Structural studies of AraR from B. thetaiotaomicron

The NrtR family of bacterial transcription factors is characterized by an N-terminal Nudix hydrolase-like effector binding domain and a C-terminal DNA binding domain. A bioinformatics analysis of the NrtR family represented by uncharacterized protein BT0354 in Bacteroides thetaiotaomicron suggests that these regulators control the catabolic pathways for L-arabinose. Many bacteria use L-arabinose as the sole source of carbon energy. The L-arabinose utilization pathway and its transcriptional regulation have been studied for a long time in several model microorganisms. Here we provide biochemical and structural characterization of the novel arabinose-responsive regulator of NrtR family protein BT0354, L-arabinose regulator from B. thetaiotaomicron (BtAraR). The BtAraR DNA binding and the role of effector molecule L-arabinose were confirmed using electrophoretic mobility shift assays. We have solved the crystal structures of BtAraR for two apo forms, and complexes with L-arabinose and double-stranded DNA target. The apo-1 form was solved as two dimers/AU in the R3 space group at 2.35 Å, while the apo-2 form was solved as one monomer/AU in the I213 space group at 2.56 Å resolution. The L-arabinose and DNA complex structures were solved as a dimer/AU in the P21 space group at 1.95 Å resolution and the P23 space group at 3.05 Å resolution, respectively. The biological unit of this protein is a dimer while the N-terminal ligand binding domain of the monomer adopts a Nudix hydrolase-like fold and the C-terminal DNA binding domain is a winged helix-turn-helix. The DNA binding-releasing mechanism can be rationalized through the comparison and analyses of these structures. The apo and DNA bound structures are more similar compared to the L-arabinose-bound structure. The r.m.s. deviation for the apo and DNA bound structures is 1.13 Å, while that for apo and the L-arabinose-bound structures is 4.54 Å. Details about the DNA binding mode, L-arabinose binding and L-arabinose induced structural change will be presented. This work was supported by National Institutes of Health grant GM094585 and by the U. S. Department of Energy, Office of Biological and Environmental Research, under contract DE-AC02-06CH11357.

A study of one-dimensional incommensurate modulated structure determination in high-resolution transmission electron microscopy

An approach has been developed for the first time to determine the displacive and substitutional modulation functions of an incommensurate modulated structure from a limited-size high-resolution transmission electron microscope image and electron-diffraction data.

Crystal structure prediction of indomethacin

Indomethacin is a non-steroidal anti-inflammatory and antipyretic agent. Because different packing arrangements of the same drug can greatly affect drug properties such as colours, solubility, stability, melting point, dissolution rate and so forth, it is important to predict its polymorphs. The computational prediction of the stable form will reduce undesirable risks in both clinical trials and manufacturing. Reported polymorphs of indomethacin include α, β, γ, δ, ε, η and ζ [1], of which only the thermodynamically stable form γ and the metastable form α are determined. Density functional theory with dispersion-correction (DFT-D) has been used extensively to study molecular crystal structures[2]. It gives better results with a compromise between the computational cost and accuracy towards the reproduction of molecular crystal structures. In the fourth blind test of crystal structure prediction in 2007, the DFT-D method gave a very successful result that predicted all four structures correctly. Rather than using transferable force fields, a dedicated tailor-made force field (TMFF) parameterised by DFT-D calculations[3] is used for every chemical compound. The force field is used to generate a set of crystal structures and delimit a candidate window for energy ranking. The powder diffraction patterns of predicted polymorphs are calculated to compare with experimental data.

Computational Pharmaceutical Materials Science: Beyond Static Structures.

The five Crystal-Structure Prediction (CSP) Blind Tests have shown that molecular-mechanics force fields are not accurate enough for crystal structure prediction[1]. The first--and only--method to successfully predict all four target crystal structures of one of the CSP Blind Tests was dispersion-corrected Density Functional Theory (DFT-D), and this is what we use for our work. However, quantum-mechanical methods (such as DFT-D), are too slow to allow simulations that include the effects of time and temperature, certainly for the size of molecules that are common in pharmaceutical industry. Including the effects of time and temperature therefore still requires molecular dynamics (MD) with less accurate force fields. In order to combine the accuracy of the successful DFT-D method with the speed of a force field to enable molecular dynamics, our group uses Tailor-Made Force Fields (TMFFs) as described by Neumann[2]. In Neumann's TMFF approach, the force field for each chemical compound of interest is parameterised from scratch against reference data from DFT-D calculations; in other words, the TMFF is fitted to mimic the DFT-D energy potential. Parameterising a dedicated force field for each individual compound requires an investment of several weeks, but has the advantage that the resulting force field is more accurate than a transferable force field. Combining crystal-structure prediction with DFT-D followed by molecular dynamics with a tailor-made force field allows us to calculate e.g. the temperature-dependent unit-cell expansion of each predicted polymorph, as well as possible temperature-dependent disorder. This is relevant for example when comparing the calculated X-ray powder diffraction patterns of the predicted crystal structures against experimental data.

Interpreting Disordered PXRD Structures using Molecular Dynamics

A central topic in the formulation of solid medicinal products is the identification of a suitable solid form of an active compound to obtain optimal physicochemical properties. To this end, disorder may be important for relevant crystal properties like stability. For example, disorder may account for more than 10% of the crystal volume. A rational approach to solid-form selection is typically based on structural information at atomic resolution. In practice, pharmaceutical compounds are not always well-behaved and especially in the study of polymorphs or compounds with flexible groups it can be challenging to obtain crystals suitable for single-crystal X-ray diffraction. Powder X-ray diffraction (PXRD) is a popular alternative, but it generally requires supplementary information like molecular connectivity in simple cases or computational models to solve larger structures. Computational modeling has come a long way and accurate and reliable structures of pharmaceutically relevant compounds can indeed be obtained using laboratory PXRD measurements and quantum-mechanical calculations [1]. The major limitation of quantum mechanical calculations, however, is that they do not consider time nor temperature but only static structures at zero temperature. Thus, these methods cannot model phenomena related to disorder. The molecular dynamics (MD) method can add temperature as well as time and spatial resolution to a model and has in recent years developed to be a scalable, reliable and increasingly available technique. As more and more groups from academia as well as industry employ MD in their work, the development will increase to gain momentum in the coming years. We use MD in a high-performance setting to study crystal properties that are relevant for pharmaceutical research. Using a combination of models from first principles and MD we are able to study highly disordered structures and polymorphs on the basis of PXRD data.