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1.  Functional and structural characterization of a thermostable acetyl esterase from Thermotoga maritima 
Proteins  2012;80(6):1545-1559.
TM0077 from Thermotoga maritima is a member of the carbohydrate esterase family 7 and is active on a variety of acetylated compounds, including cephalosporin C. TM0077 esterase activity is confined to short-chain acyl esters (C2-C3), and is optimal around 100°C and pH 7.5. The positional specificity of TM0077 was investigated using 4-nitrophenyl-β-D-xylopyranoside monoacetates as substrates in a β-xylosidase-coupled assay. TM0077 hydrolyzes acetate at positions 2, 3 and 4 with equal efficiency. No activity was detected on xylan or acetylated xylan, which implies that TM0077 is an acetyl esterase and not an acetyl xylan esterase as currently annotated. Selenomethionine-substituted and native structures of TM0077 were determined at 2.1 Å and 2.5 Å resolution, respectively, revealing a classic α/β-hydrolase fold. TM0077 assembles into a doughnut-shaped hexamer with small tunnels on either side leading to an inner cavity, which contains the six catalytic centers. Structures of TM0077 with covalently bound phenylmethylsulfonyl fluoride (PMSF) and paraoxon were determined to 2.4 Å and 2.1 Å, respectively, and confirmed that both inhibitors bind covalently to the catalytic serine (Ser188). Upon binding of inhibitor, the catalytic serine adopts an altered conformation, as observed in other esterase and lipases, and supports a previously proposed catalytic mechanism in which this Ser hydroxyl rotation prevents reversal of the reaction and allows access of a water molecule for completion of the reaction.
PMCID: PMC3348966  PMID: 22411095
Acetyl esterase; Thermotoga maritima; crystal structure; α/β hydrolase; inhibitor; serine rotation
2.  Exploiting structure similarity in refinement: automated NCS and target-structure restraints in BUSTER  
Local structural similarity restraints (LSSR) provide a novel method for exploiting NCS or structural similarity to an external target structure. Two examples are given where BUSTER re-refinement of PDB entries with LSSR produces marked improvements, enabling further structural features to be modelled.
Maximum-likelihood X-ray macromolecular structure refinement in BUSTER has been extended with restraints facilitating the exploitation of structural similarity. The similarity can be between two or more chains within the structure being refined, thus favouring NCS, or to a distinct ‘target’ structure that remains fixed during refinement. The local structural similarity restraints (LSSR) approach considers all distances less than 5.5 Å between pairs of atoms in the chain to be restrained. For each, the difference from the distance between the corresponding atoms in the related chain is found. LSSR applies a restraint penalty on each difference. A functional form that reaches a plateau for large differences is used to avoid the restraints distorting parts of the structure that are not similar. Because LSSR are local, there is no need to separate out domains. Some restraint pruning is still necessary, but this has been automated. LSSR have been available to academic users of BUSTER since 2009 with the easy-to-use -autoncs and -­target target.pdb options. The use of LSSR is illustrated in the re-refinement of PDB entries 5rnt, where -target enables the correct ligand-binding structure to be found, and 1osg, where -autoncs contributes to the location of an additional copy of the cyclic peptide ligand.
PMCID: PMC3322596  PMID: 22505257
BUSTER; NCS restraints; target-structure restraints; local structural similarity restraints
3.  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.
PMCID: PMC3069744  PMID: 21460447
autoPROC; data processing

Results 1-3 (3)