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

Alteration of Neuropilin‑1 and Heparan Sulfate Interaction Impairs Murine B16 Tumor Growth

Neuropilin-1 acts as a coreceptor with vascular endothelial growth factor receptors to facilitate binding of its ligand, vascular endothelial growth factor. Neuropilin-1 also binds to heparan sulfate, but the functional significance of this interaction has not been established. A combinatorial library screening using heparin oligosaccharides followed by molecular dynamics simulations of a heparin tetradecasaccharide suggested a highly conserved binding site composed of amino acid residues extending across the b1 and b2 domains of murine neuropilin-1. Mutagenesis studies established the importance of arginine513 and lysine514 for binding of heparin to a recombinant form of Nrp1 composed of the a1, a2, b1, and b2 domains. Recombinant Nrp1 protein bearing R513A,K514A mutations showed a significant loss of heparin-binding, heparin-induced dimerization, and heparin-dependent thermal stabilization. Isothermal calorimetry experiments suggested a 1:2 complex of heparin tetradecasaccharide:Nrp1. To study the impact of altered heparin binding in vivo, a mutant allele of Nrp1 bearing the R513A,K514A mutations was created in mice (Nrp1D) and crossbred to Nrp1+/- mice to examine the impact of altered heparan sulfate binding. Analysis of tumor formation showed variable effects on tumor growth in Nrp1D/D mice, resulting in a frank reduction in tumor growth in Nrp1D/- mice. Expression of mutant Nrp1D protein was normal in tissues, suggesting that the reduction in tumor growth was due to the altered binding of heparin/heparan sulfate to neuropilin-1. These findings suggest that the interaction of neuropilin-1 with heparan sulfate modulates its stability and its role in tumor formation and growth

Combinatorial Virtual Library Screening Study of Transforming Growth Factor-β2–Chondroitin Sulfate System

Transforming growth factor-beta (TGF-β), a member of the TGF-β cytokine superfamily, is known to bind to sulfated glycosaminoglycans (GAGs), but the nature of this interaction remains unclear. In a recent study, we found that preterm human milk TGF-β2 is sequestered by chondroitin sulfate (CS) in its proteoglycan form. To understand the molecular basis of the TGF-β2–CS interaction, we utilized the computational combinatorial virtual library screening (CVLS) approach in tandem with molecular dynamics (MD) simulations. All possible CS oligosaccharides were generated in a combinatorial manner to give 24 di- (CS02), 192 tetra- (CS04), and 1536 hexa- (CS06) saccharides. This library of 1752 CS oligosaccharides was first screened against TGF-β2 using the dual filter CVLS algorithm in which the GOLDScore and root-mean-square-difference (RMSD) between the best bound poses were used as surrogate markers for in silico affinity and in silico specificity. CVLS predicted that both the chain length and level of sulfation are critical for the high affinity and high specificity recognition of TGF-β2. Interestingly, CVLS led to identification of two distinct sites of GAG binding on TGF-β2. CVLS also deduced the preferred composition of the high specificity hexasaccharides, which were further assessed in all-atom explicit solvent MD simulations. The MD results confirmed that both sites of binding form stable GAG–protein complexes. More specifically, the highly selective CS chains were found to engage the TGF-β2 monomer with high affinity. Overall, this work present key principles of recognition with regard to the TGF-β2–CS system. In the process, it led to the generation of the in silico library of all possible CS oligosaccharides, which can be used for advanced studies on other protein–CS systems. Finally, the study led to the identification of unique CS sequences that are predicted to selectively recognize TGF-β2 and may out-compete common natural CS biopolymers

In-Depth Molecular Dynamics Study of All Possible Chondroitin Sulfate Disaccharides Reveals Key Insight into Structural Heterogeneity and Dynamism

