Atomic Resolution Cryo-EM Structure of β-Galactosidase

Outcomes of the EMDataResource cryo-EM Ligand Modeling Challenge

The EMDataResource Ligand Model Challenge aimed to assess the reliability and reproducibility of modeling ligands bound to protein and protein-nucleic acid complexes in cryogenic electron microscopy (cryo-EM) maps determined at near-atomic (1.9-2.5 Å) resolution. Three published maps were selected as targets: Escherichia coli beta-galactosidase with inhibitor, SARS-CoV-2 virus RNA-dependent RNA polymerase with covalently bound nucleotide analog and SARS-CoV-2 virus ion channel ORF3a with bound lipid. Sixty-one models were submitted from 17 independent research groups, each with supporting workflow details. The quality of submitted ligand models and surrounding atoms were analyzed by visual inspection and quantification of local map quality, model-to-map fit, geometry, energetics and contact scores. A composite rather than a single score was needed to assess macromolecule+ligand model quality. These observations lead us to recommend best practices for assessing cryo-EM structures of liganded macromolecules reported at near-atomic resolution.

Mechanism of chaperone coordination during cotranslational protein folding in bacteria

Protein folding is assisted by molecular chaperones that bind nascent polypeptides during mRNA translation. Several structurally distinct classes of chaperones promote de novo folding, suggesting that their activities are coordinated at the ribosome. We used biochemical reconstitution and structural proteomics to explore the molecular basis for cotranslational chaperone action in bacteria. We found that chaperone binding is disfavored close to the ribosome, allowing folding to precede chaperone recruitment. Trigger factor recognizes compact folding intermediates that expose an extensive unfolded surface, and dictates DnaJ access to nascent chains. DnaJ uses a large surface to bind structurally diverse intermediates and recruits DnaK to sequence-diverse solvent-accessible sites. Neither Trigger factor, DnaJ, nor DnaK destabilize cotranslational folding intermediates. Instead, the chaperones collaborate to protect incipient structure in the nascent polypeptide well beyond the ribosome exit tunnel. Our findings show how the chaperone network selects and modulates cotranslational folding intermediates.

Machine learning approaches to cryoEM density modification differentially affect biomacromolecule and ligand density quality

The application of machine learning to cryogenic electron microscopy (cryoEM) data analysis has added a valuable set of tools to the cryoEM data processing pipeline. As these tools become more accessible and widely available, the implications of their use should be assessed. We noticed that machine learning map modification tools can have differential effects on cryoEM densities. In this perspective, we evaluate these effects to show that machine learning tools generally improve densities for biomacromolecules while generating unpredictable results for ligands. This unpredictable behavior manifests both in quantitative metrics of map quality and in qualitative investigations of modified maps. The results presented here highlight the power and potential of machine learning tools in cryoEM, while also illustrating some of the risks of their unexamined use.

Exploring advances in single particle CryoEM with apoferritin: From blobs to true atomic resolution

Deciphering the three-dimensional structures of macromolecules is of paramount importance for gaining insights into their functions and roles in human health and disease. Single particle cryoEM has emerged as a powerful technique that enables direct visualization of macromolecules and their complexes, and through subsequent averaging, achieve near atomic-level resolution. A major breakthrough was recently achieved with the determination of the apoferritin structure at true atomic resolution. In this review, we discuss the latest technological innovations across the entire single-particle workflow, which have been instrumental in driving the resolution revolution and in transforming cryoEM as a mainstream technique in structural biology. We illustrate these advancements using apoferritin as an example that has served as an excellent benchmark sample for assessing emerging technologies. We further explore whether the existing technology can routinely generate atomic structures of dynamic macromolecules that more accurately represent real-world samples, the limitations in the workflow, and the current approaches employed to overcome them.

