Modular Assembly of the Bacterial Large Ribosomal Subunit.

The ribosome is a complex macromolecular machine and serves as an ideal system for understanding biological macromolecular assembly. Direct observation of ribosome assembly in vivo is difficult, as few intermediates have been isolated and thoroughly characterized. Herein, we deploy a genetic system to starve cells of an essential ribosomal protein, which results in the accumulation of assembly intermediates that are competent for maturation. Quantitative mass spectrometry and single-particle cryo-electron microscopy reveal 13 distinct intermediates, which were each resolved to ∼4-5 Å resolution and could be placed in an assembly pathway. We find that ribosome biogenesis is a parallel process, that blocks of structured rRNA and proteins assemble cooperatively, and that the entire process is dynamic and can be "re-routed" through different pathways as needed. This work reveals the complex landscape of ribosome assembly in vivo and provides the requisite tools to characterize additional assembly pathways for ribosomes and other macromolecular machines.

Learning structural heterogeneity from cryo-electron sub-tomograms with tomoDRGN.

Cryo-electron tomography (cryo-ET) enables observation of macromolecular complexes in their native, spatially contextualized cellular environment. Cryo-ET processing software to visualize such complexes at nanometer resolution via iterative alignment and averaging are well developed but rely upon assumptions of structural homogeneity among the complexes of interest. Recently developed tools allow for some assessment of structural diversity but have limited capacity to represent highly heterogeneous structures, including those undergoing continuous conformational changes. Here we extend the highly expressive cryoDRGN (Deep Reconstructing Generative Networks) deep learning architecture, originally created for single-particle cryo-electron microscopy analysis, to cryo-ET. Our new tool, tomoDRGN, learns a continuous low-dimensional representation of structural heterogeneity in cryo-ET datasets while also learning to reconstruct heterogeneous structural ensembles supported by the underlying data. Using simulated and experimental data, we describe and benchmark architectural choices within tomoDRGN that are uniquely necessitated and enabled by cryo-ET. We additionally illustrate tomoDRGN's efficacy in analyzing diverse datasets, using it to reveal high-level organization of human immunodeficiency virus (HIV) capsid complexes assembled in virus-like particles and to resolve extensive structural heterogeneity among ribosomes imaged in situ.

The SspB adaptor drives structural changes in the AAA+ ClpXP protease during ssrA-tagged substrate delivery.

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.

CryoDRGN: reconstruction of heterogeneous cryo-EM structures using neural networks.

Cryo-electron microscopy (cryo-EM) single-particle analysis has proven powerful in determining the structures of rigid macromolecules. However, many imaged protein complexes exhibit conformational and compositional heterogeneity that poses a major challenge to existing three-dimensional reconstruction methods. Here, we present cryoDRGN, an algorithm that leverages the representation power of deep neural networks to directly reconstruct continuous distributions of 3D density maps and map per-particle heterogeneity of single-particle cryo-EM datasets. Using cryoDRGN, we uncovered residual heterogeneity in high-resolution datasets of the 80S ribosome and the RAG complex, revealed a new structural state of the assembling 50S ribosome, and visualized large-scale continuous motions of a spliceosome complex. CryoDRGN contains interactive tools to visualize a dataset's distribution of per-particle variability, generate density maps for exploratory analysis, extract particle subsets for use with other tools and generate trajectories to visualize molecular motions. CryoDRGN is open-source software freely available at http://cryodrgn.csail.mit.edu .

Role of Era in assembly and homeostasis of the ribosomal small subunit.

Assembly factors provide speed and directionality to the maturation process of the 30S subunit in bacteria. To gain a more precise understanding of how these proteins mediate 30S maturation, it is important to expand on studies of 30S assembly intermediates purified from bacterial strains lacking particular maturation factors. To reveal the role of the essential protein Era in the assembly of the 30S ribosomal subunit, we analyzed assembly intermediates that accumulated in Era-depleted Escherichia coli cells using quantitative mass spectrometry, high resolution cryo-electron microscopy and in-cell footprinting. Our combined approach allowed for visualization of the small subunit as it assembled and revealed that with the exception of key helices in the platform domain, all other 16S rRNA domains fold even in the absence of Era. Notably, the maturing particles did not stall while waiting for the platform domain to mature and instead re-routed their folding pathway to enable concerted maturation of other structural motifs spanning multiple rRNA domains. We also found that binding of Era to the mature 30S subunit destabilized helix 44 and the decoding center preventing binding of YjeQ, another assembly factor. This work establishes Era's role in ribosome assembly and suggests new roles in maintaining ribosome homeostasis.

