Sulfation and cation effects on the conformational properties of the glycan backbone of chondroitin sulfate disaccharides.

Chondroitin sulfate (CS) is one of several glycosaminoglycans that are major components of proteoglycans. A linear polymer consisting of repeats of the disaccharide -4GlcAß1-3GalNAcß1-, CS undergoes differential sulfation resulting in five unique sulfation patterns. Because of the dimer repeat, the CS glycosidic "backbone" has two distinct sets of conformational degrees of freedom defined by pairs of dihedral angles: (ϕ1, ψ1) about the ß1-3 glycosidic linkage and (ϕ2, ψ2) about the ß1-4 glycosidic linkage. Differential sulfation and the possibility of cation binding, combined with the conformational flexibility and biological diversity of CS, complicate experimental efforts to understand CS three-dimensional structures at atomic resolution. Therefore, all-atom explicit-solvent molecular dynamics simulations with Adaptive Biasing Force sampling of the CS backbone were applied to obtain high-resolution, high-precision free energies of CS disaccharides as a function of all possible backbone geometries. All 10 disaccharides (ß1-3 vs ß1-4 linkage × five different sulfation patterns) were studied; additionally, ion effects were investigated by considering each disaccharide in the presence of either neutralizing sodium or calcium cations. GlcAß1-3GalNAc disaccharides have a single, broad, thermodynamically important free-energy minimum, whereas GalNAcß1-4GlcA disaccharides have two such minima. Calcium cations but not sodium cations bind to the disaccharides, and binding is primarily to the GlcA -COO(-) moiety as opposed to sulfate groups. This binding alters the glycan backbone thermodynamics in instances where a calcium cation bound to -COO(-) can act to bridge and stabilize an interaction with an adjacent sulfate group, whereas, in the absence of this cation, the proximity of a sulfate group to -COO(-) results in two like charges being both desolvated and placed adjacent to each other and is found to be destabilizing. In addition to providing information on sulfation and cation effects, the present results can be applied to building models of CS polymers and as a point of comparison in studies of CS polymer backbone dynamics and thermodynamics.

Site Identification by Ligand Competitive Saturation (SILCS) simulations for fragment-based drug design.

Fragment-based drug design (FBDD) involves screening low molecular weight molecules ("fragments") that correspond to functional groups found in larger drug-like molecules to determine their binding to target proteins or nucleic acids. Based on the principle of thermodynamic additivity, two fragments that bind nonoverlapping nearby sites on the target can be combined to yield a new molecule whose binding free energy is the sum of those of the fragments. Experimental FBDD approaches, like NMR and X-ray crystallography, have proven very useful but can be expensive in terms of time, materials, and labor. Accordingly, a variety of computational FBDD approaches have been developed that provide different levels of detail and accuracy.The Site Identification by Ligand Competitive Saturation (SILCS) method of computational FBDD uses all-atom explicit-solvent molecular dynamics (MD) simulations to identify fragment binding. The target is "soaked" in an aqueous solution with multiple fragments having different identities. The resulting computational competition assay reveals what small molecule types are most likely to bind which regions of the target. From SILCS simulations, 3D probability maps of fragment binding called "FragMaps" can be produced. Based on the probabilities relative to bulk, SILCS FragMaps can be used to determine "Grid Free Energies (GFEs)," which provide per-atom contributions to fragment binding affinities. For essentially no additional computational overhead relative to the production of the FragMaps, GFEs can be used to compute Ligand Grid Free Energies (LGFEs) for arbitrarily complex molecules, and these LGFEs can be used to rank-order the molecules in accordance with binding affinities.

Terminal sialic acids on CD44 N-glycans can block hyaluronan binding by forming competing intramolecular contacts with arginine sidechains.

