The ExoU type III secretion enzyme is a potent phospholipase A secreted by the Gram-negative opportunistic pathogen, Activation of phospholipase activity is induced by protein-protein interactions with ubiquitin in the cytosol of a targeted eukaryotic cell, leading to destruction of host cell membranes. Previous work in our laboratory suggested that conformational changes within a C-terminal domain of the toxin might be involved in the activation mechanism. In this study, we use site-directed spin-labeling electron paramagnetic resonance spectroscopy to investigate conformational changes in a C-terminal four-helical bundle region of ExoU as it interacts with lipid substrates and ubiquitin, and to examine the localization of this domain with respect to the lipid bilayer. In the absence of ubiquitin or substrate liposomes, the overall structure of the C-terminal domain is in good agreement with crystallographic models derived from ExoU in complex with its chaperone, SpcU. Significant conformational changes are observed throughout the domain in the presence of ubiquitin and liposomes combined that are not observed with either liposomes or ubiquitin alone. In the presence of ubiquitin, two interhelical loops of the C-terminal four-helix bundle appear to penetrate the membrane bilayer, stabilizing ExoU-membrane association. Thus, ubiquitin and the substrate lipid bilayer act synergistically to induce a conformational rearrangement in the C-terminal domain of ExoU.

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Achromobacter xylosoxidans is increasingly recognized as a colonizer of cystic fibrosis (CF) patients, but the role that A. xylosoxidans plays in pathology remains unknown. This knowledge gap is largely due to the lack of model systems available to study the toxic potential of this bacterium. Recently, a phospholipase A2 (PLA2) encoded by a majority of A. xylosoxidans genomes, termed AxoU, was identified. Here, we show that AxoU is a type III secretion system (T3SS) substrate that induces cytotoxicity to mammalian cells. A tissue culture model was developed showing that a subset of A. xylosoxidans isolates from CF patients induce cytotoxicity in macrophages, suggestive of a pathogenic or inflammatory role in the CF lung. In a toxic strain, cytotoxicity is correlated with transcriptional activation of axoU and T3SS genes, demonstrating that this model can be used as a tool to identify and track expression of virulence determinants produced by this poorly understood bacterium.

Despite advances in sampling and scoring strategies, Monte Carlo modeling methods still struggle to accurately predict de novo the structures of large proteins, membrane proteins, or proteins of complex topologies. Previous approaches have addressed these shortcomings by leveraging sparse distance data gathered using site-directed spin labeling and electron paramagnetic resonance spectroscopy to improve protein structure prediction and refinement outcomes. However, existing computational implementations entail compromises between coarse-grained models of the spin label that lower the resolution and explicit models that lead to resource-intense simulations. These methods are further limited by their reliance on distance distributions, which are calculated from a primary refocused echo decay signal and contain uncertainties that may require manual refinement. Here, we addressed these challenges by developing RosettaDEER, a scoring method within the Rosetta software suite capable of simulating double electron-electron resonance spectroscopy decay traces and distance distributions between spin labels fast enough to fold proteins de novo. We demonstrate that the accuracy of resulting distance distributions match or exceed those generated by more computationally intensive methods. Moreover, decay traces generated from these distributions recapitulate intermolecular background coupling parameters even when the time window of data collection is truncated. As a result, RosettaDEER can discriminate between poorly folded and native-like models by using decay traces that cannot be accurately converted into distance distributions using regularized fitting approaches. Finally, using two challenging test cases, we demonstrate that RosettaDEER leverages these experimental data for protein fold prediction more effectively than previous methods. These benchmarking results confirm that RosettaDEER can effectively leverage sparse experimental data for a wide array of modeling applications built into the Rosetta software suite.

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