High-confidence 3D template matching for cryo-electron tomography.

Visual proteomics attempts to build atlases of the molecular content of cells but the automated annotation of cryo electron tomograms remains challenging. Template matching (TM) and methods based on machine learning detect structural signatures of macromolecules. However, their applicability remains limited in terms of both the abundance and size of the molecular targets. Here we show that the performance of TM is greatly improved by using template-specific search parameter optimization and by including higher-resolution information. We establish a TM pipeline with systematically tuned parameters for the automated, objective and comprehensive identification of structures with confidence 10 to 100-fold above the noise level. We demonstrate high-fidelity and high-confidence localizations of nuclear pore complexes, vaults, ribosomes, proteasomes, fatty acid synthases, lipid membranes and microtubules, and individual subunits inside crowded eukaryotic cells. We provide software tools for the generic implementation of our method that is broadly applicable towards realizing visual proteomics.

Free-Standing DNA Origami Superlattice to Facilitate Cryo-EM Visualization of Membrane Vesicles.

Technological breakthroughs in cryo-electron microscopy (cryo-EM) methods open new perspectives for highly detailed structural characterizations of extracellular vesicles (EVs) and synthetic liposome-protein assemblies. Structural characterizations of these vesicles in solution under a nearly native hydrated state are of great importance to decipher cell-to-cell communication and to improve EVs' application as markers in diagnosis and as drug carriers in disease therapy. However, difficulties in preparing holey carbon cryo-EM grids with low vesicle heterogeneities, at low concentration and with kinetic control of the chemical reactions or assembly processes, have limited cryo-EM use in the EV study. We report a straightforward membrane vesicle cryo-EM sample preparation method that assists in circumventing these limitations by using a free-standing DNA-affinity superlattice for covering holey carbon cryo-EM grids. Our approach uses DNA origami to self-assemble to a solution-stable and micrometer-sized ordered molecular template in which structure and functional properties can be rationally controlled. We engineered the template with cholesterol-binding sites to specifically trap membrane vesicles. The advantages of this DNA-cholesterol-affinity lattice (DCAL) include (1) local enrichment of artificial and biological vesicles at low concentration and (2) isolation of heterogeneous cell-derived membrane vesicles (exosomes) from a prepurified pellet of cell culture conditioned medium on the grid.

Modeling and Simulation of the NMDA Receptor at Coarse-Grained and Atomistic Levels.

N-Methyl-D-aspartate (NMDA) receptors are glutamate-gated excitatory channels that play essential roles in brain functions. While high-resolution structures were solved for an allosterically inhibited form of functional NMDA receptor, other key functional states (particularly the active open-channel state) have not yet been resolved at atomic resolutions. To decrypt the molecular mechanism of the NMDA receptor activation, structural modeling and simulation are instrumental in providing detailed information about the dynamics and energetics of the receptor in various functional states. In this chapter, we describe coarse-grained modeling of the NMDA receptor using an elastic network model and related modeling/analysis tools (e.g., normal mode analysis, flexibility and hotspot analysis, cryo-EM flexible fitting, and transition pathway modeling) based on available structures. Additionally, we show how to build an atomistic model of the active-state receptor with targeted molecular dynamics (MD) simulation and explore its energetics and dynamics with conventional MD simulation. Taken together, these modeling and simulation can offer rich structural and dynamic information which will guide experimental studies of the activation of this key receptor.

CryoEM-sampling of metastable conformations appearing in cofactor-ligand association and catalysis of glutamate dehydrogenase.

Kinetic aspects of enzymatic reactions are described by equations based on the Michaelis-Menten theory for the initial stage. However, the kinetic parameters provide little information on the atomic mechanism of the reaction. In this study, we analyzed structures of glutamate dehydrogenase in the initial and steady stages of the reaction using cryoEM at near-atomic resolution. In the initial stage, four metastable conformations displayed different domain motions and cofactor/ligand association modes. The most striking finding was that the enzyme-cofactor-substrate complex, treated as a single state in the enzyme kinetic theory, comprised at least three different metastable conformations. In the steady stage, seven conformations, including derivatives from the four conformations in the initial stage, made the reaction pathway complicated. Based on the visualized conformations, we discussed stage-dependent pathways to illustrate the dynamics of the enzyme in action.

Single-particle Cryo-EM and molecular dynamics simulations: A perfect match.