GAGs exhibit a high level of conformational and configurational diversity, which remains untapped in terms of the recognition and modulation of proteins. Although GAGs are suggested to bind to more than 800 biologically important proteins, very few therapeutics have been designed or discovered so far. A key challenge is the inability to identify, understand and predict distinct topologies accessed by GAGs, which may help design novel protein-binding GAG sequences. Recent studies on chondroitin sulfate (CS), a key member of the GAG family, pinpointing its role in multiple biological functions led us to study the conformational dynamism of CS building blocks using molecular dynamics (MD). In the present study, we used the all-atom GLYCAM06 force field for the first time to explore the conformational space of all possible CS building blocks. Each of the 16 disaccharides was solvated in a TIP3P water box with an appropriate number of counter ions followed by equilibration and a production run. We analyzed the MD trajectories for torsional space, inter- and intra-molecular H-bonding, bridging water, conformational spread and energy landscapes. An in-house phi and psi probability density analysis showed that 1→3-linked sequences were more flexible than 1→4-linked sequences. More specifically, phi and psi regions for 1→4-linked sequences were held within a narrower range because of intra-molecular H-bonding between the GalNAc O5 atom and GlcA O3 atom, irrespective of sulfation pattern. In contrast, no such intra-molecular interaction arose for 1→3-linked sequences. Further, the stability of 1→4-linked sequences also arose from inter-molecular interactions involving bridged water molecules. The energy landscape for both classes of CS disaccharides demonstrated increased ruggedness as the level of sulfation increased. The results show that CS building blocks present distinct conformational dynamism that offers the high possibility of unique electrostatic surfaces for protein recognition. The fundamental results presented here will support the development of algorithms that help to design longer CS chains for protein recognition

A molecular dynamics-based algorithm for evaluating the glycosaminoglycan mimicking potential of synthetic, homogenous, sulfated small molecules

<div><p>Glycosaminoglycans (GAGs) are key natural biopolymers that exhibit a range of biological functions including growth and differentiation. Despite this multiplicity of function, natural GAG sequences have not yielded drugs because of problems of heterogeneity and synthesis. Recently, several homogenous non-saccharide glycosaminoglycan mimetics (NSGMs) have been reported as agents displaying major therapeutic promise. Yet, it remains unclear whether sulfated NSGMs structurally mimic sulfated GAGs. To address this, we developed a three-step molecular dynamics (MD)-based algorithm to compare sulfated NSGMs with GAGs. In the first step of this algorithm, parameters related to the range of conformations sampled by the two highly sulfated molecules as free entities in water were compared. The second step compared identity of binding site geometries and the final step evaluated comparable dynamics and interactions in the protein-bound state. Using a test case of interactions with fibroblast growth factor-related proteins, we show that this three-step algorithm effectively predicts the GAG structure mimicking property of NSGMs. Specifically, we show that two unique dimeric NSGMs mimic hexameric GAG sequences in the protein-bound state. In contrast, closely related monomeric and trimeric NSGMs do not mimic GAG in either the free or bound states. These results correspond well with the functional properties of NSGMs. The results show for the first time that appropriately designed sulfated NSGMs can be good structural mimetics of GAGs and the incorporation of a MD-based strategy at the NSGM library screening stage can identify promising mimetics of targeted GAG sequences.</p></div

Comparison of NSGMs and HS06 in the protein bound state using overall average inter-molecular hydrogen bond occupancy (A) and total binding free energy (B).

<p>A) shows the stability of bound NSGMs and HS06 based on average residue-level, inter-molecular hydrogen bond occupancy; B) shows the stability of the bound NSGMs and HS06 based on total binding free energy ΔG (in simulated kcal/mol, error bars shows standard deviation). See text for details.</p

The consistency of intermolecular hydrogen bonds across the MD simulation (final 10 ns) for FGF2–FGFR1 residues.

<p>The occurrence of inter-molecular hydrogen bonds between FGF2–FGFR1 residues (shown on y-axis) and small molecule ligands (NSGMs and HS06) are shown for each frame of the final 10 ns of the MD run. A) FGF2–FGFR1–G2.1 complex; B) FGF2–FGFR1–G2.2 complex; C) FGF2–FGFR1–HSO6(²S_O) complex; D) FGF2–FGFR1–HS06(¹C₄) complex.</p

The consistency of intermolecular hydrogen bonds across the MD simulation (final 10 ns) for FGF2 residues.

<p>The occurrence of inter-molecular hydrogen bonds between FGF2 residues (shown on y-axis) and small molecule ligands (NSGMs and HS06) are shown for each frame of the final 10 ns of the MD run. A) FGF2–G2.1 complex; B) FGF2–G2.2 complex; C) FGF2–HSO6(²S_O) complex; D) FGF2–HS06(¹C₄) complex.</p

MD-based structural equivalence of NSGMs to HS06 in free solution using minimum volume enclosing ellipsoid (MVEE) as one of the comparable parameters.

<p>A) shows MVEE of each MD frame for all four NSGMs; B) shows MVEE for HS06 sequence containing IdoA2S in ²S_O and ¹C₄ conformations; and C) compiles the results across the MD simulation in a box plot.</p