Specific sugar residues and their linkages form the basis of molecular recognition for interactions of glycoproteins with other biomolecules. Seemingly small changes, like the addition of a single monosaccharide in the covalently attached glycan component of glycoproteins, can greatly affect these interactions. For instance, the sialic acid capping of glycans affects protein-ligand binding involved in cell-cell and cell-matrix interactions. CD44 is a single-pass transmembrane glycoprotein whose binding with its carbohydrate ligand hyaluronan (HA), an extracellular matrix component, mediates processes such as leukocyte homing, cell adhesion, and tumor metastasis. This binding is highly regulated by glycosylation of the N-terminal extracellular hyaluronan-binding domain (HABD); specifically, sialic acid capped N-glycans of HABD inhibit ligand binding. However, the molecular mechanism behind this sialic acid mediated regulation has remained unknown. Two of the five N-glycosyation sites of HABD have been previously identified as having the greatest inhibitory effect on HA binding, but only if the glycans contain terminal sialic acid residues. These two sites, Asn25 and Asn120, were chosen for in silico glycosylation in this study. Here, from extensive standard molecular dynamics simulations and biased simulations, we propose a molecular mechanism for this behavior based on spontaneously-formed charge-paired hydrogen bonding interactions between the negatively-charged sialic acid residues and positively-charged Arg sidechains known to be critically important for binding to HA, which itself is negatively charged. Such intramolecular hydrogen bonds would preclude associations critical to hyaluronan binding. This observation suggests how CD44 and related glycoprotein binding is regulated by sialylation as cellular environments fluctuate.

CD44 receptor unfolding enhances binding by freeing basic amino acids to contact carbohydrate ligand.

The extracellular carbohydrate-binding domain of the Type I transmembrane receptor CD44 is known to undergo affinity switching, where change in conformation leads to enhanced binding of its carbohydrate ligand hyaluronan. Separate x-ray crystallographic and NMR experiments have led to competing explanations, with the former supporting minor conformational changes at the binding site and the latter a major order-to-disorder unfolding transition distant from the binding site. Here, all-atom explicit-solvent molecular dynamics studies employing adaptive biasing force sampling revealed a substantial favorable free-energy change associated with contact formation between the Arg(41) side chain and hyaluronan at the binding site, independent of whether the distant site was ordered or disordered. Analogous computational experiments on Arg(41)Ala mutants showed loss of this favorable free-energy change, consistent with existing experimental data. More provocatively, the simulation data revealed the molecular mechanism by which the order-to-disorder transition enhances hyaluronan binding: in the disordered state, a number of basic residues gain sufficient conformational freedom-lacking in the ordered state-to spontaneously form side-chain contacts with hyaluronan. Mutation of these residues to Ala had been known to decrease binding affinity, but there had previously been no structural explanation, given their lack of proximity to the carbohydrate-binding site in existing structures of the complex.

Peptide backbone sampling convergence with the adaptive biasing force algorithm.

Complete Boltzmann sampling of reaction coordinates in biomolecular systems continues to be a challenge for unbiased molecular dynamics simulations. A growing number of methods have been developed for applying biases to biomolecular systems to enhance sampling while enabling recovery of the unbiased (Boltzmann) distribution of states. The adaptive biasing force (ABF) algorithm is one such method and works by canceling out the average force along the desired reaction coordinate(s) using an estimate of this force progressively accumulated during the simulation. Upon completion of the simulation, the potential of mean force, and therefore Boltzmann distribution of states, is obtained by integrating this average force. In an effort to characterize the expected performance in applications such as protein loop sampling, ABF was applied to the full ranges of the Ramachandran φ/ψ backbone dihedral reaction coordinates for dipeptides of the 20 amino acids using all-atom explicit-water molecular dynamics simulations. Approximately half of the dipeptides exhibited robust and rapid convergence of the potential of mean force as a function of φ/ψ in triplicate 50 ns simulations, while the remainder exhibited varying degrees of less complete convergence. The greatest difficulties in achieving converged ABF sampling were seen in the branched-side chain amino acids threonine and valine, as well as the special case of proline. Proline dipeptide sampling was further complicated by trans-to-cis peptide bond isomerization not observed in unbiased control molecular dynamics simulations. Overall, the ABF method was found to be a robust means of sampling the entire φ/ψ reaction coordinate for the 20 amino acids, including high free-energy regions typically inaccessible in standard molecular dynamics simulations.