E. Prabhu Raman

CHARMM Additive All-Atom Force Field for Glycosidic Linkages in Carbohydrates Involving Furanoses

Presented is an extension of the CHARMM additive carbohydrate all-atom force field to enable modeling of polysaccharides containing furanose sugars. The new force field parameters encompass 1 ↔ 2, 1 → 3, 1 → 4, and 1 → 6 pyranose-furanose linkages and 2 → 1 and 2 → 6 furanose-furanose linkages, building on existing hexopyranose and furanose monosaccharide parameters. The model compounds were chosen to be monomers or glycosidic-linked dimers of tetrahydropyran (THP) and tetrahydrofuran (THF) as to contain the key atoms in full carbohydrates. Target data for optimization included two-dimensional quantum mechanical (QM) potential energy scans of the Φ/Ψ glycosidic dihedral angles, with geometry optimization at the MP2/6-31G(d) level followed by MP2/cc-pVTZ single-point energies. All possible chiralities of the model compounds at the linkage carbons were considered, and for each geometry, the THF ring was constrained to the favorable south or north conformations. Target data also included QM vibrational frequencies and pair interaction energies and distances with water molecules. Force field validation included comparison of computed crystal properties, aqueous solution densities, and NMR J-coupling constants to experimental reference values. Simulations of infinite crystals showed good agreement with experimental values for intramolecular geometries as well as for crystal unit cell parameters. Additionally, aqueous solution densities and available NMR data were reproduced to a high degree of accuracy, thus validating the hierarchically optimized parameters in both crystalline and aqueous condensed phases. The newly developed parameters allow for the modeling of linear, branched, and cyclic pyranose/furanose polysaccharides both alone and in heterogeneous systems including proteins, nucleic acids, and/or lipids when combined with existing additive CHARMM biomolecular force fields.

Reproducing Crystal Binding Modes of Ligand Functional Groups using Site-Identification by Ligand Competitive Saturation (SILCS) Simulations

The applicability of a computational method, Site Identification by Ligand Competitive Saturation (SILCS), to identify regions on a protein surface with which different types of functional groups on low-molecular weight inhibitors interact is demonstrated. The method involves molecular dynamics (MD) simulations of a protein in an aqueous solution of chemically diverse small molecules from which probability distributions of fragments types, termed FragMaps, are obtained. In the present application, SILCS simulations are performed with an aqueous solution of 1 M benzene and propane to map the affinity pattern of the protein for aromatic and aliphatic functional groups. In addition, water hydrogen and oxygen atoms serve as probes for hydrogen-bond donor and acceptor affinity, respectively. The method is tested using a set of 7 proteins for which crystal structures of complexes with several high affinity inhibitors are known. Good agreement is obtained between FragMaps and the positions of chemically similar functional groups in inhibitors as observed in the X-ray crystallographic structures. Quantitative capabilities of the SILCS approach are demonstrated by converting FragMaps to free energies, termed Grid Free Energies (GFE), and showing correlation between the GFE values and experimental binding affinities. For proteins for which ligand decoy sets are available, GFE values are shown to typically score the crystal conformation and conformations similar to it more favorable than decoys. Additionally, SILCS is tested for its ability to capture the subtle differences in ligand affinity across homologous proteins, information which may be of utility toward specificity-guided drug design. Taken together, our results show that SILCS can recapitulate the known location of functional groups of bound inhibitors for a number of proteins, suggesting that the method may be of utility for rational drug design.

Molecular Simulations of Dodecyl-β-maltoside Micelles in Water: Influence of the Headgroup Conformation and Force field Parameters

This paper deals with the development and validation of new potential parameter sets, based on the CHARMM36 and GLYCAM06 force fields, to simulate micelles of the two anomeric forms (α and β) of N-dodecyl-β-maltoside (C(12)G(2)), a surfactant widely used in the extraction and purification of membrane proteins. In this context, properties such as size, shape, internal structure, and hydration of the C(12)G(2) anomer micelles were thoroughly investigated by molecular dynamics simulations and the results compared with experiments. Additional simulations were also performed with the older CHARMM22 force field for carbohydrates (Kuttel, M.; et al. J. Comput. Chem. 2002, 23, 1236-1243). We find that our CHARMM and GLYCAM parameter sets yield similar results in the case of properties related to the micelle structure but differ for other properties such as the headgroup conformation or the micelle hydration. In agreement with experiments, our results show that for all model potentials the β-C(12)G(2) micelles have a more pronounced ellipsoidal shape than those containing α anomers. The computed radius of gyration is 20.2 and 25.4 Å for the α- and β-anomer micelles, respectively. Finally, we show that depending on the potential the water translational diffusion of the interfacial water is 7-11.5 times slower than that of bulk water due to the entrapment of the water in the micelle crevices. This retardation is independent of the headgroup in α- or β-anomers.

