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

Automated multiconformer model building for X-ray crystallography and cryo-EM

In their folded state, biomolecules exchange between multiple conformational states that are crucial for their function. Traditional structural biology methods, such as X-ray crystallography and cryogenic electron microscopy (cryo-EM), produce density maps that are ensemble averages, reflecting molecules in various conformations. Yet, most models derived from these maps explicitly represent only a single conformation, overlooking the complexity of biomolecular structures. To accurately reflect the diversity of biomolecular forms, there is a pressing need to shift toward modeling structural ensembles that mirror the experimental data. However, the challenge of distinguishing signal from noise complicates manual efforts to create these models. In response, we introduce the latest enhancements to qFit, an automated computational strategy designed to incorporate protein conformational heterogeneity into models built into density maps. These algorithmic improvements in qFit are substantiated by superior Rfree and geometry metrics across a wide range of proteins. Importantly, unlike more complex multicopy ensemble models, the multiconformer models produced by qFit can be manually modified in most major model building software (e.g., Coot) and fit can be further improved by refinement using standard pipelines (e.g., Phenix, Refmac, Buster). By reducing the barrier of creating multiconformer models, qFit can foster the development of new hypotheses about the relationship between macromolecular conformational dynamics and function.

Identifying protein conformational states in the Protein Data Bank: Toward unlocking the potential of integrative dynamics studies

Studying protein dynamics and conformational heterogeneity is crucial for understanding biomolecular systems and treating disease. Despite the deposition of over 215 000 macromolecular structures in the Protein Data Bank and the advent of AI-based structure prediction tools such as AlphaFold2, RoseTTAFold, and ESMFold, static representations are typically produced, which fail to fully capture macromolecular motion. Here, we discuss the importance of integrating experimental structures with computational clustering to explore the conformational landscapes that manifest protein function. We describe the method developed by the Protein Data Bank in Europe - Knowledge Base to identify distinct conformational states, demonstrate the resource's primary use cases, through examples, and discuss the need for further efforts to annotate protein conformations with functional information. Such initiatives will be crucial in unlocking the potential of protein dynamics data, expediting drug discovery research, and deepening our understanding of macromolecular mechanisms.

Uncovering Protein Ensembles: Automated Multiconformer Model Building for X-ray Crystallography and Cryo-EM

In their folded state, biomolecules exchange between multiple conformational states that are crucial for their function. Traditional structural biology methods, such as X-ray crystallography and cryogenic electron microscopy (cryo-EM), produce density maps that are ensemble averages, reflecting molecules in various conformations. Yet, most models derived from these maps explicitly represent only a single conformation, overlooking the complexity of biomolecular structures. To accurately reflect the diversity of biomolecular forms, there is a pressing need to shift towards modeling structural ensembles that mirror the experimental data. However, the challenge of distinguishing signal from noise complicates manual efforts to create these models. In response, we introduce the latest enhancements to qFit, an automated computational strategy designed to incorporate protein conformational heterogeneity into models built into density maps. These algorithmic improvements in qFit are substantiated by superior R f r e e and geometry metrics across a wide range of proteins. Importantly, unlike more complex multicopy ensemble models, the multiconformer models produced by qFit can be manually modified in most major model building software (e.g. Coot) and fit can be further improved by refinement using standard pipelines (e.g. Phenix, Refmac, Buster). By reducing the barrier of creating multiconformer models, qFit can foster the development of new hypotheses about the relationship between macromolecular conformational dynamics and function.

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.

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.

Cryo-electron microscopy-based drug design

Structure-based drug design (SBDD) has gained popularity owing to its ability to develop more potent drugs compared to conventional drug-discovery methods. The success of SBDD relies heavily on obtaining the three-dimensional structures of drug targets. X-ray crystallography is the primary method used for solving structures and aiding the SBDD workflow; however, it is not suitable for all targets. With the resolution revolution, enabling routine high-resolution reconstruction of structures, cryogenic electron microscopy (cryo-EM) has emerged as a promising alternative and has attracted increasing attention in SBDD. Cryo-EM offers various advantages over X-ray crystallography and can potentially replace X-ray crystallography in SBDD. To fully utilize cryo-EM in drug discovery, understanding the strengths and weaknesses of this technique and noting the key advancements in the field are crucial. This review provides an overview of the general workflow of cryo-EM in SBDD and highlights technical innovations that enable its application in drug design. Furthermore, the most recent achievements in the cryo-EM methodology for drug discovery are discussed, demonstrating the potential of this technique for advancing drug development. By understanding the capabilities and advancements of cryo-EM, researchers can leverage the benefits of designing more effective drugs. This review concludes with a discussion of the future perspectives of cryo-EM-based SBDD, emphasizing the role of this technique in driving innovations in drug discovery and development. The integration of cryo-EM into the drug design process holds great promise for accelerating the discovery of new and improved therapeutic agents to combat various diseases.

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.

Sec7 regulatory domains scaffold autoinhibited and active conformations

The late stages of Golgi maturation involve a series of sequential trafficking events in which cargo-laden vesicles are produced and targeted to multiple distinct subcellular destinations. Each of these vesicle biogenesis events requires activation of an Arf GTPase by the Sec7/BIG guanine nucleotide exchange factor (GEF). Sec7 localization and activity is regulated by autoinhibition, positive feedback, and interaction with other GTPases. Although these mechanisms have been characterized biochemically, we lack a clear picture of how GEF localization and activity is modulated by these signals. Here we report the cryoEM structure of full-length Sec7 in its autoinhibited form, revealing the architecture of its multiple regulatory domains. We use functional experiments to determine the basis for autoinhibition and use structural predictions to produce a model for an active conformation of the GEF that is supported empirically. This study therefore elucidates the conformational transition that Sec7 undergoes to become active on the organelle membrane surface.

A closed translocation channel in the substrate-free AAA+ ClpXP protease diminishes rogue degradation

AAA+ proteases degrade intracellular proteins in a highly specific manner. E. coli ClpXP, 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 in E. 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.

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.

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.

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.