Electron Microscopy Data Bank Research Articles

Refinement of cryo-EM 3D maps with a self-supervised denoising model: crefDenoiser.

Cryogenic electron microscopy (cryo-EM) is a pivotal technique for imaging macromolecular structures. However, despite extensive processing of large image sets collected in cryo-EM experiments to amplify the signal-to-noise ratio, the reconstructed 3D protein-density maps are often limited in quality due to residual noise, which in turn affects the accuracy of the macromolecular representation. Here, crefDenoiser is introduced, a denoising neural network model designed to enhance the signal in 3D cryo-EM maps produced with standard processing pipelines. The crefDenoiser model is trained without the need for `clean' ground-truth target maps. Instead, a custom dataset is employed, composed of real noisy protein half-maps sourced from the Electron Microscopy Data Bank repository. Competing with the current state-of-the-art, crefDenoiser is designed to optimize for the theoretical noise-free map during self-supervised training. We demonstrate that our model successfully amplifies the signal across a wide variety of protein maps, outperforming a classic map denoiser and following a network-based sharpening model. Without biasing the map, the proposed denoising method leads to improved visibility of protein structural features, including protein domains, secondary structure elements and modest high-resolution feature restoration.

Data-driven regularization lowers the size barrier of cryo-EM structure determination

Macromolecular structure determination by electron cryo-microscopy (cryo-EM) is limited by the alignment of noisy images of individual particles. Because smaller particles have weaker signals, alignment errors impose size limitations on its applicability. Here, we explore how image alignment is improved by the application of deep learning to exploit prior knowledge about biological macromolecular structures that would otherwise be difficult to express mathematically. We train a denoising convolutional neural network on pairs of half-set reconstructions from the electron microscopy data bank (EMDB) and use this denoiser as an alternative to a commonly used smoothness prior. We demonstrate that this approach, which we call Blush regularization, yields better reconstructions than do existing algorithms, in particular for data with low signal-to-noise ratios. The reconstruction of a protein–nucleic acid complex with a molecular weight of 40 kDa, which was previously intractable, illustrates that denoising neural networks will expand the applicability of cryo-EM structure determination for a wide range of biological macromolecules.

RNA as a component of scrapie fibrils

Recently, electron cryo-microscopy (cryo-EM) maps of fibrils from the brains of mice and hamsters with five infectious scrapie strains have been published and deposited in the electron microscopy data bank (EMDB). As noted by the primary authors, the fibrils contain a second component other than protein. The aim of the present study was to identify the nature of this second component in the published maps using an in silico approach. Extra densities (EDs) containing this component were continuous, straight, axial, at right angles to protein rungs and within hydrogen-bonding distance of protein, consistent with a structural role. EDs co-located with strips of basic residues, notably lysines, and formed a conspicuous cladding over parts of the N-terminal lobe of the protein. A Y-shaped polymer consistent with RNA was found, in places forming a single chain and at one location forming a duplex, comprising two antiparallel chains, and raising the intriguing possibility of replicative behaviour. To reflect the monotonous nature of the protein interface, it is suggested that the RNA may be a short tandem repeat. Fibrils from brains of patients with Alzheimer’s disease, Parkinson’s disease, amyotrophic lateral sclerosis and other neurodegenerations also contain EDs and may be of a similar aetiology.

Determining Macromolecular Structures Using Cryo-Electron Microscopy.

Structural insights into macromolecular and protein complexes provide key clues about the molecular basis of the function. Cryogenic electron microscopy (cryo-EM) has emerged as a powerful structural biology method for studying protein and macromolecular structures at high resolution in both native andnear-native states. Despite the ability to get detailed structural insights into the processes underlying protein function using cryo-EM, there has been hesitancy amongst plant biologists to apply the method for biomolecular interaction studies. This is largely evident from the relatively fewer structural depositions of proteins and protein complexes from plant origin in electron microscopy databank. Even though the progress has been slow, cryo-EM has significantly contributed to our understanding of the molecular biology processes underlying photosynthesis, energy transfer in plants, besides viruses infecting plants. This chapter introduces sample preparation for both negative-staining electron microscopy (NSEM) and cryo-EM for plant proteins and macromolecular complexes and data analysis using single particle analysis for beginners.

