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1.  Fragment-Based Discovery of Type I Inhibitors of Maternal Embryonic Leucine Zipper Kinase 
Fragment-based drug design was successfully applied to maternal embryonic leucine zipper kinase (MELK). A low affinity (160 μM) fragment hit was identified, which bound to the hinge region with an atypical binding mode, and this was optimized using structure-based design into a low-nanomolar and cell-penetrant inhibitor, with a good selectivity profile, suitable for use as a chemical probe for elucidation of MELK biology.
PMCID: PMC4291735  PMID: 25589925
Maternal embryonic leucine zipper kinase; fragment-based drug design; structure-based optimization
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.  Crystal Structure and Functional Analysis of the SARS-Coronavirus RNA Cap 2′-O-Methyltransferase nsp10/nsp16 Complex 
PLoS Pathogens  2011;7(5):e1002059.
Cellular and viral S-adenosylmethionine-dependent methyltransferases are involved in many regulated processes such as metabolism, detoxification, signal transduction, chromatin remodeling, nucleic acid processing, and mRNA capping. The Severe Acute Respiratory Syndrome coronavirus nsp16 protein is a S-adenosylmethionine-dependent (nucleoside-2′-O)-methyltransferase only active in the presence of its activating partner nsp10. We report the nsp10/nsp16 complex structure at 2.0 Å resolution, which shows nsp10 bound to nsp16 through a ∼930 Å2 surface area in nsp10. Functional assays identify key residues involved in nsp10/nsp16 association, and in RNA binding or catalysis, the latter likely through a SN2-like mechanism. We present two other crystal structures, the inhibitor Sinefungin bound in the S-adenosylmethionine binding pocket and the tighter complex nsp10(Y96F)/nsp16, providing the first structural insight into the regulation of RNA capping enzymes in (+)RNA viruses.
Author Summary
A novel coronavirus emerged in 2003 and was identified as the etiological agent of the deadly disease called Severe Acute Respiratory Syndrome. This coronavirus replicates and transcribes its giant genome using sixteen non-structural proteins (nsp1-16). Viral RNAs are capped to ensure stability, efficient translation, and evading the innate immunity system of the host cell. The nsp16 protein is a RNA cap modifying enzyme only active in the presence of its activating partner nsp10. We have crystallized the nsp10/16 complex and report its crystal structure at atomic resolution. Nsp10 binds to nsp16 through a ∼930 Å2 activation surface area in nsp10, and the resulting complex exhibits RNA cap (nucleoside-2′-O)-methyltransferase activity. We have performed mutational and functional assays to identify key residues involved in catalysis and/or in RNA binding, and in the association of nsp10 to nsp16. We present two additional crystal structures, that of the known inhibitor Sinefungin bound in the SAM binding pocket, and that of a tighter complex made of the mutant nsp10(Y96F) bound to nsp16. Our study provides a basis for antiviral drug design as well as the first structural insight into the regulation of RNA capping enzymes in (+)RNA viruses.
PMCID: PMC3102710  PMID: 21637813
4.  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 1-4 (4)