Addressing preferred specimen orientation in single-particle cryo-EM through tilting.

We present a strategy for tackling preferred specimen orientation in single-particle cryogenic electron microscopy by employing tilts during data collection. We also describe a tool to quantify the resulting directional resolution using 3D Fourier shell correlation volumes. We applied these methods to determine the structures at near-atomic resolution of the influenza hemagglutinin trimer, which adopts a highly preferred specimen orientation, and of ribosomal biogenesis intermediates, which adopt moderately preferred orientations.

Binding properties of YjeQ (RsgA), RbfA, RimM and Era to assembly intermediates of the 30S subunit.

Our understanding regarding the function of YjeQ (also called RsgA), RbfA, RimM and Era in ribosome biogenesis has been derived in part from the study of immature 30S particles that accumulate in null strains lacking one of these factors. However, their mechanistic details are still unknown. Here, we demonstrate that these immature particles are not dead-end products of assembly, but progress into mature 30S subunits. Mass spectrometry analysis revealed that in vivo the occupancy level of these factors in these immature 30S particles is below 10% and that the concentration of factors does not increase when immature particles accumulate in cells. We measured by microscale thermophoresis that YjeQ and Era binds to the mature 30S subunit with high affinity. However, the binding affinity of these factors to the immature particles and of RimM and RbfA to mature or immature particles was weak, suggesting that binding is not occurring at physiological concentrations. These results suggest that in the absence of these factors, the immature particles evolve into a thermodynamically stable intermediate that exhibits low affinity for the assembly factors. These results imply that the true substrates of YjeQ, RbfA, RimM and Era are immature particles that precede the ribosomal particles accumulating in the knockouts strains.

YphC and YsxC GTPases assist the maturation of the central protuberance, GTPase associated region and functional core of the 50S ribosomal subunit.

YphC and YsxC are GTPases in Bacillus subtilis that facilitate the assembly of the 50S ribosomal subunit, however their roles in this process are still uncharacterized. To explore their function, we used strains in which the only copy of the yphC or ysxC genes were under the control of an inducible promoter. Under depletion conditions, they accumulated incomplete ribosomal subunits that we named 45SYphC and 44.5SYsxC particles. Quantitative mass spectrometry analysis and the 5-6 Å resolution cryo-EM maps of the 45SYphC and 44.5SYsxC particles revealed that the two GTPases participate in the maturation of the central protuberance, GTPase associated region and key RNA helices in the A, P and E functional sites of the 50S subunit. We observed that YphC and YsxC bind specifically to the two immature particles, suggesting that they represent either on-pathway intermediates or that their structure has not significantly diverged from that of the actual substrate. These results describe the nature of these immature particles, a widely used tool to study the assembly process of the ribosome. They also provide the first insights into the function of YphC and YsxC in 50S subunit assembly and are consistent with this process occurring through multiple parallel pathways, as it has been described for the 30S subunit.

Functional interaction between ribosomal protein L6 and RbgA during ribosome assembly.

RbgA is an essential GTPase that participates in the assembly of the large ribosomal subunit in Bacillus subtilis and its homologs are implicated in mitochondrial and eukaryotic large subunit assembly. How RbgA functions in this process is still poorly understood. To gain insight into the function of RbgA we isolated suppressor mutations that partially restored the growth of an RbgA mutation (RbgA-F6A) that caused a severe growth defect. Analysis of these suppressors identified mutations in rplF, encoding ribosomal protein L6. The suppressor strains all accumulated a novel ribosome intermediate that migrates at 44S in sucrose gradients. All of the mutations cluster in a region of L6 that is in close contact with helix 97 of the 23S rRNA. In vitro maturation assays indicate that the L6 substitutions allow the defective RbgA-F6A protein to function more effectively in ribosome maturation. Our results suggest that RbgA functions to properly position L6 on the ribosome, prior to the incorporation of L16 and other late assembly proteins.

Functional domains of the 50S subunit mature late in the assembly process.