Joint micrograph denoising and protein localization in cryo-electron microscopy

Cryo-electron microscopy (cryo-EM) is an imaging technique that allows the visualization of proteins and macromolecular complexes at near-atomic resolution. The low electron doses used to prevent radiation damage to the biological samples result in images where the power of noise is 100 times stronger than that of the signal. Accurate identification of proteins from these low signal-to-noise ratio (SNR) images is a critical task, as the detected positions serve as inputs for the downstream 3D structure determination process. Current methods either fail to identify all true positives or result in many false positives, especially when analyzing images from smaller-sized proteins that exhibit extremely low contrast, or require manual labeling that can take days to complete. Acknowledging the fact that accurate protein identification is dependent upon the visual interpretability of micrographs, we propose a framework that can perform denoising and detection in a joint manner and enable particle localization under extremely low SNR conditions using self-supervised denoising and particle identification from sparsely annotated data. We validate our approach on three challenging single-particle cryo-EM datasets and projection images from one cryo-electron tomography dataset with extremely low SNR, showing that it outperforms existing state-of-the-art methods used for cryo-EM image analysis by a significant margin. We also evaluate the performance of our algorithm under decreasing SNR conditions and show that our method is more robust to noise than competing methods.

Cryo-EM of soft-landed β-galactosidase: Gas-phase and native structures are remarkably similar

Native mass spectrometry (MS) has become widely accepted in structural biology, providing information on stoichiometry, interactions, homogeneity, and shape of protein complexes. Yet, the fundamental assumption that proteins inside the mass spectrometer retain a structure faithful to native proteins in solution remains a matter of intense debate. Here, we reveal the gas-phase structure of β-galactosidase using single-particle cryo-electron microscopy (cryo-EM) down to 2.6-Å resolution, enabled by soft landing of mass-selected protein complexes onto cold transmission electron microscopy (TEM) grids followed by in situ ice coating. We find that large parts of the secondary and tertiary structure are retained from the solution. Dehydration-driven subunit reorientation leads to consistent compaction in the gas phase. By providing a direct link between high-resolution imaging and the capability to handle and select protein complexes that behave problematically in conventional sample preparation, the approach has the potential to expand the scope of both native mass spectrometry and cryo-EM.

Outcomes of the EMDataResource Cryo-EM Ligand Modeling Challenge

The EMDataResource Ligand Model Challenge aimed to assess the reliability and reproducibility of modeling ligands bound to protein and protein/nucleic-acid complexes in cryogenic electron microscopy (cryo-EM) maps determined at near-atomic (1.9-2.5 Å) resolution. Three published maps were selected as targets: E. coli beta-galactosidase with inhibitor, SARS-CoV-2 RNA-dependent RNA polymerase with covalently bound nucleotide analog, and SARS-CoV-2 ion channel ORF3a with bound lipid. Sixty-one models were submitted from 17 independent research groups, each with supporting workflow details. We found that (1) the quality of submitted ligand models and surrounding atoms varied, as judged by visual inspection and quantification of local map quality, model-to-map fit, geometry, energetics, and contact scores, and (2) a composite rather than a single score was needed to assess macromolecule+ligand model quality. These observations lead us to recommend best practices for assessing cryo-EM structures of liganded macromolecules reported at near-atomic resolution.

Improving resolution and resolvability of single-particle cryoEM structures using Gaussian mixture models

Cryogenic electron microscopy is widely used in structural biology, but its resolution is often limited by the dynamics of the macromolecule. Here we developed a refinement protocol based on Gaussian mixture models that integrates particle orientation and conformation estimation and improves the alignment for flexible domains of protein structures. We demonstrated this protocol on multiple datasets, resulting in improved resolution and resolvability, locally and globally, by visual and quantitative measures.

nextPYP: a comprehensive and scalable platform for characterizing protein variability in situ using single-particle cryo-electron tomography

Single-particle cryo-electron tomography is an emerging technique capable of determining the structure of proteins imaged within the native context of cells at molecular resolution. While high-throughput techniques for sample preparation and tilt-series acquisition are beginning to provide sufficient data to allow structural studies of proteins at physiological concentrations, the complex data analysis pipeline and the demanding storage and computational requirements pose major barriers for the development and broader adoption of this technology. Here, we present a scalable, end-to-end framework for single-particle cryo-electron tomography data analysis from on-the-fly pre-processing of tilt series to high-resolution refinement and classification, which allows efficient analysis and visualization of datasets with hundreds of tilt series and hundreds of thousands of particles. We validate our approach using in vitro and cellular datasets, demonstrating its effectiveness at achieving high-resolution and revealing conformational heterogeneity in situ. The framework is made available through an intuitive and easy-to-use computer application, nextPYP ( http://nextpyp.app ).