Knowledge of the structure and dynamics of biomolecules is key to understanding the mechanisms underlying their biological functions. Single-particle cryo-electron microscopy (cryo-EM) is a powerful structural biology technique to characterize complex biomolecular systems. Here, we review recent advances of how Molecular Dynamics (MD) simulations are being used to increase and enhance the information extracted from cryo-EM experiments. We will particularly focus on the physics underlying these experiments, how MD facilitates structure refinement, in particular for heterogeneous and non-isotropic resolution, and how thermodynamic and kinetic information can be extracted from cryo-EM data.

Cryo2StructData: A Large Labeled Cryo-EM Density Map Dataset for AI-based Modeling of Protein Structures.

The advent of single-particle cryo-electron microscopy (cryo-EM) has brought forth a new era of structural biology, enabling the routine determination of large biological molecules and their complexes at atomic resolution. The high-resolution structures of biological macromolecules and their complexes significantly expedite biomedical research and drug discovery. However, automatically and accurately building atomic models from high-resolution cryo-EM density maps is still time-consuming and challenging when template-based models are unavailable. Artificial intelligence (AI) methods such as deep learning trained on limited amount of labeled cryo-EM density maps generate inaccurate atomic models. To address this issue, we created a dataset called Cryo2StructData consisting of 7,600 preprocessed cryo-EM density maps whose voxels are labelled according to their corresponding known atomic structures for training and testing AI methods to build atomic models from cryo-EM density maps. Cryo2StructData is larger than existing, publicly available datasets for training AI methods to build atomic protein structures from cryo-EM density maps. We trained and tested deep learning models on Cryo2StructData to validate its quality showing that it is ready for being used to train and test AI methods for building atomic models.

How to apply the broad toolbox of correlative light and electron microscopy to address a specific biological question.

Correlative light and electron microscopy (CLEM) is an approach that combines the strength of multiple imaging techniques to obtain complementary information about a given specimen. The "toolbox" for CLEM is broad, making it sometimes difficult to choose an appropriate approach for a given biological question. In this chapter, we provide experimental details for three CLEM approaches that can help the interested reader in designing a personalized CLEM strategy for obtaining ultrastructural data by using transmission electron microscopy (TEM). First, we describe chemical fixation of cells grown on a solid support (broadest approach). Second, we apply high-pressure freezing/freeze substitution to describe cellular ultrastructure (cryo-immobilization approach). Third, we give a protocol for a ultrastructural labeling by immuno-electron microscopy (immuno-EM approach). In addition, we also describe how to overlay fluorescence and electron microscopy images, an approach that is applicable to each of the reported different CLEM strategies. Here we provide step-by step descriptions prior to discussing possible technical problems and variations of these three general schemes to suit different models or different biological questions. This chapter is written for electron microscopists that are new to CLEM and unsure how to begin. Therefore, our protocols are meant to provide basic information with further references that should help the reader get started with applying a tailored strategy for a specific CLEM experiment.

Building a super-resolution fluorescence cryomicroscope.

Correlated super-resolution fluorescence microscopy and cryo-electron microscopy enables imaging with both high labeling specificity and high resolution. Naturally, combining two sophisticated imaging techniques within one workflow also introduces new requirements on hardware, such as the need for a super-resolution fluorescence capable microscope that can be used to image cryogenic samples. In this chapter, we describe the design and use of the "cryoscope"; a microscope designed for single-molecule localization microscopy (SMLM) of cryoEM samples that fits right into established cryoEM workflows. We demonstrate the results that can be achieved with our microscope by imaging fluorescently labeled vimentin, an intermediate filament, within U2OS cells grown on EM grids, and we provide detailed 3d models that encompass the entire design of the microscope.

Imaging intracellular components in situ using super-resolution cryo-correlative light and electron microscopy.

