Reconstruction principles of icosahedral virus structure determination using electron cryomicroscopy

Virus structures are megadalton nucleoprotein complexes with an exceptional variety of protein-protein and protein-nucleic-acid interactions. Three-dimensional crystal structures of over 70 virus capsids, from more than 20 families and 30 different genera of viruses, have been solved to near-atomic resolution. The enormous amount of information contained in these structures is difficult to access, even for scientists trained in structural biology. Virus Particle Explorer (VIPER) is a web-based catalogue of structural information that describes the icosahedral virus particles. In addition to high-resolution crystal structures, VIPER has expanded to include virus structures obtained by cryo-electron microscopy (EM) techniques. The VIPER database is a powerful resource for virologists, microbiologists, virus crystallographers and EM researchers. This review describes how to use VIPER, using several examples to show the power of this resource for research and educational purposes.

Volta phase plate data collection facilitates image processing and cryo-EM structure determination.

Cryo-electron microscopy (cryo-EM), the structural analysis of samples embedded in vitreous ice, is a powerful approach for determining three-dimensional (3D) structures of biological specimens. Over the past two decades, this technique has been used to successfully calculate subnanometer (<10 Å) resolution and, in some cases, near-atomic resolution structures of highly symmetrical and stable complexes such as icosahedral viruses and ribosomes, as well as samples that form ordered two-dimensional or helical arrays. However, determining high-resolution 3D structures of smaller, less symmetrical, and dynamic samples remains a significant challenge. The recent development of electron microscopes with automated data collection capabilities and robust direct electron detection cameras, as well as new powerful image processing algorithms, has dramatically expanded the number of biological macromolecules amenable for study using cryo-EM. In addition, these new technological and computational developments have been used to successfully determine <5 Å resolution 3D structures of samples, such as membrane proteins and complexes with either low or no symmetry, that traditionally were not considered promising candidates for high-resolution cryo-EM. With these exciting new advances, cryo-EM is now on pace to determine atomic resolution 3D structures.

Proteins are the molecules carry out the vital functions and make more than the half of dry weight in every cell. Protein in nature folds into a unique and energetically favorable 3-Dimensional (3-D) structure which is critical and unique to its biological function. In contrast to other methods for protein structure determination, Electron Cryo-rricroscopy (CryoEM) is able to produce volumetric maps of proteins that are poorly soluble, large and hard to crystallize. Furthermore, it studies the proteins in their native environment. Unfortunately, the volumetric maps generated by current advances in CryoEM technique produces protein maps at medium resolution about (~5 to 10A) in which it is hard to determine the atomic-structure of the protein. However, the resolution of the volumetric maps is improving steadily, and recent works could obtain atomic models at higher resolutions (~3A). De novo protein modeling is the process of building the structure of the protein using its CryoEM volumetric map. Thereupon, the volumetric maps at medium resolution generated by CryoEM technique proposed a new challenge. At the medium resolution, the location and orientation of secondary structure elements (SSE) can be visually and computationally identified. However, the order and direction (called protein topology) of the SSEs detected from the CryoEM volumetric map are not visible. In order to determine the protein structure, the topology of the SSEs has to be figured out and then the backbone can be built. Consequently, the topology problem has become a bottle neck for protein modeling using CryoEM In this dissertation, we focus to establish an effective computational framework to derive the atomic structure of a protein from the medium resolution CryoEM volumetric maps. This framework includes a topology graph component to rank effectively the topologies of the SSEs and a model building component. In order to generate the small subset of candidate topologies, the problem is translated into a layered graph representation. We developed a dynamic programming algorithm (TopoDP) for the new representation to overcome the problem of large search space. Our approach shows the improved accuracy, speed and memory use when compared with existing methods. However, the generating of such set was infeasible using a brute force method. Therefore, the topology graph component effectively reduces the topological space using the geometrical features of the secondary structures through a constrained K-shortest paths method in our layered graph. The model building component involves the bending of a helix and the loop construction using skeleton of the volumetric map. The forward-backward CCD is applied to bend the helices and model the loops.

