepox, binds chemokines through their GAG-binding domain. In addition, E163 interacts with GAGs to be anchored at the cell surface, but its ability to interfere with chemokine-GAG interactions has not been demonstrated. We report the identification of the GAG-binding regions in E163 and the generation of mutant forms deficient of GAG binding. Chemokine binding assays show that some of the E163 GAG-binding sites are also involved in the interaction with chemokines. By using recombinant GAG-binding mutant forms we demonstrate that E163 prevents the interaction of chemokines with cell-surface GAGs, providing mechanisms for the immunomodulatory activity of the viral chemokine-binding protein E163.
Project description:We describe a combinatorial virtual screening approach for predicting high specificity heparin/heparan sulfate sequences using the well-studied antithrombin-heparin interaction as a test case. Heparan sulfate hexasaccharides were simulated in the 'average backbone' conformation, wherein the inter-glycosidic bond angles were held constant at the mean of the known solution values, irrespective of their sequence. Molecular docking utilized GOLD with restrained inter-glycosidic torsions and intra-ring conformations, but flexible substituents at the 2-, 3-, and 6-positions and explicit incorporation of conformational variability of the iduronate residues. The approach reproduces the binding geometry of the sequence-specific heparin pentasaccharide to within 2.5 A. Screening of a combinatorial virtual library of 6,859 heparin hexasaccharides using a dual filter strategy, in which predicted antithrombin affinity was the first filter and self-consistency of docking was the second, resulted in only 10 sequences. Of these, nine were found to bind antithrombin in a manner identical to the natural pentasaccharide, while a novel hexasaccharide bound the inhibitor in a unique but dramatically different geometry and orientation. This work presents the first approach on combinatorial library screening for heparin/heparan sulfate GAGs to determine high specificity sequences and opens up huge opportunities to investigate numerous other physiologically relevant GAG-protein interactions.