Molecular Dynamics Simulations of Ibuprofen Binding to Aβ Peptides

Using replica exchange molecular dynamics simulations and the implicit solvent model we probed binding of ibuprofen to Abeta(10-40) monomers and amyloid fibrils. We found that the concave (CV) fibril edge has significantly higher binding affinity for ibuprofen than the convex edge. Furthermore, binding of ibuprofen to Abeta monomers, as compared to fibrils, results in a smaller free energy gain. The difference in binding free energies is likely to be related to the presence of the groove on the CV fibril edge, in which ibuprofen tends to accumulate. The confinement effect of the groove promotes the formation of large low-energy ibuprofen clusters, which rarely occur on the surface of Abeta monomers. These observations led us to suggest that the ibuprofen binding mechanism for Abeta fibrils is different from that for monomers. In general, ibuprofen shows a preference to bind to those regions of Abeta monomers (amino terminal) and fibrils (the CV edge) that are also the primary aggregation interfaces. Based on our findings and on available experimental data, we propose a rationale for the ibuprofen antiaggregation effect.

Inclusion of multiple fragment types in the Site Identification by Ligand Competitive Saturation (SILCS) approach

The site identification by ligand competitive saturation (SILCS) method identifies the location and approximate affinities of small molecular fragments on a target macromolecular surface by performing molecular dynamics (MD) simulations of the target in an aqueous solution of small molecules representative of different chemical functional groups. In this study, we introduce a set of small molecules to map potential interactions made by neutral hydrogen bond donors and acceptors and charged donor and acceptor fragments in addition to nonpolar fragments. The affinity pattern is obtained in the form of discretized probability or, equivalently, free energy maps, called FragMaps, which can be visualized with the target surface. We performed SILCS simulations for four proteins for which structural and thermodynamic data is available for multiple diverse ligands. Good overlap is shown between high affinity regions identified by the FragMaps and the crystallographic positions of ligand functional groups with similar chemical functionality, thus demonstrating the validity of the qualitative information obtained from the simulations. To test the ability of FragMaps in providing quantitative predictions, we calculate the previously introduced ligand grid free energy (LGFE) metric and observe its correspondence with experimentally measured binding affinity. LGFE is computed for different conformational ensembles and improvement in prediction is shown with increasing ligand conformational sampling. Ensemble generation includes a Monte Carlo sampling approach that uses the GFE FragMaps directly as the energy function. The results show that some but not all experimental trends are predicted and warrant improvements in the scoring methodology. In addition, the potential utility of atom-based free energy contributions to the LGFE scores and the use of multiple ligands in SILCS to identify displaceable water molecules during ligand design are discussed.

Binding of nonsteroidal anti‐inflammatory drugs to Aβ fibril

Nonsteroidal anti‐inflammatory drugs are considered as potential therapeutic agents against Alzheimers disease. Using replica exchange molecular dynamics and atomistic implicit solvent model, we studied the mechanisms of binding of naproxen and ibuprofen to the Aβ fibril derived from solid‐state NMR measurements. The binding temperature of naproxen is found to be almost 40 K higher than of ibuprofen implicating higher binding affinity of naproxen. The key factor, which enhances naproxen binding, is strong interactions between ligands bound to the surface of the fibril. The naphthalene ring in naproxen appears to provide a dominant contribution to ligand‐ligand interactions. In contrast, ligand‐fibril interactions cannot explain differences in the binding affinities of naproxen and ibuprofen. The concave fibril edge with the groove is identified as the primary binding location for both ligands. We show that confinement of the ligands to the groove facilitates ligand‐ligand interactions that lowers the energy of the ligands bound to the concave edge compared with those bound to the convex edge. Our simulations appear to provide microscopic rationale for the differing binding affinities of naproxen and ibuprofen observed experimentally. Proteins 2010.

