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1.  Crystal Structure of Archaeoglobus fulgidus CTP:Inositol-1-Phosphate Cytidylyltransferase, a Key Enzyme for Di-myo-Inositol-Phosphate Synthesis in (Hyper)Thermophiles▿† 
Journal of Bacteriology  2011;193(9):2177-2185.
Many Archaea and Bacteria isolated from hot, marine environments accumulate di-myo-inositol-phosphate (DIP), primarily in response to heat stress. The biosynthesis of this compatible solute involves the activation of inositol to CDP-inositol via the action of a recently discovered CTP:inositol-1-phosphate cytidylyltransferase (IPCT) activity. In most cases, IPCT is part of a bifunctional enzyme comprising two domains: a cytoplasmic domain with IPCT activity and a membrane domain catalyzing the synthesis of di-myo-inositol-1,3′-phosphate-1′-phosphate from CDP-inositol and l-myo-inositol phosphate. Herein, we describe the first X-ray structure of the IPCT domain of the bifunctional enzyme from the hyperthermophilic archaeon Archaeoglobus fulgidus DSMZ 7324. The structure of the enzyme in the apo form was solved to a 1.9-Å resolution. The enzyme exhibited apparent Km values of 0.9 and 0.6 mM for inositol-1-phosphate and CTP, respectively. The optimal temperature for catalysis was in the range 90 to 95°C, and the Vmax determined at 90°C was 62.9 μmol · min−1 · mg of protein−1. The structure of IPCT is composed of a central seven-stranded mixed β-sheet, of which six β-strands are parallel, surrounded by six α-helices, a fold reminiscent of the dinucleotide-binding Rossmann fold. The enzyme shares structural homology with other pyrophosphorylases showing the canonical motif G-X-G-T-(R/S)-X4-P-K. CTP, l-myo-inositol-1-phosphate, and CDP-inositol were docked into the catalytic site, which provided insights into the binding mode and high specificity of the enzyme for CTP. This work is an important step toward the final goal of understanding the full catalytic route for DIP synthesis in the native, bifunctional enzyme.
doi:10.1128/JB.01543-10
PMCID: PMC3133074  PMID: 21378188
2.  Data processing and analysis with the autoPROC toolbox 
Typical topics and problems encountered during data processing of diffraction experiments are discussed and the tools provided in the autoPROC software are described.
A typical diffraction experiment will generate many images and data sets from different crystals in a very short time. This creates a challenge for the high-throughput operation of modern synchrotron beamlines as well as for the subsequent data processing. Novice users in particular may feel overwhelmed by the tables, plots and numbers that the different data-processing programs and software packages present to them. Here, some of the more common problems that a user has to deal with when processing a set of images that will finally make up a processed data set are shown, concentrating on difficulties that may often show up during the first steps along the path of turning the experiment (i.e. data collection) into a model (i.e. interpreted electron density). Difficulties such as unexpected crystal forms, issues in crystal handling and suboptimal choices of data-collection strategies can often be dealt with, or at least diagnosed, by analysing specific data characteristics during processing. In the end, one wants to distinguish problems over which one has no immediate control once the experiment is finished from problems that can be remedied a posteriori. A new software package, autoPROC, is also presented that combines third-party processing programs with new tools and an automated workflow script that is intended to provide users with both guidance and insight into the offline processing of data affected by the difficulties mentioned above, with particular emphasis on the automated treatment of multi-sweep data sets collected on multi-axis goniostats.
doi:10.1107/S0907444911007773
PMCID: PMC3069744  PMID: 21460447
autoPROC; data processing

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