Relationships between RNA topology and nucleocapsid structure in a model icosahedral virus

High-resolution structures of membrane and soluble proteins can be obtained by cryo-electron crystallography, given highly-ordered cryo-preparations of perfectly flat 2D crystals are available. Studies of membrane proteins, which are embedded into a lipid membrane, mimicking the native cell membrane, are of particular biological interest. However there are multiple reasons why electron crystallography is far from being a mainstream protein structure determination technique. In this thesis we address three major difficulties of electron crystallography: (i) resolution loss due to not perfectly flat crystals, (ii) reliable high-throughput automatic image processing and (iii) correction of electron beam-induced motion of the sample.&#13;\n&#13;\nThe conventional electron crystallography image processing procedure assumes perfectly flat 2D crystals, which are almost impossible to obtain. Our new processing approach, described in Chapter 2, weakens this assumption tremendously. Traditional processing assigns the same tilt geometry to all proteins of one 2D crystal. Thus local tilt geometry variations, due to not perfectly flat crystals, are neglected. We developed an algorithm that optimizes the tilt geometry of each protein separately, while exploiting the correlation between neighboring proteins. The new method proves the feasibility of this approach, improves the achieved resolution and opens the doors to new studies, i.e. structural studies of membrane proteins embedded into lipid vesicles.&#13;\n&#13;\nRecently a new generation of digital detectors tremendously changed the cryo-electron microscopy field. Beside a significantly increased signal-to-noise ratio these detectors record dose-fractionated movies of the sample under the electron beam. Previous cameras only recorded one image instead. This new exposure mode enables the computational correction for beam-induced sample movements. In Chapter 3 we describe a new “real-time” automation pipeline for electron crystallography using direct electron detectors. The novel pipeline automatically corrects for homogeneous sample drift on frame level and processes the acquired images automatically. Both, the time-to-solution and the quality of the obtained 3D reconstructions are significantly improved.&#13;\n&#13;\nHeterogeneous beam-induced sample movements are the most severe resolution- limiting factor in modern cryo-electron crystallography. In Chapter 4 we present an algorithm, termed movie-mode unbending, which corrects for inhomogeneous beam- induced sample drift. In contrast to the previous homogeneous drift-correction, the novel algorithm can correct for locally varying beam-induced sample motion. This novel approach significantly increases the resolution of electron cryo-crystallographic studies recorded on the latest detectors.&#13;\n&#13;\nChapter 5 covers multiple successful applications of the methods introduced above. For instance the real-time drift-correction module of the developed automation pipeline was used for multiple near-atomic resolution single particle and helical image processing projects. The high-throughput crystal image processing was applied to different kinds of 2D crystals and enabled the qualitative assessment of different sample preparation methods. Additionally we describe the implementation of a high-throughput single particle automation pipeline, which will enable the generation of near-atomic resolution single particle cryo-electron microscopy density maps on a daily basis.

Single particle analysis integrated with microscopy: A high-throughput approach for reconstructing icosahedral particles

Segmentation of icosahedral virus density map from cryo-electron microscope (CryoEM) is a challenging task because virus structure is complex and density map is at low resolution. Fast marching method is widely used in segmentation, in which seed selection is essential for correct segmentation results. However, the selection of an appropriate seed is difficult. In this paper, we present the method of selecting the seed in fast marching algorithm by making use of the shape symmetry to improve the fast marching method for icosahedral virus segmentation. Based on the feature of icosahedron, we compute and get its symmetry axes inside the density map. With these symmetry axes, we specify the initial seeds with the local maxima value along symmetry axes. Further, the new data structures are presented, which can effectively reduce the memory cost when implement the fast marching algorithm. Experimental results show that the approach can obtain segmentation results of the density maps fast and accurately.

Cryo-EM structure and functional analyses show that human iron exporter ferroportin (Fpn) can bind and transport calcium ions and the transport activity is reduced when the concentration of ferrous ions is high.

Cryo-electron microscopy (cryo-EM) has transformed structural biology by enabling the determination of macromolecular structures at near-atomic resolution. The recent Nobel Prizes awarded to John Hopfield and Geoffrey Hinton in Physics, and to David Baker, Demis Hassabis, and John Jumper in Chemistry, highlight the growing convergence of artificial intelligence (AI) and computational innovation in advancing scientific discovery. Cryo-EM has emerged as a powerful tool for elucidating the architectures and dynamics of complex biological assemblies. Its ability to capture multiple conformational states provides mechanistic insights that are critical for understanding molecular function. However, traditional cryo-EM data processing pipelines face challenges in handling large, heterogeneous datasets. Manual particle selection, extensive computational demands, and uneven sampling of conformational states often limit resolution, especially for rare or transient conformations. Recent advances in AI-driven methods are reshaping this landscape. Deep neural networks now facilitate automated particle picking, denoising, classification, and three-dimensional reconstruction, substantially reducing human bias and computational bottlenecks. Machine learning models have also enhanced the accuracy of map interpretation and atomic modeling, accelerating the transition from raw micrographs to high-resolution structures. These developments represent a paradigm shift, positioning AI as an integral component of the “resolution revolution” in structural biology. This comprehensive article examines the state of the art in cryo-electron microscopy, highlighting how artificial intelligence is redefining every stage of the workflow-from automated particle picking and map reconstruction to structure modeling and high-resolution refinement. The integration of deep learning and neural network-based tools has transformed cryo-EM into a data-driven and highly precise discovery platform. By showcasing these innovations, we emphasize AI’s pivotal role in advancing next-generation cryo-EM research and accelerating molecular insights with profound implications for structural biology and drug discovery. Not applicable.