Structural basis for breadth development in the HIV-1 V3-glycan targeting DH270 antibody clonal lineage

Antibody affinity maturation enables adaptive immune responses to a wide range of pathogens. In some individuals broadly neutralizing antibodies develop to recognize rapidly mutating pathogens with extensive sequence diversity. Vaccine design for pathogens such as HIV-1 and influenza has therefore focused on recapitulating the natural affinity maturation process. Here, we determine structures of antibodies in complex with HIV-1 Envelope for all observed members and ancestral states of the broadly neutralizing HIV-1 V3-glycan targeting DH270 antibody clonal B cell lineage. These structures track the development of neutralization breadth from the unmutated common ancestor and define affinity maturation at high spatial resolution. By elucidating contacts mediated by key mutations at different stages of antibody development we identified sites on the epitope-paratope interface that are the focus of affinity optimization. Thus, our results identify bottlenecks on the path to natural affinity maturation and reveal solutions for these that will inform immunogen design aimed at eliciting a broadly neutralizing immune response by vaccination.

Synthesis of thiodisaccharides related to 4-thiolactose. Specific structural modifications increase the inhibitory activity against E. coli β-galactosidase

In the search for new glycosidase inhibitors, a set of benzyl β-D-Gal-S-(1→4)-3-deoxy-4-thio-α-D-hexopyranosides was synthesized. Diverse configurations were installed at C-2 and C-4 of the glucose residue. The benzyl glycosidic group was kept intact or substituted by an electron-donating or electron-withdrawing group that could also participate in hydrogen bonding. All thiodisaccharides were found to be inhibitors of E. coli β-galactosidase. In general, benzyl thiodisaccharides were better inhibitors than those substituted (NO₂ or NH₂) on the benzyl ring. Thiodisaccharides containing a hexopyranoside, instead of a pentopyranoside, showed a weaker inhibitory activity, except for those having the α-D-xylo configuration, which exhibited inhibition constants of the same order of magnitude. These and previous results indicated that the inhibition process by thiodisaccharides is strongly dependent on the configuration of the 3-deoxy-4-thiopyranoside, as well as its substitution pattern (such as the presence of a benzyl glycoside). The enzyme-inhibitor interaction during the hydrolysis process involves a conformational selection resulting from rotation around the thioglycosidic bond and the flexibility of the terminal six-membered ring. Thus, the mentioned structural features of the inhibitor could give rise to favorable ground state conformations for the interaction with the enzyme, similar to those found for selected thiodisaccharides in the bound state. These studies demonstrated that the performance of thiodisaccharides as enzyme inhibitors could be increased by selecting the appropriate configuration and substitution of the hexopyranoside replacing the glucose moiety of 4-thiolactose.

Multidisciplinary Advances Address the Challenges in Developing Drugs against Transient Receptor Potential Channels to Treat Metabolic Disorders

Transient receptor potential (TRP) channels are cation channels that regulate key physiological and pathological processes in response to a broad range of stimuli. Moreover, they systemically regulate the release of hormones, metabolic homeostasis, and complications of diabetes, which positions them as promising therapeutic targets to combat metabolic disorders. Nevertheless, there are significant challenges in the design of TRP ligands with high potency and durability. Herein we summarize the four challenges as hydrophobicity, selectivity, mono-target therapy, and interspecies discrepancy. We present 1134 TRP ligands with diversified modes of TRP-ligand interaction and provide a detailed discussion of the latest strategies, especially cryogenic electron microscopy (cryo-EM) and computational methods. We propose solutions to address the challenges with a critical analysis of advances in membrane partitioning, polypharmacology, biased agonism, and biochemical screening of transcriptional modulators. They are fueled by the breakthrough from cryo-EM, chemoinformatics and bioinformatics. The discussion is aimed to shed new light on designing next-generation drugs to treat obesity, diabetes and its complications, with optimal hydrophobicity, higher mode selectivity, multi-targeting and consistent activities between human and rodents.