Super-resolution cryo-correlative light and electron microscopy (SRcryoCLEM) is emerging as a powerful method to enable targeted in situ structural studies of biological samples. By combining the high specificity and localization accuracy of single-molecule localization microscopy (cryoSMLM) with the high resolution of cryo-electron tomography (cryoET), this method enables accurately targeted data acquisition and the observation and identification of biomolecules within their natural cellular context. Despite its potential, the adaptation of SRcryoCLEM has been hindered by the need for specialized equipment and expertise. In this chapter, we outline a workflow for cryoSMLM and cryoET-based SRcryoCLEM, and we demonstrate that, given the right tools, it is possible to incorporate cryoSMLM into an established cryoET workflow. Using Vimentin as an exemplary target of interest, we demonstrate all stages of an SRcryoCLEM experiment: performing cryoSMLM, targeting cryoET acquisition based on single-molecule localization maps, and correlation of cryoSMLM and cryoET datasets using scNodes, a software package dedicated to SRcryoCLEM. By showing how SRcryoCLEM enables the imaging of specific intracellular components in situ, we hope to facilitate adoption of the technique within the field of cryoEM.

Correlative cryo-microscopy pipelines for in situ cellular studies.

Correlative cryo-microscopy pipelines combining light and electron microscopy and tomography in cryogenic conditions (cryoCLEM) on the same sample are powerful methods for investigating the structure of specific cellular targets identified by a fluorescent tag within their unperturbed cellular environment. CryoCLEM approaches circumvent one of the inherent limitations of cryo EM, and specifically cryo electron tomography (cryoET), of identifying the imaged structures in the crowded 3D environment of cells. Whereas several cryoCLEM approaches are based on thinning the sample by cryo FIB milling, here we present detailed protocols of two alternative cryoCLEM approaches for in situ studies of adherent cells at the single-cell level without the need for such cryo-thinning. The first approach is a complete cryogenic pipeline in which both fluorescence and electronic imaging are performed on frozen-hydrated samples, the second is a hybrid cryoCLEM approach in which fluorescence imaging is performed at room temperature, followed by rapid freezing and subsequent cryoEM imaging. We provide a detailed description of the two methods we have employed for imaging fluorescently labeled cellular structures with thickness below 350-500nm, such as cell protrusions and organelles located in the peripheral areas of the cells.

Toward quantitative super-resolution methods for cryo-CLEM.

Cryogenic ultrastructural imaging techniques such as cryo-electron tomography have produced a revolution in how the structure of biological systems is investigated by enabling the determination of structures of protein complexes immersed in a complex biological matrix within vitrified cell and model organisms. However, so far, the portfolio of successes has been mostly limited to highly abundant complexes or to structures that are relatively unambiguous and easy to identify through electron microscopy. In order to realize the full potential of this revolution, researchers would have to be able to pinpoint lower abundance species and obtain functional annotations on the state of objects of interest which would then be correlated to ultrastructural information to build a complete picture of the structure-function relationships underpinning biological processes. Fluorescence imaging at cryogenic conditions has the potential to be able to meet these demands. However, wide-field images acquired at low numeric aperture (NA) using air immersion objective have a low resolving power and cannot provide accurate enough three-dimensional (3D) localization to enable the assignment of functional annotations to individual objects of interest or target sample debulking to ensure the preservation of the structures of interest. It is therefore necessary to develop super-resolved cryo-fluorescence workflows capable of fulfilling this role and enabling new biological discoveries. In this chapter, we present the current state of development of two super-resolution cryogenic fluorescence techniques, superSIL-STORM and astigmatism-based 3D STORM, show their application to a variety of biological systems and discuss their advantages and limitations. We further discuss the future applicability to cryo-CLEM workflows though examples of practical application to the study of membrane protein complexes both in mammalian cells and in Escherichia coli.

Swin-cryoEM: Multi-class cryo-electron micrographs single particle mixed detection method.

Cryo-electron micrograph images have various characteristics such as varying sizes, shapes, and distribution densities of individual particles, severe background noise, high levels of impurities, irregular shapes, blurred edges, and similar color to the background. How to demonstrate good adaptability in the field of image vision by picking up single particles from multiple types of cryo-electron micrographs is currently a challenge in the field of cryo-electron micrographs. This paper combines the characteristics of the MixUp hybrid enhancement algorithm, enhances the image feature information in the pre-processing stage, builds a feature perception network based on the channel self-attention mechanism in the forward network of the Swin Transformer model network, achieving adaptive adjustment of self-attention mechanism between different single particles, increasing the network's tolerance to noise, Incorporating PReLU activation function to enhance information exchange between pixel blocks of different single particles, and combining the Cross-Entropy function with the softmax function to construct a classification network based on Swin Transformer suitable for cryo-electron micrograph single particle detection model (Swin-cryoEM), achieving mixed detection of multiple types of single particles. Swin-cryoEM algorithm can better solve the problem of good adaptability in picking single particles of many types of cryo-electron micrographs, improve the accuracy and generalization ability of the single particle picking method, and provide high-quality data support for the three-dimensional reconstruction of a single particle. In this paper, ablation experiments and comparison experiments were designed to evaluate and compare Swin-cryoEM algorithms in detail and comprehensively on multiple datasets. The Average Precision is an important evaluation index of the evaluation model, and the optimal Average Precision reached 95.5% in the training stage Swin-cryoEM, and the single particle picking performance was also superior in the prediction stage. This model inherits the advantages of the Swin Transformer detection model and is superior to mainstream models such as Faster R-CNN and YOLOv5 in terms of the single particle detection capability of cryo-electron micrographs.