The cryo-EM structure of ETB-Gi complex, obtained using the Fusion-G system, provides insights into the distinct activation mechanism and G-protein promiscuity of ETB, aiding in the design of ETB agonists for therapeutic applications.

In modern structural biology, there are three major methods for structural biologists to obtain structural information of macromolecules: cryo-electron microscopy (cryo-EM), nuclear magnetic resonance (NMR), and X-ray crystallography. Cryo-EM, in comparison with the other two methods, allows structural biologists to obtain the structures of various macromolecules in a more native and less perturbed system. Over the past decade, cryo-EM has enabled scientists to determine the structures of protein complexes at atomic resolution and made a profound impact in molecular bioscience and pharmaceutical sectors. Along with cryo-EM, another emerging technique called cryo-electron tomography (cryo-ET) has gained increasing importance in structural biology. It has the potential to visualize macromolecular complexes and assemblies in their native environments at high resolution, but there are still some challenges for small, sparse subjects and in approaching atomic resolution in situ. This chapter summarizes the major steps involved in structure determination using cryo-EM and cryo-ET and highlights the major challenges for in situ cryo-ET. We also present a few examples of near-atomic resolution structure determination of macromolecular assemblies both in purified systems in vitro and in native contexts in situ. Future perspectives are discussed as well.

Structures of macromolecules in their native state provide unique unambiguous insights into their functions. Cryo-electron tomography combined with subtomogram averaging demonstrated the power to solve such structures in situ at resolutions in the range of 3 Angstrom for some macromolecules. In order to be applicable to the structural determination of the majority of macromolecules observable in cells in limited amounts, processing of tomographic data has to be performed in a high-throughput manner. Here we present TomoBEAR—a modular configurable workflow engine for streamlined processing of cryo-electron tomographic data for subtomogram averaging. TomoBEAR combines commonly used cryo-EM packages with reasonable presets to provide a transparent (“white box”) approach for data management and processing. We demonstrate applications of TomoBEAR to two data sets of purified macromolecular targets, to an ion channel RyR1 in a membrane, and the tomograms of plasma FIB-milled lamellae and demonstrate the ability to produce high-resolution structures. TomoBEAR speeds up data processing, minimizes human interventions, and will help accelerate the adoption of in situ structural biology by cryo-ET. The source code and the documentation are freely available.

Role of artificial intelligence in advancing cryo electron microscopy.

Metal-organic frameworks (MOFs) have attracted considerable interest due to their well-defined pore architecture and structural tunability on molecular dimensions. While single-crystal X-ray diffraction (SCXRD) has been widely used to elucidate the structures of MOFs at the atomic scale, the formation of large and well-ordered crystals is still a crucial bottleneck for structure determination. To alleviate this challenge, three-dimensional electron diffraction (3D ED) has been developed for structure determination of nano- (submicron-)sized crystals. Such 3D ED data are collected from each single crystal using transmission electron microscopy. In this protocol, we introduce the entire workflow for structural analysis of MOFs by 3D ED, from sample preparation, data acquisition and data processing to structure determination. We describe methods for crystal screening and handling of crystal agglomerates, which are crucial steps in sample preparation for single-crystal 3D ED data collection. We further present how to set up a transmission electron microscope for 3D ED data acquisition and, more importantly, offer suggestions for the optimization of data acquisition conditions. For data processing, including unit cell and space group determination, and intensity integration, we provide guidelines on how to use electron and X-ray crystallography software to process 3D ED data. Finally, we present structure determination from 3D ED data and discuss the important features associated with 3D ED data that need to be considered. We believe that this protocol provides critical details for implementing and utilizing 3D ED as a structure determination platform for nano- (submicron-)sized MOFs as well as other crystalline materials.