Multiple-image super-resolution of cryo-electron micrographs based on deep internal learning

Single-particle cryo-electron microscopy (cryo-EM) is a powerful imaging modality capable of visualizing proteins and macromolecular complexes at near-atomic resolution. The low electron-doses used to prevent radiation damage to the biological samples, however, result in images where the power of the noise is 100 times greater than the power of the signal. To overcome these low signal-to-noise ratios (SNRs), hundreds of thousands of particle projections are averaged to determine the three-dimensional structure of the molecule of interest. The sampling requirements of high-resolution imaging impose limitations on the pixel sizes that can be used for acquisition, limiting the size of the field of view and requiring data collection sessions of several days to accumulate sufficient numbers of particles. Meanwhile, recent image super-resolution (SR) techniques based on neural networks have shown state-of-the-art performance on natural images. Building on these advances, here, we present a multiple-image SR algorithm based on deep internal learning designed specifically to work under low-SNR conditions. Our approach leverages the internal image statistics of cryo-EM movies and does not require training on ground-truth data. When applied to single-particle datasets of apoferritin and T20S proteasome, we show that the resolution of the 3D structure obtained from SR micrographs can surpass the limits imposed by the imaging system. Our results indicate that the combination of low magnification imaging with in silico image SR has the potential to accelerate cryo-EM data collection by virtue of including more particles in each exposure and doing so without sacrificing resolution.

Improving Protein-Ligand Interaction Modeling with cryo-EM Data, Templates, and Deep Learning in 2021 Ligand Model Challenge

Elucidating protein-ligand interaction is crucial for studying the function of proteins and compounds in an organism and critical for drug discovery and design. The problem of protein-ligand interaction is traditionally tackled by molecular docking and simulation, which is based on physical forces and statistical potentials and cannot effectively leverage cryo-EM data and existing protein structural information in the protein-ligand modeling process. In this work, we developed a deep learning bioinformatics pipeline (DeepProLigand) to predict protein-ligand interactions from cryo-EM density maps of proteins and ligands. DeepProLigand first uses a deep learning method to predict the structure of proteins from cryo-EM maps, which is averaged with a reference (template) structure of the proteins to produce a combined structure to add ligands. The ligands are then identified and added into the structure to generate a protein-ligand complex structure, which is further refined. The method based on the deep learning prediction and template-based modeling was blindly tested in the 2021 EMDataResource Ligand Challenge and was ranked first in fitting ligands to cryo-EM density maps. These results demonstrate that the deep learning bioinformatics approach is a promising direction for modeling protein-ligand interactions on cryo-EM data using prior structural information.

Multi-curve fitting and tubulin-lattice signal removal for structure determination of large microtubule-based motors

Revealing high-resolution structures of microtubule-associated proteins (MAPs) is critical for understanding their fundamental roles in various cellular activities, such as cell motility and intracellular cargo transport. Nevertheless, large flexible molecular motors that dynamically bind and release microtubule networks are challenging for cryo-electron microscopy (cryo-EM). Traditional structure determination of MAPs bound to microtubules needs alignment information from the reconstruction of microtubules, which cannot be readily applied to large MAPs without a fixed binding pattern. Here, we developed a comprehensive approach to estimate the microtubule networks (multi-curve fitting), model the tubulin-lattice signals, and remove them (tubulin-lattice subtraction) from the raw cryo-EM micrographs. The approach does not require an ordered binding pattern of MAPs on microtubules, nor does it need a reconstruction of the microtubules. We demonstrated the capability of our approach using the reconstituted outer-arm dynein (OAD) bound to microtubule doublets. The tubulin-lattice subtraction improves the OAD alignment, thus leading to high-resolution reconstructions. In addition, the multi-curve fitting approach provides an accurate automatic alternative method to pick or segment filaments in 2D images and potentially in 3D tomograms. The accuracy of our approach has been demonstrated by using several other biological filaments. Our work provides a new tool to determine high-resolution structures of large MAPs bound to curved microtubule networks.

Cryo-electron tomography related radiation-damage parameters for individual-molecule 3D structure determination

To understand the dynamic structure-function relationship of soft- and biomolecules, the determination of the three-dimensional (3D) structure of each individual molecule (nonaveraged structure) in its native state is sought-after. Cryo-electron tomography (cryo-ET) is a unique tool for imaging an individual object from a series of tilted views. However, due to radiation damage from the incident electron beam, the tolerable electron dose limits image contrast and the signal-to-noise ratio (SNR) of the data, preventing the 3D structure determination of individual molecules, especially at high-resolution. Although recently developed technologies and techniques, such as the direct electron detector, phase plate, and computational algorithms, can partially improve image contrast/SNR at the same electron dose, the high-resolution structure, such as tertiary structure of individual molecules, has not yet been resolved. Here, we review the cryo-electron microscopy (cryo-EM) and cryo-ET experimental parameters to discuss how these parameters affect the extent of radiation damage. This discussion can guide us in optimizing the experimental strategy to increase the imaging dose or improve image SNR without increasing the radiation damage. With a higher dose, a higher image contrast/SNR can be achieved, which is crucial for individual-molecule 3D structure. With 3D structures determined from an ensemble of individual molecules in different conformations, the molecular mechanism through their biochemical reactions, such as self-folding or synthesis, can be elucidated in a straightforward manner.

