Using EM to Understand the Structure and Function of Molecular Machines

Cryo-electron microscopy (cryo-EM) has for a long time been a technique of choice for determining structure of large and flexible macromolecular complexes that were difficult to study by other experimental techniques such as X-ray crystallography or nuclear magnetic resonance. However, a fast development of instruments and software for cryo-EM in the last decade has allowed that a large range of complexes can be studied by cryo-EM, and that their structures can be obtained at near-atomic resolution, including the structures of small complexes (e.g., membrane proteins) whose size was earlier an obstacle to cryo-EM. Image analysis to identify multiple coexisting structures in the same specimen (multiconformation reconstruction) is now routinely done both to solve structures at near-atomic resolution and to study conformational dynamics. Methods for multiconformation reconstruction and latest examples of their applications are the focus of this review.

Structural studies of biocomplexes using single-particle cryo-electron microscopy (cryo-EM) is now a well-established technique in structural biology and has become competitive with X-ray crystallography. The latest advances in EM enable us to determine structures of protein complexes at 3-5 Å resolution for an extremely broad range of sizes from ~200 kDa up to hundreds of megadaltons (Bartesaghi et al., Science 348(6239):1147-1151, 2051; Bai et al., Nature 525(7568):212-217, 2015; Vinothkumar et al., Nature 515(7525):80-84, 2014; Grigorieff and Harrison, Curr Opin Struct Biol 21(2):265-273, 2011). The majority of biocomplexes comprise a number of different components and are not amenable to crystallisation. Secretion systems are typical examples of such multi-protein complexes, and structural studies of them are extremely challenging. The only feasible approach to revealing their spatial organisation and functional modification is cryo-EM. The development of systems for digital registration of images and algorithms for the fast and efficient processing of recorded images and subsequent analysis facilitated the determination of structures at near-atomic resolution. In this review we will describe sample preparation for cryo-EM, how data are collected by new detectors, and the logistics of image analysis through the basic steps required for reconstructions of both small and large biological complexes and their refinement to nearly atomic resolution. The processing workflow is illustrated using examples of EM analysis of a Type IV Secretion System.

BackgroundProteins in their majority act rarely as single entities. Multisubunit macromolecular complexes are the actors in most of the cellular processes. These nanomachines are hold together by weak protein-protein interactions and undergo functionally important conformational changes. TFIID is such a multiprotein complex acting in eukaryotic transcription initiation. This complex is first to be recruited to the promoter of the genes and triggers the formation of the transcription preinitiation complex involving RNA polymerase II which leads to gene transcription. The exact role of TFIID in this process is not yet understood.MethodsLast generation electron microscopes, improved data collection and new image analysis tools made it possible to obtain structural information of biological molecules at atomic resolution. Cryo-electron microscopy of vitrified samples visualizes proteins in a fully hydrated, close to native state. Molecular images are recorded at liquid nitrogen temperature in low electron dose conditions to reduce radiation damage. Digital image analysis of these noisy images aims at improving the signal-to-noise ratio, at separating distinct molecular views and at reconstructing a three-dimensional model of the biological particle.ResultsUsing these methods we showed the early events of an activated transcription initiation process. We explored the interaction of the TFIID coactivator with the yeast Rap1 activator, the transcription factor TFIIA and the promoter DNA. We demonstrated that TFIID serves as an assembly platform for transient protein-protein interactions, which are essential for transcription initiation.ConclusionsRecent developments in electron microscopy have provided new insights into the structural organization and the dynamic reorganization of large macromolecular complexes. Examples of near-atomic resolutions exist but the molecular flexibility of macromolecular complexes remains the limiting factor in most case. Electron microscopy has the potential to provide both structural and dynamic information of biological assemblies in order to understand the molecular mechanisms of their functions.

An increasing number of density maps of macromolecular structures, including proteins 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 not trivial. Here, we report a new computational method, Emap2sec+, which identifies DNA or RNA as well as the secondary structures of proteins in cryo-EM maps of 5 to 10 Å resolution. Emap2sec+ employs the deep Residual convolutional neural network. Emap2sec+ assigns structural labels with associated probabilities at each voxel in a cryo-EM map, which will help structure modeling in an EM map. Emap2sec+ showed stable and high assignment accuracy for nucleotides in low resolution maps and improved performance for protein secondary structure assignments than its earlier version when tested on simulated and experimental maps.

This abstract highlights the principles, applications, and impact of Cryo-EM on our understanding of biology and disease. Cryo-Electron Microscopy (Cryo-EM) is a revolutionary imaging technique that has transformed the field of structural biology. By rapidly freezing biological samples in a vitreous ice state, Cryo-EM enables the visualization of macromolecules, cells, and tissues at near-atomic resolution. This technique has overcome traditional limitations in electron microscopy, allowing for the study of biological specimens in their native, hydrated state. Cryo-EM has revealed intricate details of protein structures, elucidated mechanisms of cellular processes, and enabled the development of novel therapeutics. With its ability to image a wide range of biological samples, Cryo-EM has become an indispensable tool for researchers seeking to understand the intricacies of life at the molecular level. Cryo-Electron Microscopy (Cryo-EM) has revolutionized the field of structural biology, enabling researchers to visualize biological macromolecules, cells, and tissues at unprecedented resolution. By combining advanced cryogenic techniques, high-vacuum environments, and sophisticated electron optics, Cryo-EM has overcome traditional limitations in electron microscopy.