Structural Modeling of Biological Entities, Such as Viruses, At Atomic Resolution

The structural modeling of biological entities, particularly viruses, at the atomic level is a noteworthy pursuit in the field of molecular biology. This endeavor necessitates the application of advanced computational techniques to elucidate the three-dimensional structures of these entities at the molecular level, thereby providing insights into their functioning and interactions with other biological components. The results of such studies hold immense potential for enhancing our understanding of the fundamental mechanisms governing biological processes and informing the development of novel therapeutic strategies. The achievements in question were realized through the utilization of UCSF Chimera, a software program for molecular visualization. These accomplishments include the investigation of T bacteriophages and other bacterial infection models. To attain this level of detail, X-ray structures and cryo-EM maps sourced from the RCSB (Research Collaboratory for Structural Bioinformatics) and EMDB (Electron Microscopy Data Bank) databanks were employed in conjunction with a high-performance computer to construct structures comprising millions of atoms.

EMDB-the Electron Microscopy Data Bank.

The Electron Microscopy Data Bank (EMDB) is the global public archive of three-dimensional electron microscopy (3DEM) maps of biological specimens derived from transmission electron microscopy experiments. As of 2021, EMDB is managed by the Worldwide Protein Data Bank consortium (wwPDB; wwpdb.org) as a wwPDB Core Archive, and the EMDB team is a core member of the consortium. Today, EMDB houses over 30000 entries with maps containing macromolecules, complexes, viruses, organelles and cells. Herein, we provide an overview of the rapidly growing EMDB archive, including its current holdings, recent updates, and future plans.

DAQ-score database: Deep-learning based quality estimation of cryo-EM derived protein models

An increasing number of protein structures are determined by cryo-electron microscopy (cryo-EM) as cryo-EM has become one of the most important methods to determine structures. On the other hand, it has been noticed that errors occur in the model building process from cryo-EM maps, probably more frequently than one might think, particularly when the map resolution is not very high. Thus, establishing quality assessment methods has become a crucial and urgent task for biomolecular structure determination with cryo-EM. We have recently developed a quality assessment method to detect protein structural model outliers using machine learning techniques. Our method, called DAQ (Deep-learning-based Amino acid-wise model Quality) score, uses deep neural network to capture local density features of amino acids and atoms in proteins and assesses the likelihood that modeled residues in a structural model is correct (Terashi et al., Nature Methods, 2022). DAQ is also able to detect not only errors in conformations but also shifts in sequence assignment to otherwise correct main-chain conformations, which is often not easy to detect by checking density fitting. Here, we performed a PDB-scale model analysis by DAQ. We applied DAQ to around 10,000 protein structure models in PDB that were derived from cryo-EM maps deposited in Electron Microscopy Data Bank (EMDB). We report the tendency of common errors made in the models through the large-scale analysis. When authors deposited updated structure models to PDB over an initial model, we see clear improvement of DAQ score in the updated version of the model. A common type of errors observed include sequence shifts along alpha helices. Model assessment results with DAQ are made available in a database (https://daqdb.bio.purdue.edu/), where models can be searched by PDB IDs, EMDB IDs and keywords.

EMPIAR: the Electron Microscopy Public Image Archive.

Public archiving in structural biology is well established with the Protein Data Bank (PDB; wwPDB.org) catering for atomic models and the Electron Microscopy Data Bank (EMDB; emdb-empiar.org) for 3D reconstructions from cryo-EM experiments. Even before the recent rapid growth in cryo-EM, there was an expressed community need for a public archive of image data from cryo-EM experiments for validation, software development, testing and training. Concomitantly, the proliferation of 3D imaging techniques for cells, tissues and organisms using volume EM (vEM) and X-ray tomography (XT) led to calls from these communities to publicly archive such data as well. EMPIAR (empiar.org) was developed as a public archive for raw cryo-EM image data and for 3D reconstructions from vEM and XT experiments and now comprises over a thousand entries totalling over 2 petabytes of data. EMPIAR resources include a deposition system, entry pages, facilities to search, visualize and download datasets, and a REST API for programmatic access to entry metadata. The success of EMPIAR also poses significant challenges for the future in dealing with the very fast growth in the volume of data and in enhancing its reusability.

Integrating model simulation tools and cryo‐electron microscopy

AbstractThe power of computer simulations, including machine‐learning, has become an inseparable part of scientific analysis of biological data. This has significantly impacted the field of cryogenic electron microscopy (cryo‐EM), which has grown dramatically since the “resolution‐revolution.” Many maps are now solved at 3–4 Å or better resolution, although a significant proportion of maps deposited in the Electron Microscopy Data Bank are still at lower resolution, where the positions of atoms cannot be determined unambiguously. Additionally, cryo‐EM maps are often characterized by a varying local resolution, partly due to conformational heterogeneity of the imaged molecule. To address such problems, many computational methods have been developed for cryo‐EM map reconstruction and atomistic model building. Here, we review the development in algorithms and tools for building models in cryo‐EM maps at different resolutions. We describe methods for model building, including rigid and flexible fitting of known models, model validation, small‐molecule fitting, and model visualization. We provide examples of how these methods have been used to elucidate the structure and function of dynamic macromolecular machines.This article is categorized under: Structure and Mechanism > Molecular Structures Structure and Mechanism > Computational Biochemistry and Biophysics Software > Molecular Modeling