Despite the identification of many factors that facilitate ribosome assembly, the molecular mechanisms by which they drive ribosome biogenesis are poorly understood. Here, we analyze the late stages of assembly of the 50S subunit using Bacillus subtilis cells depleted of RbgA, a highly conserved GTPase. We found that RbgA-depleted cells accumulate late assembly intermediates bearing sub-stoichiometric quantities of ribosomal proteins L16, L27, L28, L33a, L35 and L36. Using a novel pulse labeling/quantitative mass spectrometry technique, we show that this particle is physiologically relevant and is capable of maturing into a complete 50S particle. Cryo-electron microscopy and chemical probing revealed that the central protuberance, the GTPase associating region and tRNA-binding sites in this intermediate are unstructured. These findings demonstrate that key functional sites of the 50S subunit remain unstructured until late stages of maturation, preventing the incomplete subunit from prematurely engaging in translation. Finally, structural and biochemical analysis of a ribosome particle depleted of L16 indicate that L16 binding is necessary for the stimulation of RbgA GTPase activity and, in turn, release of this co-factor, and for conversion of the intermediate to a complete 50S subunit.

NCOA4 initiates ferritinophagy by binding GATE16 using two highly avid short linear interaction motifs.

Cells carefully regulate cytosolic iron, which is a vital enzymatic cofactor, yet is toxic in excess. In mammalian cells, surplus iron is sequestered in ferritin cages that, in iron limiting conditions, are degraded through the selective autophagy pathway ferritinophagy to liberate free iron. Prior work identified the ferritinophagy receptor protein NCOA4, which links ferritin and LC3/GABARAP-family member GATE16, effectively tethering ferritin to the autophagic machinery. Here, we elucidate the molecular mechanism underlying this interaction, discovering two short linear motifs in NCOA4 that each bind GATE16 with weak affinity. These binding motifs are highly avid and, in concert, support high-affinity NCOA4•GATE16 complex formation. We further find the minimal NCOA4383-522 fragment bearing these motifs is sufficient for ferritinophagy and that both motifs are necessary for this activity. This work suggests a general mechanism wherein selective autophagy receptors can distinguish between the inactive soluble pools of LC3/GABARAPs and the active membrane-conjugated forms that drive autophagy. Finally, we find that iron decreases NCOA4383-522's affinity for GATE16, providing a plausible mechanism for iron-dependent regulation of ferritinophagy.

Rapid structural analysis of bacterial ribosomes in situ.

Rapid structural analysis of purified proteins and their complexes has become increasingly common thanks to key methodological advances in cryo-electron microscopy (cryo-EM) and associated data processing software packages. In contrast, analogous structural analysis in cells via cryo-electron tomography (cryo-ET) remains challenging due to critical technical bottlenecks, including low-throughput sample preparation and imaging, and laborious data processing methods. Here, we describe the development of a rapid in situ cryo-ET sample preparation and data analysis workflow that results in the routine determination of sub-nm resolution ribosomal structures. We apply this workflow to E. coli, producing a 5.8 Å structure of the 70S ribosome from cells in less than 10 days, and we expect this workflow will be widely applicable to related bacterial samples.

Design, construction and characterization of a set of insulated bacterial promoters.

We have generated a series of variable-strength, constitutive, bacterial promoters that act predictably in different sequence contexts, span two orders of magnitude in strength and contain convenient sites for cloning and the introduction of downstream open-reading frames. Importantly, their design insulates these promoters from the stimulatory or repressive effects of many 5'- or 3'-sequence elements. We show that different promoters from our library produce constant relative levels of two different proteins in multiple genetic contexts. This set of promoters should be a useful resource for the synthetic-biology community.

Learning structural heterogeneity from cryo-electron sub-tomograms with tomoDRGN.

Cryo-electron tomography (cryo-ET) allows one to observe macromolecular complexes in their native, spatially contextualized environment. Tools to visualize such complexes at nanometer resolution via iterative alignment and averaging are well-developed but rely on assumptions of structural homogeneity among the complexes under consideration. Recently developed downstream analysis tools allow for some assessment of macromolecular diversity but have limited capacity to represent highly heterogeneous macromolecules, including those undergoing continuous conformational changes. Here, we extend the highly expressive cryoDRGN deep learning architecture, originally created for cryo-electron microscopy single particle analysis, to sub-tomograms. Our new tool, tomoDRGN, learns a continuous low-dimensional representation of structural heterogeneity in cryo-ET datasets while also learning to reconstruct a large, heterogeneous ensemble of structures supported by the underlying data. Using simulated and experimental data, we describe and benchmark architectural choices within tomoDRGN that are uniquely necessitated and enabled by cryo-ET data. We additionally illustrate tomoDRGN's efficacy in analyzing an exemplar dataset, using it to reveal extensive structural heterogeneity among ribosomes imaged in situ.

