Can Computational Phase Retrieval Help 2D Classification in Cryo-EM?

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

With contrast enhancement technique, cryo-transmission electron microscopy (cryo-TEM) can be more advantageous. A Zernike phase plate (ZPP) and a hole-free phase plate (HFPP) are reported to have capability to enhance contrast [1, 2, 3, 4]. The ZPP is made of thin amorphous carbon film with a center hole, and the HFPP is made of thinner carbon film with no hole. In this study, we report the results with these phase plates on cryo-microscopic techniques such as cryo-TEM tomography and single particle analysis. With ZPP and HFPP, images in a close-to-focus condition exhibited higher contrast compared to defocus phase-contrast images (see Fig. A-C). The image with HFPP shows reduced fringe contrast (Fig. C), compared to the image with ZPP (Fig. B). In addition, the HFPP does not require any alignment procedure due to lack of a center hole. Thus, the data acquisition with HFPP could be easier than that with ZPP. [1] R Danev and K Nagayama, Ultramicroscopy 88 (2001) 243-252. [2] M Malac et al, Ultramicroscopy 118 (2012), 77-89. [3] M Malac et al, U.S. Patent US 8,785,850. [4] M Malac et al, JP. Patent 2011-151019. Figure: Cryo-TEM images of ice-embedded GroEL particles acquired with a few microns of defocus (A), ZPP (B) and HFPP (C). The images with ZPP and HFPP were taken with a close-to-focus condition. C2-P-07 doi:10.1093/jmicro/dfv303

Near-Atomic Resolution Structure Determination in Over-Focus with Volta Phase Plate by Cs-Corrected Cryo-EM

The rapidly evolving role of cryo-EM in drug design

Cryo-electron microscopy (cryo-EM) has emerged as a powerful technique for high-resolution structural determination of macromolecules. However, accurately classifying single-particle cryo-EM images remains challenging, especially when dealing with deformed particles. In traditional 2D classification methods, clustering algorithms are used for classification. This assumption leads to some deformed particles being misclassified in 2D images, which adversely affects downstream tasks. To address this challenge, we propose a point cloud-based deformation measurement model that integrates a Variational Autoencoder (VAE) with a heuristic point cloud matching algorithm to calculate particle deformation values. This model enables the identification and removal of particles with large deformations. Our experiments on simulated and real cryo-EM datasets, including Tobacco Mosaic Virus (TMV) and mixed capsids of MS2 virions (MS2). The model achieves robust classification (F1: 0.85-0.88) while preserving 93-95% of structural details, and can effectively filter out deformed particles after 2D classification. The model identifies and removes deformed or misclassified particles to improve classification quality. It serves as a data-filtering post-processing step following 2D classification. By improving the quality of particle datasets, it enhances the reliability of subsequent analysis in cryo-EM.

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.

Cryo-EM in Enzymology

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.

Nuclear magnetic resonance (NMR) spectroscopy plays a fundamental role in the stereochemical elucidation of chiral organic molecules. Together with X-ray crystallography, they belong to the mostly used methods for establishing the relative configuration. Conventional NMR methods rely on the usage of chemical shifts, nuclear Overhauser effects (NOEs), and homonuclear and heteronuclear scalar couplings ( J -couplings). In this chapter, basic theory of each NMR parameter is discussed, while several examples are demonstrated showing the power of isotropic NMR restraints in the determination of relative configuration. The application of anisotropic NMR data, such as residual dipolar couplings (RDCs) and residual chemical shift anisotropies (RCSAs), has emerged as a powerful technique in the past 20 years for establishing constitution, relative configuration, and conformation of flexible and complex molecules. In strong contrast to NOE and scalar coupling, RDC and RCSA are long-range structural parameters. In this chapter, a number of successful examples illustrating the application of anisotropic NMR data in the determination of relative configuration will be given, where in some cases their absolute configuration could be established by a combination of anisotropic NMR and chiroptical spectroscopy. We also review recent advances in the development of new alignment media that is essential for anisotropic NMR measurements. Novel computational approaches for analyzing the anisotropic NMR parameters are briefly discussed, as these methods significantly accelerated the procedure for establishing the complete three-dimensional structures of organic molecules. In the last part, we review recent progress and application of computational NMR methods based on density functional theory (DFT) such as DP4/DP4+ and CASE-3D, which become more and more popular in determining the relative configuration of chiral organic molecules. For establishing the absolute configuration, we discuss both methods relying on chiral derivatizing agents and chiral solvents. We put a special focus on Mosher ester method based on chiral derivatization, as this method is among the most popular one. The principal and application of Mosher ester method is illustrated with published example.

