Molecular dynamics studies of the STAT3 homodimer:DNA complex: relationships between STAT3 mutations and protein-DNA recognition.

Signal Transducers and Activators of Transcription (STAT) proteins are a group of latent cytoplasmic transcription factors involved in cytokine signaling. STAT3 is a member of the STAT family and is expressed at elevated levels in a large number of diverse human cancers and is now a validated target for anticancer drug discovery.. Understanding the dynamics of the STAT3 dimer interface, accounting for both protein-DNA and protein-protein interactions, with respect to the dynamics of the latent unphosphorylated STAT3 monomer, is important for designing potential small-molecule inhibitors of the activated dimer. Molecular dynamics (MD) simulations have been used to study the activated STAT3 homodimer:DNA complex and the latent unphosphorylated STAT3 monomer in an explicit water environment. Analysis of the data obtained from MD simulations over a 50 ns time frame has suggested how the transcription factor interacts with DNA, the nature of the conformational changes, and ways in which function may be affected. Examination of the dimer interface, focusing on the protein-DNA interactions, including involvement of water molecules, has revealed the key residues contributing to the recognition events involved in STAT3 protein-DNA interactions. This has shown that the majority of mutations in the DNA-binding domain are found at the protein-DNA interface. These mutations have been mapped in detail and related to specific protein-DNA contacts. Their structural stability is described, together with an analysis of the model as a starting-point for the discovery of novel small-molecule STAT3 inhibitors.

Molecular Dynamics of HIV1-Integrase in Complex with 93del - A Structural Perspective on the Mechanism of Inhibition

HIV1 integrase is an important target for the antiviral therapy. Guanine-rich quadruplex, such as 93del, have been shown to be potent inhibitors of this enzyme and thus representing a new class of antiviral agents. Although X-ray and NMR structures of HIV1 integrase and 93del have been reported, there is no structural information of the complex and the mechanism of inhibition still remains unexplored. A number of computational methods including automated protein-DNA docking and molecular dynamics simulation in explicit solvent were used to model the binding of 93del to HIV1 integrase. Analysis of the dynamic behaviour of the complex using principal components analysis and elastic network modelling techniques allow us to understand how the binding of 93del aptamer and its interactions with key residues affect the intrinsic motions of the catalytic loops by stabilising them in catalytically inactive conformations. Such insights into the structural mechanism of inhibition can aid in improving the design of anti-HIV aptamers.

The molecular dynamics of Trypanosoma brucei UDP-galactose 4'-epimerase:a drug target for African sleeping sickness

During the past century, several epidemics of human African trypanosomiasis, a deadly disease caused by the protist Trypanosoma brucei, have afflicted sub-Saharan Africa. Over 10 000 new victims are reported each year, with hundreds of thousands more at risk. As current drug treatments are either highly toxic or ineffective, novel trypanocides are urgently needed. The T. brucei galactose synthesis pathway is one potential therapeutic target. Although galactose is essential for T. brucei survival, the parasite lacks the transporters required to intake galactose from the environment. UDP-galactose 4'-epimerase (TbGalE) is responsible for the epimerization of UDP-glucose to UDP-galactose and is therefore of great interest to medicinal chemists. Using molecular dynamics simulations, we investigate the atomistic motions of TbGalE in both the apo and holo states. The sampled conformations and protein dynamics depend not only on the presence of a UDP-sugar ligand, but also on the chirality of the UDP-sugar C4 atom. This dependence provides important insights into TbGalE function and may help guide future computer-aided drug discovery efforts targeting this protein.

Using sketch-map coordinates to analyze and bias molecular dynamics simulations

When examining complex problems, such as the folding of proteins, coarse grained descriptions of the system drive our investigation and help us to rationalize the results. Oftentimes collective variables (CVs), derived through some chemical intuition about the process of interest, serve this purpose. Because finding these CVs is the most difficult part of any investigation, we recently developed a dimensionality reduction algorithm, sketch-map, that can be used to build a low-dimensional map of a phase space of high-dimensionality. In this paper we discuss how these machine-generated CVs can be used to accelerate the exploration of phase space and to reconstruct free-energy landscapes. To do so, we develop a formalism in which high-dimensional configurations are no longer represented by low-dimensional position vectors. Instead, for each configuration we calculate a probability distribution, which has a domain that encompasses the entirety of the low-dimensional space. To construct a biasing potential, we exploit an analogy with metadynamics and use the trajectory to adaptively construct a repulsive, history-dependent bias from the distributions that correspond to the previously visited configurations. This potential forces the system to explore more of phase space by making it desirable to adopt configurations whose distributions do not overlap with the bias. We apply this algorithm to a small model protein and succeed in reproducing the free-energy surface that we obtain from a parallel tempering calculation.

