Richard A. Bryce

Carbohydrate-Protein Recognition: Molecular Dynamics Simulations and Free Energy Analysis of Oligosaccharide Binding to Concanavalin A

Carbohydrate ligands are important mediators of biomolecular recognition. Microcalorimetry has found the complex-type N-linked glycan core pentasaccharide beta-GlcNAc-(1-->2)-alpha-Man-(1-->3)-[beta-GlcNAc-(1-->2)-alpha-Man-(1-->6)]-Man to bind to the lectin, Concanavalin A, with almost the same affinity as the trimannoside, Man-alpha-(1-->6)-[Man-alpha-(1-->3)]-Man. Recent determination of the structure of the pentasaccharide complex found a glycosidic linkage psi torsion angle to be distorted by 50 degrees from the NMR solution value and perturbation of some key mannose-protein interactions observed in the structures of the mono- and trimannoside complexes. To unravel the free energy contributions to binding and to determine the structural basis for this degeneracy, we present the results of a series of nanosecond molecular dynamics simulations, coupled to analysis via the recently developed MM-GB/SA approach (Srinivasan et al., J. Am. Chem. Soc. 1998, 120:9401-9409). These calculations indicate that the strength of key mannose-protein interactions at the monosaccharide site is preserved in both the oligosaccharides. Although distortion of the pentasaccharide is significant, the principal factor in reduced binding is incomplete offset of ligand and protein desolvation due to poorly matched polar interactions. This analysis implies that, although Concanavalin A tolerates the additional 6 arm GlcNAc present in the pentasaccharide, it does not serve as a key recognition determinant.

Cooperative effects in the structuring of fluoride water clusters: Ab initio hybrid quantum mechanical/molecular mechanical model incorporating polarizable fluctuating charge solvent

A new hybrid quantum mechanical/molecular mechanical model of solvation is developed and used to describe the structure and dynamics of small fluoride/water clusters, using an ab initio wave function to model the ion and a fluctuating charge potential to model the waters. Appropriate parameters for the water–water and fluoride–water interactions are derived, with the fluoride anion being described by density functional theory and a large Gaussian basis. The role of solvent polarization in determining the structure and energetics of F(H2O)4− clusters is investigated, predicting a slightly greater stability of the interior compared to the surface structure, in agreement with ab initio studies. An extended Lagrangian treatment of the polarizable water, in which the water atomic charges fluctuate dynamically, is used to study the dynamics of F(H2O)4− cluster. A simulation using a fixed solvent charge distribution indicates principally interior, solvated states for the cluster. However, a preponderance of tris...

Synthesis and Biological Evaluation of Coumarin-Based Inhibitors of NAD(P)H: Quinone Oxidoreductase-1 (NQO1)†

The synthesis is reported here of two novel series of inhibitors of human NAD(P)H quinone oxidoreductase-1 (NQO1), an enzyme overexpressed in several types of tumor cell. The first series comprises substituted symmetric dicoumarol analogues; the second series contains hybrid compounds where one 4-hydroxycoumarin system is replaced by a different aromatic moiety. Several compounds show equivalent or improved NQO1 inhibition over dicoumarol, both in the presence and in the absence of added protein. Further, correlation is demonstrated between the ability of these agents to inhibit NQO1 and computed binding affinity. We have solved the crystal structure of NQO1 complexed to a hybrid compound and find good agreement with the in silico model. For both MIA PaCa-2 pancreatic tumor cells and HCT116 colon cancer cells, dicoumarol shows the greatest toxicity of all compounds. Thus, we provide a computational, synthetic, and biological platform to generate competitive NQO1 inhibitors with superior pharmacological properties to dicoumarol. This will allow a more definitive study of NQO1 activity in cells, in particular, its drug activating/detoxifying properties and ability to modulate oncoprotein stability.

Discovery of novel inhibitors of Trypanosoma cruzi trans-sialidase from in silico screening

trans-Sialidase from Trypanosoma cruzi (TcTS) has emerged as a potential drug target for treatment of Chagas disease. Here, we report the results of virtual screening for the discovery of novel TcTS inhibitors, which targeted both the sialic acid and sialic acid acceptor sites of this enzyme. A library prepared from the Evotec database of commercially available compounds was screened using the molecular docking program GOLD, following the application of drug-likeness filters. Twenty-three compounds selected from the top-scoring ligands were purchased and assayed using a fluorimetric assay. Novel inhibitor scaffolds, with IC(50) values in the submillimolar range were discovered. The 3-benzothiazol-2-yl-4-phenyl-but-3-enoic acid scaffold was studied in more detail, and TcTS inhibition was confirmed by an alternative sialic acid transfer assay. Attempts to obtain crystal structures of these compounds with TcTS proved unsuccessful but provided evidence of ligand binding at the active site.

Rational drug design in parasitology: trans-sialidase as a case study for Chagas disease

Trypanosoma cruzi trans-sialidase is a potential target for Chagas disease chemotherapy. From the specific need of T. cruzi to obtain sialic acid through trans-sialidase-mediated transfers from host sources and the lack of alternative to this for the parasite, a good case can be made for T. cruzi trans-sialidase to serve as a potential drug target against Chagas disease. This review deals with both the particular aspects relevant to T. cruzi trans-sialidase as a target and generalises the situation for drug design in its broader aspects on the basis of some special problems in terms of rational drug design that T. cruzi trans-sialidase raises, particularly those of multiple gene copies and active site plasticity.