Mass-selective and ice-free electron cryomicroscopy protein sample preparation via native electrospray ion-beam deposition

Despite tremendous advances in sample preparation and classification algorithms for electron cryomicroscopy (cryo-EM) and single-particle analysis (SPA), sample heterogeneity remains a major challenge and can prevent access to high-resolution structures. In addition, optimization of preparation conditions for a given sample can be time-consuming. In the current work, it is demonstrated that native electrospray ion-beam deposition (native ES-IBD) is an alternative, reliable approach for the preparation of extremely high-purity samples, based on mass selection in vacuum. Folded protein ions are generated by native electrospray ionization, separated from other proteins, contaminants, aggregates, and fragments, gently deposited on cryo-EM grids, frozen in liquid nitrogen, and subsequently imaged by cryo-EM. We demonstrate homogeneous coverage of ice-free cryo-EM grids with mass-selected protein complexes. SPA reveals that the complexes remain folded and assembled, but variations in secondary and tertiary structures are currently limiting information in 2D classes and 3D EM density maps. We identify and discuss challenges that need to be addressed to obtain a resolution comparable to that of the established cryo-EM workflow. Our results show the potential of native ES-IBD to increase the scope and throughput of cryo-EM for protein structure determination and provide an essential link between gas-phase and solution-phase protein structures.

It started with a Cys: Spontaneous cysteine modification during cryo-EM grid preparation

Advances in single particle cryo-EM data collection and processing have seen a significant rise in its use. However, the influences of the environment generated through grid preparation, by for example interactions of proteins with the air-water interface are poorly understood and can be a major hurdle in structure determination by cryo-EM. Initial interactions of proteins with the air-water interface occur quickly and proteins can adopt preferred orientation or partially unfold within hundreds of milliseconds. It has also been shown previously that thin-film layers create hydroxyl radicals. To investigate the potential this might have in cryo-EM sample preparation, we studied two proteins, HSPD1, and beta-galactosidase, and show that cysteine residues are modified in a time-dependent manner. In the case of both HSPD1 and beta-galactosidase, this putative oxidation is linked to partial protein unfolding, as well as more subtle structural changes. We show these modifications can be alleviated through increasing the speed of grid preparation, the addition of DTT, or by sequestering away from the AWI using continuous support films. We speculate that the modification is oxidation by reactive oxygen species which are formed and act at the air-water interface. Finally, we show grid preparation on a millisecond timescale outruns cysteine modification, showing that the reaction timescale is in the range of 100s to 1,000s milliseconds and offering an alternative approach to prevent spontaneous cysteine modification and its consequences during cryo-EM grid preparation.

Liquid-EM goes viral - visualizing structure and dynamics

Liquid-electron microscopy (EM), the room temperature correlate to cryo-EM, is an exciting new technique delivering real-time data of dynamic reactions in solution. Here, we explain how liquid-EM gained popularity in recent years by examining key experiments conducted on viral assemblies and host-pathogen interactions. We describe developing workflows for specimen preparation, data collection, and computing processes that led to the first high-resolution virus structures in a liquid environment. Equally important, we review why liquid-electron tomography may become the next big thing in biomedical research due to its ability to monitor live viruses entering cells within seconds. Taken together, we pose the idea that liquid-EM can serve as a dynamic complement to current cryo-EM methods, inspiring the "real-time revolution" in nanoscale imaging.

Emerging Themes in CryoEM─Single Particle Analysis Image Processing

Cryo-electron microscopy (CryoEM) has become a vital technique in structural biology. It is an interdisciplinary field that takes advantage of advances in biochemistry, physics, and image processing, among other disciplines. Innovations in these three basic pillars have contributed to the boosting of CryoEM in the past decade. This work reviews the main contributions in image processing to the current reconstruction workflow of single particle analysis (SPA) by CryoEM. Our review emphasizes the time evolution of the algorithms across the different steps of the workflow differentiating between two groups of approaches: analytical methods and deep learning algorithms. We present an analysis of the current state of the art. Finally, we discuss the emerging problems and challenges still to be addressed in the evolution of CryoEM image processing methods in SPA.