Site-specific fragment identification guided by single-step free energy perturbation calculations

The in-silico Site Identification by Ligand Competitive Saturation (SILCS) approach identifies the binding sites of representative chemical entities on the entire protein surface, information that can be applied for computational fragment-based drug design. In this study, we report an efficient computational protocol that uses sampling of the protein-fragment conformational space obtained from the SILCS simulations and performs single step free energy perturbation (SSFEP) calculations to identify site-specific favorable chemical modifications of benzene involving substitutions of ring hydrogens with individual non-hydrogen atoms. The SSFEP method is able to capture the experimental trends in relative hydration free energies of benzene analogues and for two datasets of experimental relative binding free energies of congeneric series of ligands of the proteins α-thrombin and P38 MAP kinase. The approach includes a protocol in which data obtained from SILCS simulations of the proteins is first analyzed to identify favorable benzene binding sites following which an ensemble of benzene-protein conformations for that site is obtained. The SSFEP protocol applied to that ensemble results in good reproduction of experimental free energies of the α-thrombin ligands, but not for P38 MAP kinase ligands. Comparison with results from a P38 full-ligand simulation and analysis of conformations reveals the reason for the poor agreement being the connectivity with the remainder of the ligand, a limitation inherent in fragment-based methods. Since the SSFEP approach can identify favorable benzene modifications as well as identify the most favorable fragment conformations, the obtained information can be of value for fragment linking or structure-based optimization.

Sampling of Organic Solutes in Aqueous and Heterogeneous Environments Using Oscillating Excess Chemical Potentials in Grand Canonical-like Monte Carlo-Molecular Dynamics Simulations.

Solute sampling of explicit bulk-phase aqueous environments in grand canonical (GC) ensemble simulations suffer from poor convergence due to low insertion probabilities of the solutes. To address this, we developed an iterative procedure involving Grand Canonical-like Monte Carlo (GCMC) and molecular dynamics (MD) simulations. Each iteration involves GCMC of both the solutes and water followed by MD, with the excess chemical potential (μex) of both the solute and the water oscillated to attain their target concentrations in the simulation system. By periodically varying the μex of the water and solutes over the GCMC-MD iterations, solute exchange probabilities and the spatial distributions of the solutes improved. The utility of the oscillating-μex GCMC-MD method is indicated by its ability to approximate the hydration free energy (HFE) of the individual solutes in aqueous solution as well as in dilute aqueous mixtures of multiple solutes. For seven organic solutes: benzene, propane, acetaldehyde, methanol, formamide, acetate, and methylammonium, the average μex of the solutes and the water converged close to their respective HFEs in both 1 M standard state and dilute aqueous mixture systems. The oscillating-μex GCMC methodology is also able to drive solute sampling in proteins in aqueous environments as shown using the occluded binding pocket of the T4 lysozyme L99A mutant as a model system. The approach was shown to satisfactorily reproduce the free energy of binding of benzene as well as sample the functional group requirements of the occluded pocket consistent with the crystal structures of known ligands bound to the L99A mutant as well as their relative binding affinities.

Nonsteroidal anti-inflammatory drug naproxen destabilizes Aβ amyloid fibrils: a molecular dynamics investigation.

Using implicit solvent model and replica exchange molecular dynamics, we examine the propensity of a nonsteroidal anti-inflammatory drug, naproxen, to interfere with Aβ fibril growth. We also compare the antiaggregation propensity of naproxen with that of ibuprofen. Naproxens antiaggregation effect is influenced by two factors. Similar to ibuprofen, naproxen destabilizes binding of incoming Aβ peptides to the fibril due to direct competition between the ligands and the peptides for the same binding location on the fibril surface (the edge). However, in contrast to ibuprofen, naproxen binding also alters the conformational ensemble of Aβ monomers by promoting β-structure. The second factor weakens naproxens antiaggregation effect. These findings appear to explain the experimental observations, in which naproxen binds to the Aβ fibril with higher affinity than ibuprofen, yet produces weaker antiaggregation action.

Molecular Dynamics Simulations of Anti-Aggregation Effect of Ibuprofen

Using implicit solvent molecular dynamics and replica exchange simulations, we study the impact of ibuprofen on the growth of wild-type Abeta fibrils. We show that binding of ibuprofen to Abeta destabilizes the interactions between incoming peptides and the fibril. As a result, ibuprofen interference modifies the free energy landscape of fibril growth and reduces the free energy gain of Abeta peptide binding to the fibril by approximately 2.5 RT at 360 K. Furthermore, ibuprofen interactions shift the thermodynamic equilibrium from fibril-like locked states to disordered docked states. Ibuprofens anti-aggregation effect is explained by its competition with incoming Abeta peptides for the same binding site located on the fibril edge. Although ibuprofen impedes fibril growth, it does not significantly change the mechanism of fibril elongation or the structure of Abeta peptides bound to the fibril.