Structural analyses of the GI.4 norovirus by cryo-electron microscopy and X-ray crystallography revealing binding sites for human monoclonal antibodies.

Noroviruses are major causative agents of acute nonbacterial gastroenteritis in humans. There are neither antiviral therapeutic agents nor vaccines for noroviruses at this time. To evaluate the potential usefulness of two previously isolated human monoclonal antibody fragments, CV-1A1 and CV-2F5, we first conducted a single-particle analysis to determine the cryo-electron microscopy structure of virus-like particles (VLPs) from the genogroup I genotype 4 (GI.4) Chiba strain uniformly coated with CV-1A1 fragments. The results revealed that the GI.4-specific CV-1A1 antibody bound to the P2 subdomain, in which amino acids are less conserved and variable. Interestingly, a part of the CV-1A1 intrudes into the histo-blood group antigen-binding site, suggesting that this antibody might exert neutralizing activity. Next, we determined the crystal structure of the protruding (P) domain of the capsid protein in the complex form with the CV-2F5 antibody fragment. Consistent with the cross-reactivity, the CV-2F5 bound to the P1 subdomain, which is rich in amino acids conserved among the GI strains, and moreover induced a disruption of Chiba VLPs. These results suggest that the broadly reactive CV-2F5 antibody can be used as both a universal detection reagent and an antiviral drug for GI noroviruses. IMPORTANCE: We conducted the structural analyses of the VP1 protein from the GI.4 Chiba norovirus to identify the binding sites of the previously isolated human monoclonal antibodies CV-1A1 and CV-2F5. The cryo-electron microscopy of the Chiba virus-like particles (VLPs) complexed with the Fv-clasp forms of GI.4-specific CV-1A1 revealed that this antibody binds to the highly variable P2 subdomain, suggesting that this antibody may have neutralizing ability against the GI.4 strains. X-ray crystallography revealed that the CV-2F5 antibody bound to the P1 subdomain, which is rich in conserved amino acids. This result is consistent with the ability of the CV-2F5 antibody to react with a wide variety of GI norovirus strains. It is also found that the CV-2F5 antibody caused a disruption of VLPs. Our findings, together with previous reports on the structures of VP1 proteins and VLPs, are expected to open a path for the structure-based development of antivirals and vaccines against norovirus disease.

New insights into the structure and dynamics of the TOM complex in mitochondria.

To date, there is no general physical model of the mechanism by which unfolded polypeptide chains with different properties are imported into the mitochondria. At the molecular level, it is still unclear how transit polypeptides approach, are captured by the protein translocation machinery in the outer mitochondrial membrane, and how they subsequently cross the entropic barrier of a protein translocation pore to enter the intermembrane space. This deficiency has been due to the lack of detailed structural and dynamic information about the membrane pores. In this review, we focus on the recently determined sub-nanometer cryo-EM structures and our current knowledge of the dynamics of the mitochondrial two-pore outer membrane protein translocation machinery (TOM core complex), which provide a starting point for addressing the above questions. Of particular interest are recent discoveries showing that the TOM core complex can act as a mechanosensor, where the pores close as a result of interaction with membrane-proximal structures. We highlight unusual and new correlations between the structural elements of the TOM complexes and their dynamic behavior in the membrane environment.

Exploring GPCR conformational dynamics using single-molecule fluorescence.