Molecular dynamics and free energy analysis of neuraminidase–ligand interactions

We report molecular dynamics calculations of neuraminidase in complex with an inhibitor, 4‐amino‐2‐deoxy‐2,3‐didehydro‐N‐acetylneuraminic acid (N‐DANA), with subsequent free energy analysis of binding by using a combined molecular mechanics/continuum solvent model approach. A dynamical model of the complex containing an ionized Glu119 amino acid residue is found to be consistent with experimental data. Computational analysis indicates a major van der Waals component to the inhibitor‐neuraminidase binding free energy. Based on the N‐DANA/neuraminidase molecular dynamics trajectory, a perturbation methodology was used to predict the binding affinity of related neuraminidase inhibitors by using a force field/Poisson‐Boltzmann potential. This approach, incorporating conformational search/local minimization schemes with distance‐dependent dielectric or generalized Born solvent models, correctly identifies the most potent neuraminidase inhibitor. Mutation of the key ligand four‐substituent to a hydrogen atom indicates no favorable binding free energy contribution of a hydroxyl group; conversely, cationic substituents form favorable electrostatic interactions with neuraminidase. Prospects for further development of the method as an analysis and rational design tool are discussed.

The application of quantum mechanics in structure-based drug design

Introduction: Computational chemistry has become an established and valuable component in structure-based drug design. However the chemical complexity of many ligands and active sites challenges the accuracy of the empirical potentials commonly used to describe these systems. Consequently, there is a growing interest in utilizing electronic structure methods for addressing problems in protein–ligand recognition. Areas covered: In this review, the authors discuss recent progress in the development and application of quantum chemical approaches to modeling protein–ligand interactions. The authors specifically consider the development of quantum mechanics (QM) approaches for studying large molecular systems pertinent to biology, focusing on protein–ligand docking, protein–ligand binding affinities and ligand strain on binding. Expert opinion: Although computation of binding energies remains a challenging and evolving area, current QM methods can underpin improved docking approaches and offer detailed insights into ligand strain and into the nature and relative strengths of complex active site interactions. The authors envisage that QM will become an increasingly routine and valued tool of the computational medicinal chemist.

Assessment of QM/MM Scoring Functions for Molecular Docking to HIV-1 Protease

We explore the ability of four quantum mechanical (QM)/molecular mechanical (MM) models to accurately identify the native pose of six HIV-1 protease inhibitors and compare them with the AMBER force field and ChemScore and GoldScore scoring functions. Three QM/MM scoring functions treated the ligand at the HF/6-31G*, AM1d, and PM3 levels; the fourth QM/MM function modeled the ligand and active site at the PM3-D level. For the discrimination of native from non-native poses, solvent-corrected HF/6-31G*:AMBER and AMBER functions exhibited the best overall performance. While the electrostatic component of the MM and QM/MM functions appears important for discriminating the native pose of the ligand, the polarization contribution in the QM/MM functions was relatively insensitive to a ligands binding mode and, for one ligand, actually hindered discrimination. The inclusion of a desolvation penalty, here using a generalized Born solvent model, improved discrimination for the MM and QM/MM methods. There appeared to be no advantage to binding mode prediction by incorporating active site polarization at the PM3-D level. Finally, we found that choice of the protonation state of the aspartyl dyad in the HIV-1 protease active site influenced the ability of scoring methods to determine the native binding pose.

A SOLVATION MODEL USING A HYBRID QUANTUM MECHANICAL/MOLECULAR MECHANICAL POTENTIAL WITH FLUCTUATING SOLVENT CHARGES

Abstract A solvation model is described which utilises a hybrid quantum mechanical/molecular mechanical potential incorporating solvent polarization via a classical fluctuating charge method. The model is implemented within a molecular dynamics scheme which treats the fluctuating charges as dynamical variables and is validated using a number of dimers involving water.

Exploring reaction pathways with transition path and umbrella sampling: Application to methyl maltoside

The transition path sampling (TPS) method is a powerful approach to study chemical reactions or transitional properties on complex potential energy landscapes. One of the main advantages of the method over potential of mean force methods is that reaction rates can be directly accessed without knowledge of the exact reaction coordinate. We have investigated the complementary nature of these two differing approaches, comparing transition path sampling with the weighted histogram analysis method to study a conformational change in a small model system. In this case study, the transition paths for a transition between two rotational conformers of a model disaccharide molecule, methyl beta-D-maltoside, were compared with a free energy surface constrained by the two commonly used glycosidic (phi,psi) torsional angles. The TPS method revealed a reaction channel that was not apparent from the potential of mean force method, and the suitability of phi and psi as reaction coordinates to describe the isomerization in vacuo was confirmed by examination of the transition path ensemble. Using both transition state theory and transition path sampling methods, the transition rate was estimated. We have estimated a characteristic time between transitions of approximately 160 ns for this rare isomerization event between the two conformations of the carbohydrate. We conclude that transition path sampling can extract subtle information about the dynamics not apparent from the potential of mean force method. However, in calculating the reaction rate, the transition path sampling method required 27.5 times the computational effort than was needed by the potential of mean force method.