Automatic determination of the handedness of single-particle maps of macromolecules solved by CryoEM

Single-Particle Analysis by Cryo-Electron Microscopy is a well-established technique to elucidate the three-dimensional (3D) structure of biological macromolecules. The orientation of the acquired projection images must be initially estimated without any reference to the final structure. In this step, algorithms may find a mirrored version of all the orientations resulting in a mirrored 3D map. It is as compatible with the acquired images as its unmirrored version from the image processing point of view, only that it is not biologically plausible.In this article, we introduce HaPi (Handedness Pipeline), the first method to automatically determine the hand of electron density maps of macromolecules solved by CryoEM. HaPi is built by training two 3D convolutional neural networks. The first determines α-helices in a map, and the second determines whether the α-helix is left-handed or right-handed. A consensus strategy defines the overall map hand. The pipeline is trained on simulated and experimental data. The handedness can be detected only for maps whose resolution is better than 5 Å. HaPi can identify the hand in 89% of new simulated maps correctly. Moreover, we evaluated all the maps deposited at the Electron Microscopy Data Bank and 11 structures uploaded with the incorrect hand were identified.

Protein Structural Modeling for Electron Microscopy Maps Using VESPER and MAINMAST.

An increasing number of protein structures are determined by cryo‐electron microscopy (cryo‐EM) and stored in the Electron Microscopy Data Bank (EMDB). To interpret determined cryo‐EM maps, several methods have been developed that model the tertiary structure of biomolecules, particularly proteins. Here we show how to use two such methods, VESPER and MAINMAST, which were developed in our group. VESPER is a method mainly for two purposes: fitting protein structure models into an EM map and aligning two EM maps locally or globally to capture their similarity. VESPER represents each EM map as a set of vectors pointing toward denser points. By considering matching the directions of vectors, in general, VESPER aligns maps better than conventional methods that only consider local densities of maps. MAINMAST is a de novo protein modeling tool designed for EM maps with resolution of 3–5 Å or better. MAINMAST builds a protein main chain directly from a density map by tracing dense points in an EM map and connecting them using a tree‐graph structure. This article describes how to use these two tools using three illustrative modeling examples. © 2022 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Protein structure model fitting using VESPER Alternate Protocol: Atomic model fitting using VESPER web server Basic Protocol 2: Protein de novo modeling using MAINMAST

Validation analysis of EMDB entries.

The Electron Microscopy Data Bank (EMDB) is the central archive of the electron cryo-microscopy (cryo-EM) community for storing and disseminating volume maps and tomograms. With input from the community, EMDB has developed new resources for the validation of cryo-EM structures, focusing on the quality of the volume data alone and that of the fit of any models, themselves archived in the Protein Data Bank (PDB), to the volume data. Based on recommendations from community experts, the validation resources are developed in a three-tiered system. Tier 1 covers an extensive and evolving set of validation metrics, including tried and tested metrics as well as more experimental ones, which are calculated for all EMDB entries and presented in the Validation Analysis (VA) web resource. This system is particularly useful for cryo-EM experts, both to validate individual structures and to assess the utility of new validation metrics. Tier 2 comprises a subset of the validation metrics covered by the VA resource that have been subjected to extensive testing and are considered to be useful for specialists as well as nonspecialists. These metrics are presented on the entry-specific web pages for the entire archive on the EMDB website. As more experience is gained with the metrics included in the VA resource, it is expected that consensus will emerge in the community regarding a subset that is suitable for inclusion in the tier 2 system. Tier 3, finally, consists of the validation reports and servers that are produced by the Worldwide Protein Data Bank (wwPDB) Consortium. Successful metrics from tier 2 will be proposed for inclusion in the wwPDB validation pipeline and reports. The details of the new resource are described, with an emphasis on the tier 1 system. The output of all three tiers is publicly available, either through the EMDB website (tiers 1 and 2) or through the wwPDB ftp sites (tier 3), although the content of all three will evolve over time (fastest for tier 1 and slowest for tier 3). It is our hope that these validation resources will help the cryo-EM community to obtain a better understanding of the quality and of the best ways to assess the quality of cryo-EM structures in EMDB and PDB.