A self-learning algorithm for biased molecular dynamics

A new self-learning algorithm for accelerated dynamics, reconnaissance metadynamics, is proposed that is able to work with a very large number of collective coordinates. Acceleration of the dynamics is achieved by constructing a bias potential in terms of a patchwork of one-dimensional, locally valid collective coordinates. These collective coordinates are obtained from trajectory analyses so that they adapt to any new features encountered during the simulation. We show how this methodology can be used to enhance sampling in real chemical systems citing examples both from the physics of clusters and from the biological sciences.

A Molecular Dynamics Study of the Early Stages of Calcium Carbonate Growth

The precipitation of calcium carbonate in water has been examined using a combination of molecular dynamics and umbrella sampling. During 20 ns molecular dynamics trajectories at elevated calcium carbonate concentrations, amorphous particles are observed to form and appear to be composed of misaligned domains of vaterite and aragonite. The addition of further calcium ions to these clusters is found to be energetically favorable and virtually barrierless. By contrast, there is a large barrier to the addition of calcium to small calcite crystals. Thus, even though calcite nanocrystals are stable in solution, at high supersaturations, particles of amorphous material form because this material grows much faster than ordered calcite nanocrystals.

A Neutron Diffraction and Molecular Dynamics Investigation of Acetate-based Ionic Liquids as Solvents for Glucose

The liquid state structure of the ionic liquid, 1-ethyl-3-methylimidazolium acetate, and the solute/solvent structure of glucose dissolved in the ionic liquid at a 1: 6 molar ratio have been investigated at 323 K by molecular dynamics simulations and neutron diffraction experiments using H/D isotopically substituted materials. Interactions between hydrogen-bond donating cation sites and polar, directional hydrogen-bond accepting acetate anions are examined. Ion-ion radial distribution functions for the neat ionic liquid, calculated from both MD and derived from the empirical potential structure refinement model to the experimental data, show the alternating shell-structure of anions around the cation, as anticipated. Spatial probability distributions reveal the main anion-to-cation features as in-plane interactions of anions with imidazolium ring hydrogens and cation-cation planar stacking. Interestingly, the presence of the polarised hydrogen-bond acceptor anion leads to increased anion-anion tail-tail structuring within each anion shell, indicating the onset of hydrophobic regions within the anion regions of the liquid.

Molecular dynamics simulation model for the quantitative assessment of tool wear during single point diamond turning of cubic silicon carbide

Silicon carbide (SiC) is a material of great technological interest for engineering applications concerning hostile environments where silicon-based components cannot work (beyond 623 K). Single point diamond turning (SPDT) has remained a superior and viable method to harness process efficiency and freeform shapes on this harder material. However, it is extremely difficult to machine this ceramic consistently in the ductile regime due to sudden and rapid tool wear. It thus becomes non trivial to develop an accurate understanding of tool wear mechanism during SPDT of SiC in order to identify measures to suppress wear to minimize operational cost.

In this paper, molecular dynamics (MD) simulation has been deployed with a realistic analytical bond order potential (ABOP) formalism based potential energy function to understand tool wear mechanism during single point diamond turning of SiC. The most significant result was obtained using the radial distribution function which suggests graphitization of diamond tool during the machining process. This phenomenon occurs due to the abrasive processes between these two ultra hard materials. The abrasive action results in locally high temperature which compounds with the massive cutting forces leading to sp3–sp2 order–disorder transition of diamond tool. This represents the root cause of tool wear during SPDT operation of cubic SiC. Further testing led to the development of a novel method for quantitative assessment of the progression of diamond tool wear from MD simulations.

Simulations of Biased Agonists in the β2 Adrenergic Receptor with Accelerated Molecular Dynamics

The biased agonism of the G protein-coupled receptors (GPCRs), where in addition to a traditional G protein-signalling pathway a GPCR promotes intracellular signals though ß-arrestin, is a novel paradigm in pharmacology. Biochemical and biophysical studies have suggested that a GPCR forms a distinct ensemble of conformations signalling through the G protein and ß-arrestin. Here we report on the dynamics of the ß2 adrenergic receptor bound to the ß-arrestin and G protein biased agonists and the empty receptor to further characterize the receptor conformational changes caused by biased agonists. We use conventional and accelerated molecular dynamics (aMD) simulations to explore the conformational transitions of the GPCR from the active state to the inactive state. We found that aMD simulations enable monitoring the transition within the nanosecond timescale while capturing the known microscopic characteristics of the inactive states, such as the ionic lock, the inward position of F6.44, and water clusters. Distinct conformational states are shown to be stabilized by each biased agonist. In particular, in simulations of the receptor with the ß-arrestin biased agonist, N-cyclopentylbutanepherine we observe a different pattern of motions in helix 7 when compared to simulations with the G protein biased agonist, Salbutamol that involves perturbations of the network of interactions within the NPxxY motif. Understanding the network of interactions induced by biased ligands and the subsequent receptor conformational shifts will lead to development of more efficient drugs. © 2013 American Chemical Society