G protein-coupled receptors (GPCRs) are membrane proteins that transmit specific external stimuli into cells by changing their conformation. This conformational change allows them to couple and activate G-proteins to initiate signal transduction. A critical challenge in studying and inferring these structural dynamics arises from the complexity of the cellular environment, including the presence of various endogenous factors. Due to the recent advances in cell-expression systems, membrane-protein purification techniques, and labeling approaches, it is now possible to study the structural dynamics of GPCRs at a single-molecule level both in vitro and in live cells. In this review, we discuss state-of-the-art techniques and strategies for expressing, purifying, and labeling GPCRs in the context of single-molecule research. We also highlight four recent studies that demonstrate the applications of single-molecule microscopy in revealing the dynamics of GPCRs. These techniques are also useful as complementary methods to verify the results obtained from other structural biology tools like cryo-electron microscopy and x-ray crystallography.

Confronting heterogeneity in cryogenic electron microscopy data: Innovative strategies and future perspectives with data-driven methods.

The surge in the influx of data from cryogenic electron microscopy (cryo-EM) experiments has intensified the demand for robust algorithms capable of autonomously managing structurally heterogeneous datasets. This presents a wealth of exciting opportunities from a data science viewpoint, inspiring the development of numerous innovative, application-specific methods, many of which leverage contemporary data-driven techniques. However, addressing the challenges posed by heterogeneous datasets remains a paramount yet unresolved issue in the field. Here, we explore the subtleties of this challenge and the array of strategies devised to confront it. We pinpoint the shortcomings of existing methodologies and deliberate on prospective avenues for improvement. Specifically, our discussion focuses on strategies to mitigate model overfitting and manage data noise, as well as the effects of constraints, priors, and invariances on the optimization process.

Graphene in cryo-EM specimen optimization.

Specimen preparation is a critical but challenging step in high-resolution cryogenic electron microscopy (cryo-EM) structural analysis of macromolecules. In the past decade, graphene has gained much recognition as the supporting substrate to optimize cryo-EM specimen preparation. It improves macromolecule embedding in ice, reduces beam-induced motion, while imposing negligible background noise. Various types of graphene-coated cryo-EM grids were implemented to improve the robustness and efficiency of specimen preparation. Graphene functionalization by different means has been proved specifically useful in addressing challenges related to the air-water interface (AWI), such as preferential orientation and sample denaturation. Graphene sandwich specimen preparation sets a new direction to explore in cryo-EM analysis of biological specimens. In this review, we discuss the current challenges and future prospects of graphene application in cryo-EM analysis of macromolecules.

Bridging structural and cell biology with cryo-electron microscopy.

Most life scientists would agree that understanding how cellular processes work requires structural knowledge about the macromolecules involved. For example, deciphering the double-helical nature of DNA revealed essential aspects of how genetic information is stored, copied and repaired. Yet, being reductionist in nature, structural biology requires the purification of large amounts of macromolecules, often trimmed off larger functional units. The advent of cryogenic electron microscopy (cryo-EM) greatly facilitated the study of large, functional complexes and generally of samples that are hard to express, purify and/or crystallize. Nevertheless, cryo-EM still requires purification and thus visualization outside of the natural context in which macromolecules operate and coexist. Conversely, cell biologists have been imaging cells using a number of fast-evolving techniques that keep expanding their spatial and temporal reach, but always far from the resolution at which chemistry can be understood. Thus, structural and cell biology provide complementary, yet unconnected visions of the inner workings of cells. Here we discuss how the interplay between cryo-EM and cryo-electron tomography, as a connecting bridge to visualize macromolecules in situ, holds great promise to create comprehensive structural depictions of macromolecules as they interact in complex mixtures or, ultimately, inside the cell itself.

Ghostbuster: A phase retrieval diffraction tomography algorithm for cryo-EM.

Ewald sphere curvature correction, which extends beyond the projection approximation, stretches the shallow depth of field in cryo-EM reconstructions of thick particles. Here we show that even for previously assumed thin particles, reconstruction artifacts which we refer to as ghosts can appear. By retrieving the lost phases of the electron exitwaves and accounting for the first Born approximation scattering within the particle, we show that these ghosts can be effectively eliminated. Our simulations demonstrate how such ghostbusting can improve reconstructions as compared to existing state-of-the-art software. Like ptychographic cryo-EM, our Ghostbuster algorithm uses phase retrieval to improve reconstructions, but unlike the former, we do not need to modify the existing data acquisition pipelines.