Application of Monolayer Graphene to Cryo-Electron Microscopy Grids for High-resolution Structure Determination.

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.

Application of monolayer graphene to cryo-electron microscopy grids for high-resolution structure determination.

In cryogenic electron microscopy (cryo-EM), purified macromolecules are typically applied to a grid bearing a holey carbon foil, blotted to remove excess liquid and rapidly frozen in a roughly 20-100 nm thick layer of vitreous ice that is suspended across roughly 1 µm-wide foil holes. The resulting sample is then imaged using cryogenic transmission electron microscopy and, after substantial image processing, near-atomic resolution structures can be determined. Despite cryo-EM's widespread adoption, sample preparation remains a severe bottleneck in cryo-EM workflows, with users often encountering challenges related to samples behaving poorly in the suspended vitreous ice. Recently, methods have been developed to modify cryo-EM 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 cryo-EM 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 an exemplar Cas9 complex using a highly pure sample at a relatively low concentration.

A proteolytic AAA+ machine poised to unfold a protein substrate.

AAA+ proteolytic machines unfold proteins prior to degradation. Cryo-EM of a ClpXP-substrate complex reveals a postulated but heretofore unseen intermediate in substrate unfolding/degradation. The natively folded substrate is drawn tightly against the ClpX channel by interactions between axial pore loops and the substrate degron tail, and by contacts with the native substrate that are, in part, enabled by movement of one ClpX subunit out of the typically observed hexameric spiral.

KsgA facilitates ribosomal small subunit maturation by proofreading a key structural lesion.

Ribosome assembly is orchestrated by many assembly factors, including ribosomal RNA methyltransferases, whose precise role is poorly understood. Here, we leverage the power of cryo-EM and machine learning to discover that the E. coli methyltransferase KsgA performs a 'proofreading' function in the assembly of the small ribosomal subunit by recognizing and partially disassembling particles that have matured but are not competent for translation. We propose that this activity allows inactive particles an opportunity to reassemble into an active state, thereby increasing overall assembly fidelity. Detailed structural quantifications in our datasets additionally enabled the expansion of the Nomura assembly map to highlight rRNA helix and r-protein interdependencies, detailing how the binding and docking of these elements are tightly coupled. These results have wide-ranging implications for our understanding of the quality-control mechanisms governing ribosome biogenesis and showcase the power of heterogeneity analysis in cryo-EM to unveil functionally relevant information in biological systems.

Imaging structurally dynamic ribosomes with cryogenic electron microscopy.

Throughout the history of electron microscopy, ribosomes have served as an ideal subject for imaging and technological development, which in turn has driven our understanding of ribosomal biology. Here, we provide a historical perspective at the intersection of electron microscopy technology development and ribosome biology and reflect on how this technique has shed light on each stage of the life cycle of this dynamic macromolecular machine. With an emphasis on prokaryotic systems, we specifically describe how pairing cryo-EM with clever experimental design, time-resolved techniques, and next-generation heterogeneous structural analysis has afforded insights into the modular nature of assembly, the roles of the many transient biogenesis and translation co-factors, and the subtle variations in structure and function between strains and species. The work concludes with a prospective outlook on the field, highlighting the pivotal role cryogenic electron tomography is playing in adding cellular context to our understanding of ribosomal life cycles, and noting how this exciting technology promises to bridge the gap between cellular and structural biology.

Uncovering structural ensembles from single-particle cryo-EM data using cryoDRGN.

Single-particle cryogenic electron microscopy (cryo-EM) has emerged as a powerful technique to visualize the structural landscape sampled by a protein complex. However, algorithmic and computational bottlenecks in analyzing heterogeneous cryo-EM datasets have prevented the full realization of this potential. CryoDRGN is a machine learning system for heterogeneous 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 is effective in modeling 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 the analysis of an existing assembling 50S ribosome dataset, 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. CryoDRGN is open source software that is freely available.