Three-dimensional Structure Databases of Biological Macromolecules.

Databases of three-dimensional structures of proteins (and their associated molecules) provide: (a) Curated repositories of coordinates of experimentally determined structures, including extensive metadata; for instance information about provenance, details about data collection and interpretation, and validation of results. (b) Information-retrieval tools to allow searching to identify entries of interest and provide access to them. (c) Links among databases, especially to databases of amino-acid and genetic sequences, and of protein function; and links to software for analysis of amino-acid sequence and protein structure, and for structure prediction. (d) Collections of predicted three-dimensional structures of proteins. These will become more and more important after the breakthrough in structure prediction achieved by AlphaFold2. The single global archive of experimentally determined biomacromolecular structures is the Protein Data Bank (PDB). It is managed by wwPDB, a consortium of five partner institutions: the Protein Data Bank in Europe (PDBe), the Research Collaboratory for Structural Bioinformatics (RCSB), the Protein Data Bank Japan (PDBj), the BioMagResBank (BMRB), and the Electron Microscopy Data Bank (EMDB). In addition to jointly managing the PDB repository, the individual wwPDB partners offer many tools for analysis of protein and nucleic acid structures and their complexes, including providing computer-graphic representations. Their collective and individual websites serve as hubs of the community of structural biologists, offering newsletters, reports from Task Forces, training courses, and "helpdesks," as well as links to external software.Many specialized projects are based on the information contained in the PDB. Especially important are SCOP, CATH, and ECOD, which present classifications of protein domains.

Detecting Protein and DNA/RNA Structures in Cryo-Em Maps of Intermediate Resolution using Deep Learning

An increasing number of density maps of macromolecular structures, including proteins and protein and DNA/RNA complexes, have been determined by cryo-electron microscopy (cryo-EM). Although lately maps at a near-atomic resolution are routinely reported, there are still substantial fractions of maps determined at intermediate or low resolutions, where extracting structure information is difficult. Of the structures deposited to the Electron Microscopy Data Bank (EMDB) between 2016 and 2019, over 50% were solved at intermediate resolution ranging between 5-10 Å, where detailed structures are not visible.

Using Curriculum Learning in Pattern Recognition of 3-dimensional Cryo-electron Microscopy Density Maps.

Although Cryo-electron microscopy (cryo-EM) has been successfully used to derive atomic structures for many proteins, it is still challenging to derive atomic structure when the resolution of cryo-EM density maps is in the medium range, e.g., 5-10 Å. Studies have attempted to utilize machine learning methods, especially deep neural networks to build predictive models for the detection of protein secondary structures from cryo-EM images, which ultimately helps to derive the atomic structure of proteins. However, the large variation in data quality makes it challenging to train a deep neural network with high prediction accuracy. Curriculum learning has been shown as an effective learning paradigm in machine learning. In this paper, we present a study using curriculum learning as a more effective way to utilize cryo-EM density maps with varying quality. We investigated three distinct training curricula that differ in whether/how images used for training in past are reused while the network was continually trained using new images. A total of 1,382 3-dimensional cryo-EM images were extracted from density maps of Electron Microscopy Data Bank in our study. Our results indicate learning with curriculum significantly improves the performance of the final trained network when the forgetting problem is properly addressed.

Cylindrical Similarity Measurement for Helices in Medium-Resolution Cryo-Electron Microscopy Density Maps.

Cryo-electron microscopy (cryo-EM) density maps at medium resolution (5-10 Å) reveal secondary structural features such as α-helices and β-sheets, but they lack the side chain details that would enable a direct structure determination. Among the more than 800 entries in the Electron Microscopy Data Bank (EMDB) of medium-resolution density maps that are associated with atomic models, a wide variety of similarities can be observed between maps and models. To validate such atomic models and to classify structural features, a local similarity criterion, the F1 score, is proposed and evaluated in this study. The F1 score is theoretically normalized to a range from zero to one, providing a local measure of cylindrical agreement between the density and atomic model of a helix. A systematic scan of 30,994 helices (among 3,247 protein chains modeled into medium-resolution density maps) reveals an actual range of observed F1 scores from 0.171 to 0.848, suggesting that the cylindrical fit of the current data is well stratified by the proposed measure. The best (highest) F1 scores tend to be associated with regions that exhibit high and spatially homogeneous local resolution (between 5 Å and 7.5 Å) in the helical density. The proposed F1 scores can be used as a discriminative classifier for validation studies and as a ranking criterion for cryo-EM density features in databases.

Energy landscape of domain motion in glutamate dehydrogenase deduced from cryo-electron microscopy.

