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Caffrey, B.

Publications and source records attributed to Caffrey, B..

2 recordsLinked to original sources

Cryo-EM reveals disrupted human p97 allosteric activation by disease mutations and inhibitor binding

The human AAA ATPase p97, a potential cancer target, plays a vital role in clearing misfolded proteins. p97 dysfunction is also known to play a crucial role in several neurodegenerative disorders. Here, we present cryo-EM structural analyses of four disease mutants p97R155H, p97R191Q, p97A232E, p97D592N, as well as p97E470D, implicated in resistance to the drug CB-5083. These structures demonstrate that the mutations affect nucleotide-driven p97 allosteric activation by predominantly interfering with either the coupling between the D1 and N-terminal domains (p97R155H and p97R191Q), the inter-protomer interactions (p97A232E), or the coupling between D1 and D2 nucleotide domains (p97D592N, p97E470D). We also show that binding of the competitive inhibitor CB-5083 to the D2 domain prevents conformational changes similar to that seen for mutations that affect coupling between D1 and D2 domains. Our studies enable tracing of the path of allosteric activation across p97 and establish a common mechanistic link between active site inhibition and defects in allosteric activation by disease-causing mutations.

biochemistry↗

Automated methods for 3D Segmentation of Focused Ion Beam-Scanning Electron Microscopic Images

Focused Ion Beam Scanning Electron Microscopy (FIB-SEM) is an imaging approach that enables analysis of the 3D architecture of cells and tissues at resolutions that are 1-2 orders of magnitude higher than that possible with light microscopy. The slow speeds of data collection and analysis are two critical problems that limit more extensive use of FIB-SEM technology. Here, we present a robust method that enables rapid, large-scale acquisition of data from tissue specimens, combined with an approach for automated data segmentation using machine learning, which dramatically increases the speed of image analysis. We demonstrate the feasibility of these methods through the 3D analysis of human muscle tissue by showing that our process results in an improvement in speed of up to three orders of magnitude as compared to manual approaches for data segmentation. All programs and scripts we use are open source and are immediately available for use by others.\n\nImpact StatementThe high-throughput, easy-to-use and versatile segmentation pipeline described in our manuscript will enable rapid, large-scale statistical analysis of sub-cellular structures in tissues.

cell biology↗