Molecular dynamics simulation of nanoindentation of Fe3C and tetrahedral Fe4C

Study of nanomechanical response of iron carbides is important because presence of iron carbides greatly influences the performance and longevity of steel components. This work contributes to the literature by exploring nanoindentation of nanocrystalline Fe3C and tetrahedral-Fe4C using molecular dynamics simulation. The chemical interactions of iron and carbon were described through an analytical bond order inter-atomic potential (ABOP) energy function. The indentations were performed at an indentation speed of 50 m/sec and a repeat trial was performed at 5 m/sec. Load-displacement (P-h) curve for both these carbides showed residual indentation depth and maximum indentation depth (hf/hmax) ratio to be higher than 0.7 i.e. a circumstance where Oliver and Pharr method was not appropriate to be applied to evaluate the material properties. Alternate evaluation revealed Fe3C to be much harder than Fe4C. Gibbs free energy of formation and radial distribution function, coupled with state of the average local temperature and von Mises stresses indicate the formation of a new phase of iron-carbide. Formation of this newer phase was found to be due to deviatoric strain rather than the high temperature induced in the substrate during nanoindentation

Nanoindentation of polysilicon and single crystal silicon: Molecular dynamics simulation and experimental validation

This paper presents novel advances on the deformation behaviour of polycrystalline and single crystal silicon using molecular dynamics (MD) simulation and validation of the same via nanoindentation experiments. In order to unravel the mechanism of deformation, four simulations were performed: Indentation of polycrystalline silicon substrate with a (i) Berkovich pyramidal and a (ii) spherical (arc) indenter, and indentation of a single crystal silicon substrate with these two indenters. The simulation results reveal that high pressure phase transformation (HPPT) in silicon (Si-I to Si-II phase transformation) occurred in all cases, however, its extent and the manner in which it occurred differed significantly between polycrystalline silicon and single crystal silicon, and was the main driver of differences in nanoindentation deformation behaviour between the two types of silicon. An interesting observation was that in polycrystalline silicon, the HPPT was observed to occur preferentially along the grain boundaries than across the grain boundaries. An automated dislocation extraction algorithm (DXA) revealed no dislocations in the deformation zone, suggesting HPPT to be the primary mechanism in inducing plasticity in silicon.

Diamond machining of silicon: A review of advances in molecular dynamics simulation

Molecular dynamics (MD) simulation has enhanced our understanding about ductile-regime machining of brittle materials such as silicon and germanium. In particular, MD simulation has helped understand the occurrence of brittle–ductile transition due to the high-pressure phase transformation (HPPT), which induces Herzfeld–Mott transition. In this paper, relevant MD simulation studies in conjunction with experimental studies are reviewed with a focus on (i) the importance of machining variables: undeformed chip thickness, feed rate, depth of cut, geometry of the cutting tool in influencing the state of the deviatoric stresses to cause HPPT in silicon, (ii) the influence of material properties: role of fracture toughness and hardness, crystal structure and anisotropy of the material, and (iii) phenomenological understanding of the wear of diamond cutting tools, which are all non-trivial for cost-effective manufacturing of silicon. The ongoing developmental work on potential energy functions is reviewed to identify opportunities for overcoming the current limitations of MD simulations. Potential research areas relating to how MD simulation might help improve existing manufacturing technologies are identified which may be of particular interest to early stage researchers.

Generalized Langevin equation: An efficient approach to nonequilibrium molecular dynamics of open systems

The generalized Langevin equation (GLE) has been recently suggested to simulate the time evolution of classical solid and molecular systems when considering general nonequilibrium processes. In this approach, a part of the whole system (an open system), which interacts and exchanges energy with its dissipative environment, is studied. Because the GLE is derived by projecting out exactly the harmonic environment, the coupling to it is realistic, while the equations of motion are non-Markovian. Although the GLE formalism has already found promising applications, e. g., in nanotribology and as a powerful thermostat for equilibration in classical molecular dynamics simulations, efficient algorithms to solve the GLE for realistic memory kernels are highly nontrivial, especially if the memory kernels decay nonexponentially. This is due to the fact that one has to generate a colored noise and take account of the memory effects in a consistent manner. In this paper, we present a simple, yet efficient, algorithm for solving the GLE for practical memory kernels and we demonstrate its capability for the exactly solvable case of a harmonic oscillator coupled to a Debye bath.