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Sharff, Andrew

Publications and source records attributed to Sharff, Andrew.

Novel Vinculin Binding Site of the IpaA Invasin of Shigella

Internalization of Shigella into host epithelial cells, where the bacteria replicates and spreads to neighboring cells, requires a type 3 secretion system (T3SS) effector coined IpaA. IpaA binds directly to and activates the cytoskeletal protein vinculin after injection in the host cell cytosol, and this was previously thought to be directed by two amphipathic {alpha}-helical vinculin-binding sites (VBS) found in the C-terminal tail domain of IpaA. Here, we report a third VBS, IpaA-VBS3, that is located N-terminal to the other two VBSs of IpaA and show that one IpaA molecule can bind up to three vinculin molecules. Biochemical in vitro Shigella invasion assays and the 1.6 {angstrom} crystal structure of the vinculin {center_dot} IpaA-VBS3 complex showed that IpaA-VBS3 is functionally redundant with the other two IpaA-VBSs in cell invasion and in activating the latent F-actin binding functions of vinculin. Multiple VBSs in IpaA are reminiscent of talin, which harbors 11 VBSs. However, most of the talin VBSs have low affinity and are buried in helix bundles, whereas all three of the VBSs of IpaA are high affinity, readily available, and in close proximity to each other in the IpaA structure. Although deletion of IpaA-VBS3 has no detectable effects on Shigella invasion of epithelial cells, deletion of all three VBSs impaired bacterial invasion to levels found in an ipaA null mutant strain. Thus, IpaA-directed mimicry of talin in activating vinculin occurs through three high affinity VBSs that are essential for Shigella pathogenesis.

59 BASIC BIOLOGICAL SCIENCES↗

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 http://scripts.iucr.org/cgi-bin/cr.cgi?rm, where -target enables the correct ligand-binding structure to be found, and http://scripts.iucr.org/cgi-bin/cr.cgi?rm, where -autoncs contributes to the location of an additional copy of the cyclic peptide ligand.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

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.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