Cryo-electron microscopy (cryo-EM) and cryo-electron tomography (cryo-ET) have transformed structural biology by enabling the visualization of biological macromolecules at near-atomic resolution. Since its establishment in 2002, the Electron Microscopy Data Bank (EMDB) has played a pivotal role in this “Resolution Revolution,” serving as a central repository for cryo-EM data. Over the past decade, major advances in detector technology, image processing, and analytical methodologies have led to a surge in cryo-EM structure depositions. This study provides a comprehensive analysis of temporal trends in the EMDB, highlighting the growing number of entries, improvements in resolution, and the evolution of detector usage. It emphasizes the rise of single-particle analysis (SPA) as the dominant cryo-EM technique and examines how high-resolution detectors have enhanced data quality. Additionally, it explores resolution standards across leading scientific journals, underscoring the role of EMDB not only as a data archive but also as a catalyst for innovation in structural biology.

Human cytosolic leucyl-tRNA synthetase is one component of a macromolecular aminoacyl-tRNA synthetase complex. This is unlike prokaryotic and lower eukaryotic LeuRSs that exist as free soluble enzymes. There is little known about it, since the purified enzyme has been unavailable. Herein, human cytosolic leucyl-tRNA synthetase was heterologously expressed in a baculovirus system and purified to homogeneity. The molecular mass (135 kDa) of the enzyme is close to the theoretical value derived from its cDNA. The kinetic constants of the enzyme for ATP, leucine, and tRNA(Leu) in the ATP-PP(i) exchange and tRNA leucylation reactions were determined, and the results showed that it is quite active as a free enzyme. Human cytosolic leucyl-tRNA synthetase expressed in human 293 T cells localizes predominantly to the cytosol. Additionally, it is found to have a long C-terminal extension that is absent from bacterial and yeast LeuRSs. A C-terminal 89-amino acid truncated human cytosolic leucyl-tRNA synthetase was constructed and purified, and the catalytic activities, thermal stability, and subcellular location were found to be almost identical to native enzyme. In vivo and in vitro experiments, however, show that the C-terminal extension of human cytosolic leucyl-tRNA synthetase is indispensable for its interaction with the N-terminal of human cytosolic arginyl-tRNA synthetase in the macromolecular complex. Our results also indicate that the two molecules interact with each other only through their appended domains.

Role of artificial intelligence in advancing cryo electron microscopy.

Macromolecular crystallography has been, for the last few decades, the main source of structural information of biological macromolecular systems and it is one of the most powerful techniques for the analysis of enzyme mechanisms and macromolecular interactions at the atomic level. In addition, it is also an extremely powerful tool for drug design. Recent technological and methodological developments in macromolecular X-ray crystallography have allowed solving structures that until recently were considered difficult or even impossible, such as structures at atomic or subatomic resolution or large macromolecular complexes and assemblies at low resolution. These developments have also helped to solve the 3D-structure of macromolecules from twin crystals. Recently, this technique complemented with cryo-electron microscopy and neutron crystallography has provided the structure of large macromolecular machines with great precision allowing understanding of the mechanisms of their function.