Analysis of the conformational changes of protein is important to elucidate the mechanisms of protein motions correlating with their function. Here, we studied the spontaneous domain motion of unliganded glutamate dehydrogenase from Thermococcusprofundus using cryo-electron microscopy and proposed a novel method to construct free-energy landscape of protein conformations. Each subunit of the homo-hexameric enzyme comprises nucleotide-binding domain (NAD domain) and hexamer-forming core domain. A large active-site cleft is situated between the two domains and varies from open to close according to the motion of a NAD domain. A three-dimensional map reconstructed from all cryo-electron microscopy images displayed disordered volumes of NAD domains, suggesting that NAD domains in the collected images adopted various conformations in domain motion. Focused classifications on NAD domain of subunits provided several maps of possible conformations in domain motion. To deduce what kinds of conformations appeared in EM images, we developed a novel analysis method that describe the EM maps as a linear combination of representative conformations appearing in a 200-ns molecular dynamics simulation as reference. The analysis enabled us to estimate the appearance frequencies of the representative conformations, which illustrated a free-energy landscape in domain motion. In the open/close domain motion, two free-energy basins hindered the direct transformation from open to closed state. Structure models constructed for representative EM maps in classifications demonstrated the correlation between the energy landscape and conformations in domain motion. Based on the results, the domain motion in glutamate dehydrogenase and the analysis method to visualize conformational changes and free-energy landscape were discussed. DATABASE: The EM maps of the four conformations were deposited to Electron Microscopy Data Bank (EMDB) as accession codes EMD-9845 (open), EMD-9846 (half-open1), EMD-9847 (half-open2), and EMD-9848 (closed), respectively. In addition, the structural models built for the four conformations were deposited to the Protein Data Bank (PDB) as accession codes 6JN9 (open), 6JNA (half-open1), 6JNC (half-open2), and 6JND (closed), respectively.

Molecular Basis for Hormone Recognition and Activation of Corticotropin-Releasing Factor Receptors.

Corticotropin-releasing factor (CRF) and the three related peptides urocortins 1-3 (UCN1-UCN3) are endocrine hormones that control the stress responses by activating CRF1R and CRF2R, two members of class B G-protein-coupled receptors (GPCRs). Here, we present two cryoelectron microscopy (cryo-EM) structures of UCN1-bound CRF1R and CRF2R with the stimulatory G protein. In both structures, UCN1 adopts a single straight helix with its N terminus dipped into the receptor transmembrane bundle. Although the peptide-binding residues in CRF1R and CRF2R are different from other members of class B GPCRs, the residues involved in receptor activation and G protein coupling are conserved. In addition, both structures reveal bound cholesterol molecules to the receptor transmembrane helices. Our structures define the basis of ligand-binding specificity in the CRF receptor-hormone system, establish a common mechanism of class B GPCR activation and G protein coupling, and provide a paradigm for studying membrane protein-lipid interactions for class B GPCRs.

Evolving data standards for cryo-EM structures.

Electron cryo-microscopy (cryo-EM) is increasingly being used to determine 3D structures of a broad spectrum of biological specimens from molecules to cells. Anticipating this progress in the early 2000s, an international collaboration of scientists with expertise in both cryo-EM and structure data archiving was established (EMDataResource, previously known as EMDataBank). The major goals of the collaboration have been twofold: to develop the necessary infrastructure for archiving cryo-EM-derived density maps and models, and to promote development of cryo-EM structure validation standards. We describe how cryo-EM data archiving and validation have been developed and jointly coordinated for the Electron Microscopy Data Bank and Protein Data Bank archives over the past two decades, as well as the impact of evolving technology on data standards. Just as for X-ray crystallography and nuclear magnetic resonance, engaging the scientific community via workshops and challenging activities has played a central role in developing recommendations and requirements for the cryo-EM structure data archives.

VASEM: visual analytics system for electron microscopy data bank

Electron Microscopy Data Bank (EMDB) provides biological researchers with a platform for uploading and downloading data obtained mainly by electron microscopy. With the continuous expansion of the total amount of data stored in EMDB in recent years, its influence has also been continuously improved. In order to make it easier for domain scientists to make better use of these data, we analyzed the EMDB meta-information structure and developed a visual analytics system, called VASEM, to provide convenient data exploration, searching, and analysis tools. Domain scientists can use our system to interactively filter massive EMDB data into relatively small subset for analysis and obtain subset depiction from visualization results. A modified parallel coordinates plot suitable for large-scale multi-dimensional ranking data visualization is also proposed for EM data subset depiction tasks. We present two usage scenarios to describe how the proposed system achieves the design goals.