Chapter 4 - NMR-based approaches to the study of GPCRs and GPCR-ligand interactions

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.

The insulin receptor (IR) is a type II receptor tyrosine kinase that plays essential roles in metabolism, growth, and proliferation. Dysregulation of IR signaling is linked to many human diseases, such as diabetes and cancers. The resolution revolution in cryo-electron microscopy has led to the determination of several structures of IR with different numbers of bound insulin molecules in recent years, which have tremendously improved our understanding of how IR is activated by insulin. Here, we review the insulin-induced activation mechanism of IR, including (a) the detailed binding modes and functions of insulin at site 1 and site 2 and (b) the insulin-induced structural transitions that are required for IR activation. We highlight several other key aspects of the activation and regulation of IR signaling and discuss the remaining gaps in our understanding of the IR activation mechanism and potential avenues of future research.

Assembly of eukaryotic ribosomal subunits is a very complex and sequential process that starts in the nucleolus and finishes in the cytoplasm with the formation of functional ribosomes. Over the past few years, characterization of the many molecular events underlying eukaryotic ribosome biogenesis has been drastically improved by the “resolution revolution” of cryo-electron microscopy (cryo-EM). However, if very early maturation events have been well characterized for both yeast ribosomal subunits, little is known regarding the final maturation steps occurring to the small (40S) ribosomal subunit. To try to bridge this gap, we have used proteomics together with cryo-EM and single particle analysis to characterize yeast pre-40S particles containing the ribosome biogenesis factor Tsr1. Our analyses lead us to refine the timing of the early pre-40S particle maturation steps. Furthermore, we suggest that after an early and structurally stable stage, the beak and platform domains of pre-40S particles enter a “vibrating” or “wriggling” stage, that might be involved in the final maturation of 18S rRNA as well as the fitting of late ribosomal proteins into their mature position.

Recent developments in cryo-electron microscopy (cryo-EM) hardware along with continuously evolving software tools have led to the discovery of many novel structures that it was not possible to solve until now, resulting in what is termed “the resolution revolution”. In structural virology, it has also led to a re-evaluation of known structures. Most virion structures solved by X-ray crystallography or cryo-EM are focused on the capsid protein (CP) as a result of the application of icosahedral symmetry averaging to “improve” the electron density maps. However, this has the consequence that the intrinsic asymmetry of important components of virions, such as the viral genome and structural proteins lacking such symmetry, are masked. Single-stranded (ss), positive-sense RNA viruses are major pathogens in all kingdoms of life. Asymmetric cryo-EM structure determination of a major model virus in this class, bacteriophage MS2, reveals the limitations of a symmetrized view. As well as the presence and interactions made by the unique Maturation Protein, it also reveals multiple gRNA-CP dimer contacts corresponding to our previous prediction that dispersed, sequence-degenerate RNA motifs (Packaging Signals, PSs) play important roles during the virion assembly. Here, we describe how relaxing symmetry during structure determination can image such gRNA-PS contacts in a range of ssRNA viruses including the picornavirus Bovine Enterovirus-1, the alphaviruses Sindbis and Semliki Forest Viruses, as well as the plant virus Turnip Crinkle Virus. The revelation of these functionally important gRNA-CP contacts changes our fundamental understanding of assembly in these pathogens and may have further translational importance.

Cryogenic transmission electron microscopy (cryo-TEM) combined with single particle analysis (SPA) is an emerging imaging approach for soft materials. However, the accuracy of SPA-reconstructed nanostructures, particularly those formed by synthetic polymers, remains uncertain due to potential packing heterogeneity of the nanostructures. In this study, the combination of molecular dynamics (MD) simulations and image simulations is utilized to validate the accuracy of cryo-TEM 3D reconstructions of self-assembled polypeptoid fibril nanostructures. Using CryoSPARC software, image simulations, 2D classifications, ab initio reconstructions, and homogenous refinements are performed. By comparing the results with atomic models, the recovery of molecular details is assessed, heterogeneous structures are identified, and the influence of extraction location on the reconstructions is evaluated. These findings confirm the fidelity of single particle analysis in accurately resolving complex structural characteristics and heterogeneous structures, exhibiting its potential as a valuable tool for detailed structural analysis of synthetic polymers and soft materials.