Many functions in living cells are governed by macromolecular complexes. To fully describe the underlying mechanisms, they have to be understood at atomic level. The present study combines data obtained by X-ray crystallography and cryo-electron microscopy (cryo-EM) with molecular dynamics (MD) simulations. Two functions of macromolecular complexes, the downregulation of neurotransmitter release by the SNARE protein complex under oxidative stress and the translocation of transfer RNAs (tRNAs) through the ribosome during protein synthesis, were investigated. First, the hypothesis that oxidation of two cysteines on linker of the SNARE protein SNAP-25B and consequent disulfide bond formation shortens this linker sufficiently to hinder complex formation was tested. For this purpose, MD simulations of the SNARE complex with and without the disulfide bond were compared. Disulfide bond formation lead to conformational changes of the linker and of three central hydrophobic layers necessary to form the SNARE complex. Previously, mutations of residues contributing to these layers have been shown to reduce neurotransmitter release, suggesting that the stability of these layers is crucial for complex formation. The results from the simulations agree with the hypothesis that disulfide bond formation leads to a destabilization of the SNARE complex thus rendering it dysfunctional. This mechanism is interpreted as a chemomechanical regulation to shut down neurotransmitter release under oxidative stress, which has been linked to neurodegenerative diseases. In a second part I investigated the ribosome, where after peptide bond formation, bound tRNAs translocate by more than 7 nm to adjacent binding sites (A and P to P and E), accompanied by large-scale conformational motions (L1-stalk, intersubunit rotation) of the ribosome. By combining existing cryo-EM reconstructions of translocation intermediates with high resolution crystal structures, we obtained 13 near-atomic resolution structures. Subsequently, MD simulations of were carried out for each intermediate state. The obtained dynamics within these states allowed to estimate transition rates between states for motions of the L1-stalk, tRNAs and intersubunit rotations. Rapid motions of the L1-stalk and the small (30S) subunit on sub-microsecond timescales were revealed, whereas tRNA motions were seen to be rate-limiting for most transitions. By calculating the interaction free energy between L1-stalk and tRNA, molecular forces were derived showing that the L1-stalk is actively pulling the tRNA from P to E binding site, thereby overcoming barriers for the tRNA motion. Further, ribosomal proteins L5 and L16 guide the tRNAs by 'sliding' and 'stepping' mechanisms involving key protein-tRNA contacts. This explains how tRNA binding affinity is kept sufficiently constant to allow rapid translocation despite large-scale displacements. Translocation is accompanied by rotations of the 30S ribosomal subunit of more than 20 degrees relative to the large (50S) subunit. For each translocation intermediate, the affinity of the two subunits with each other must be finely tuned enabling such conformational flexibility while maintaining integrity of the ribosomal complex. Analyzing the trajectories at residue level reveals two classes of intersubunit contact interactions: i) persistent residue contacts which are independent of 30S rotation and primarily located close to the axis of rotation. ii) contacts that are formed and ruptured depending on the rotation angle, seen mainly on the periphery. Strikingly, also these rotation specific contacts substantially contribute to the overall stability of the ribosomal assembly and are expected to maintain a constant interaction energy with low barriers for rotation. The simulations reveal that upon removal of tRNAs peripheral contacts are weakened and, in turn, intersubunit rotation angles decrease, in agreement with cryo-EM analysis of tRNA depleted ribosomes. The identified mechanisms for lowering free energy barriers and for fine-tuning affinities might have developed similarly in other macromolecular complexes.

Electron cryomicroscopy (cryo-EM) is a powerful tool in structural biology for determining 3D structures of macromolecular complexes. In the previous decade, many technological advancements have been introduced to improve the methodology of single particle cryo-EM, and hence the resolution of its protein reconstructions. These advancements include developments in both software algorithms and microscope hardware. Despite all these developments, the resolution of single particle cryo-EM cannot surpass 1.5 Å, a barrier blocking direct visualization of atomic details of the protein structure. Here, we show that by improving the optical performance of the electron microscope with new hardware, such resolution barrier can be removed, and atomic resolution of 1.25 Å can be reached with an engineered protein apoferritin. The atomic resolution protein structure helped set benchmarks for validating data quality in high resolution cryo-EM. We found that the new microscope also benefited to other proteins with lower contrast and higher flexibility, such as proteasome, chaperonin and fatty acid synthetase. For these proteins, sample properties and behavior were the major limitations. We anticipate that this would be alleviated by introducing other new microscope hardware, so that in the future, time and effort of structural biologists can concentrate on biochemical preparation of the sample, and single particle cryo-EM can routinely yield high quality protein structures at atomic resolution.

Advances in electron microscopy: A qualitative view of instrumentation development for macromolecular imaging and tomography

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.

Iron deficiency and iron deficiency anemia pose significant health challenges worldwide. Iron carbohydrate nanoparticles administered intravenously are a mainstay of treatment to deliver elemental iron safely and effectively. However, despite decades of clinical use, a complete understanding of their physical structure and the significance for their behavior, particularly at the nano-bio interface, is still lacking, underscoring the need to employ more sophisticated characterization methods. Our study used cryogenic Scanning Transmission Electron Microscopy (cryo-STEM) to examine iron carbohydrate nanoparticle morphology. This method builds upon previous research, where direct visualization of the iron cores in these complexes was achieved using cryogenic Transmission Electron Microscopy (cryo-TEM). Our study confirms that the average size of the iron cores within these nanoparticles is approximately 2 nm across all iron-based products studied. Furthermore, our investigation revealed the existence of discernible cluster-like morphologies, not only for ferumoxytol, as previously reported, but also within all the examined iron-carbohydrate products. The application of cryo-STEM for the analyses of product morphologies provides high-contrast and high-resolution images of the nanoparticles, and facilitates the characterization at liquid nitrogen temperature, thereby preserving the structural integrity of these complex samples. The findings from this study offer valuable insights into the physical structure of iron-carbohydrate nanoparticles, a crucial step towards unraveling the intricate relationship between the structure and function of this widely used drug class in treating iron deficiency. Additionally, we developed and utilized the self-supervised machine learning workflow for the image analysis of iron-carbohydrate complexes, which might be further expanded into a useful characterization tool for comparability studies.

Solving the structures of macromolecular complexes.