Search for: All records

Abstract The AAA protease FtsH associates with HflK/C subunits to form a megadalton-size complex that spans the inner membrane and extends into the periplasm ofE. coli. How this bacterial complex and homologous assemblies in eukaryotic organelles recruit, extract, and degrade membrane-embedded substrates is unclear. Following the overproduction of protein components, recent cryo-EM structures showed symmetric HflK/C cages surrounding FtsH in a manner proposed to inhibit the degradation of membrane-embedded substrates. Here, we present structures of native protein complexes, in which HflK/C instead forms an asymmetric nautilus-shaped assembly with an entryway for membrane-embedded substrates to reach and be engaged by FtsH. Consistent with this nautilus-like structure, proteomic assays suggest that HflK/C enhances FtsH degradation of certain membrane-embedded substrates. Membrane curvature in our FtsH•HflK/C complexes is opposite that of surrounding membrane regions, a property that correlates with lipid scramblase activity and possibly with FtsH’s function in the degradation of membrane-embedded proteins. 

Abstract AAA+ proteases degrade intracellular proteins in a highly specific manner.E. coliClpXP, for example, relies on a C-terminal ssrA tag or other terminal degron sequences to recognize proteins, which are then unfolded by ClpX and subsequently translocated through its axial channel and into the degradation chamber of ClpP for proteolysis. Prior cryo-EM structures reveal that the ssrA tag initially binds to a ClpX conformation in which the axial channel is closed by a pore-2 loop. Here, we show that substrate-free ClpXP has a nearly identical closed-channel conformation. We destabilize this closed-channel conformation by deleting residues from the ClpX pore-2 loop. Strikingly, open-channel ClpXP variants degrade non-native proteins lacking degrons faster than the parental enzymes in vitro but degraded GFP-ssrA more slowly. When expressed inE. coli, these open channel variants behave similarly to the wild-type enzyme in assays of filamentation and phage-Mu plating but resulted in reduced growth phenotypes at elevated temperatures or when cells were exposed to sub-lethal antibiotic concentrations. Thus, channel closure is an important determinant of ClpXP degradation specificity. 

Energy-dependent protein degradation by the AAA+ ClpXP protease helps maintain protein homeostasis in bacteria and eukaryotic organelles of bacterial origin. In  Escherichia coli and many other proteobacteria, the SspB adaptor assists ClpXP in degrading ssrA-tagged polypeptides produced as a consequence of tmRNA-mediated ribosome rescue. By tethering these incomplete ssrA-tagged proteins to ClpXP, SspB facilitates their efficient degradation at low substrate concentrations. How this process occurs structurally is unknown. Here, we present a cryo-EM structure of the SspB adaptor bound to a GFP-ssrA substrate and to ClpXP. This structure provides evidence for simultaneous contacts of SspB and ClpX with the ssrA tag within the tethering complex, allowing direct substrate handoff concomitant with the initiation of substrate translocation. Furthermore, our structure reveals that binding of the substrate·adaptor complex induces unexpected conformational changes within the spiral structure of the AAA+ ClpX hexamer and its interaction with the ClpP tetradecamer. 

In cryogenic electron microscopy (cryoEM), purified macromolecules are applied to a grid bearing a holey carbon foil; the molecules are then blotted to remove excess liquid and rapidly frozen in a roughly 20-100 nm thick layer of vitreous ice, suspended across roughly 1 µm wide foil holes. The resulting sample is imaged using cryogenic transmission electron microscopy, and after image processing using suitable software, near-atomic resolution structures can be determined. Despite cryoEM's widespread adoption, sample preparation remains a severe bottleneck in cryoEM workflows, with users often encountering challenges related to samples behaving poorly in the suspended vitreous ice. Recently, methods have been developed to modify cryoEM grids with a single continuous layer of graphene, which acts as a support surface that often increases particle density in the imaged area and can reduce interactions between particles and the air-water interface. Here, we provide detailed protocols for the application of graphene to cryoEM grids and for rapidly assessing the relative hydrophilicity of the resulting grids. Additionally, we describe an EM-based method to confirm the presence of graphene by visualizing its characteristic diffraction pattern. Finally, we demonstrate the utility of these graphene supports by rapidly reconstructing a 2.7 Å resolution density map of a Cas9 complex using a pure sample at a relatively low concentration. 

Abstract Spt-Ada-Gcn5-Acetyltransferase (SAGA) is a conserved multi-subunit complex that activates RNA polymerase II-mediated transcription by acetylating and deubiquitinating nucleosomal histones and by recruiting TATA box binding protein (TBP) to DNA. The prototypical yeast Saccharomyces cerevisiae SAGA contains 19 subunits that are organized into Tra1, core, histone acetyltransferase, and deubiquitination modules. Recent cryo-electron microscopy studies have generated high-resolution structural information on the Tra1 and core modules of yeast SAGA. However, the two catalytical modules were poorly resolved due to conformational flexibility of the full assembly. Furthermore, the high sample requirement created a formidable barrier to further structural investigations of SAGA. Here, we report a workflow for isolating/stabilizing yeast SAGA and preparing cryo-EM specimens at low protein concentration using a graphene oxide support layer. With this procedure, we were able to determine a cryo-EM reconstruction of yeast SAGA at 3.1 Å resolution and examine its conformational landscape with the neural network-based algorithm cryoDRGN. Our analysis revealed that SAGA adopts a range of conformations with its HAT module and central core in different orientations relative to Tra1. 

CryoDRGN is a machine learning system for heterogenous cryo-EM reconstruction of proteins and protein complexes from single particle cryo-EM data. Central to this approach is a deep generative model for heterogeneous cryo-EM density maps, which we empirically find effectively models both discrete and continuous forms of structural variability. Once trained, cryoDRGN is capable of generating an arbitrary number of 3D density maps, and thus interpreting the resulting ensemble is a challenge. Here, we showcase interactive and automated processing approaches for analyzing cryoDRGN results. Specifically, we detail a step-by-step protocol for analysis of the assembling 50S ribosome dataset (Davis et al., EMPIAR-10076), including preparation of inputs, network training, and visualization of the resulting ensemble of density maps. Additionally, we describe and implement methods to comprehensively analyze and interpret the distribution of volumes with the assistance of an associated atomic model. This protocol is appropriate for structural biologists familiar with processing single particle cryo-EM datasets and with moderate experience navigating Python and Jupyter notebooks. It requires 3-4 days to complete.