High-resolution structure determination using high-throughput electron cryo-tomography

In this article, it is shown that high-throughput strategies for tomographic data acquisition combined with unsupervised techniques for image analysis provide the foundation for closing the resolution gap between the high-resolution strategies used to study molecular assemblies reconstituted in vitro and techniques for in situ structure determination.

Tomographic reconstruction of frozen-hydrated specimens followed by extraction and averaging of sub-tomograms has successfully been used to determine the structure of macromolecules in their native environment at resolutions that are high enough to reveal molecular level interactions. The low throughput characteristic of tomographic data acquisition combined with the complex data-analysis pipeline that is required to obtain high-resolution maps, however, has limited the applicability of this technique to favorable samples or to resolutions that are too low to provide useful mechanistic information. Recently, beam image-shift electron cryo-tomography (BISECT), a strategy to significantly accelerate the acquisition of tilt series without sacrificing image quality, was introduced. The ability to produce thousands of high-quality tilt series during a single microscope session, however, introduces significant bottlenecks in the downstream data analysis, which has so far relied on specialized pipelines. Here, recent advances in accurate estimation of the contrast transfer function and self-tuning exposure-weighting routines that contribute to improving the resolution and streamlining the structure-determination process using sub-volume averaging are reviewed. Ultimately, the combination of automated data-driven techniques for image analysis together with high-throughput strategies for tilt-series acquisition will pave the way for tomography to become the technique of choice for in situ structure determination.

Beyond the Backbone: The Next Generation of Pathwalking Utilities for Model Building in CryoEM Density Maps

Single-particle electron cryomicroscopy (cryoEM) has become an indispensable tool for studying structure and function in macromolecular assemblies. As an integral part of the cryoEM structure determination process, computational tools have been developed to build atomic models directly from a density map without structural templates. Nearly a decade ago, we created Pathwalking, a tool for de novo modeling of protein structure in near-atomic resolution cryoEM density maps. Here, we present the latest developments in Pathwalking, including the addition of probabilistic models, as well as a companion tool for modeling waters and ligands. This software was evaluated on the 2021 CryoEM Ligand Challenge density maps, in addition to identifying ligands in three IP3R1 density maps at ~3 Å to 4.1 Å resolution. The results clearly demonstrate that the Pathwalking de novo modeling pipeline can construct accurate protein structures and reliably localize and identify ligand density directly from a near-atomic resolution map.

On bias, variance, overfitting, gold standard and consensus in single-particle analysis by cryo-electron microscopy

Single-particle analysis (SPA) by cryo-electron microscopy comprises the estimation of many parameters along its image-processing pipeline. Overfitting observed in SPA is normally due to misestimated parameters, and the only way to identify these is by comparing the estimates of multiple algorithms or, at least, multiple executions of the same algorithm.

Cryo-electron microscopy (cryoEM) has become a well established technique to elucidate the 3D structures of biological macromolecules. Projection images from thousands of macromolecules that are assumed to be structurally identical are combined into a single 3D map representing the Coulomb potential of the macromolecule under study. This article discusses possible caveats along the image-processing path and how to avoid them to obtain a reliable 3D structure. Some of these problems are very well known in the community. These may be referred to as sample-related (such as specimen denaturation at interfaces or non-uniform projection geometry leading to underrepresented projection directions). The rest are related to the algorithms used. While some have been discussed in depth in the literature, such as the use of an incorrect initial volume, others have received much less attention. However, they are fundamental in any data-analysis approach. Chiefly among them, instabilities in estimating many of the key parameters that are required for a correct 3D reconstruction that occur all along the processing workflow are referred to, which may significantly affect the reliability of the whole process. In the field, the term overfitting has been coined to refer to some particular kinds of artifacts. It is argued that overfitting is a statistical bias in key parameter-estimation steps in the 3D reconstruction process, including intrinsic algorithmic bias. It is also shown that common tools (Fourier shell correlation) and strategies (gold standard) that are normally used to detect or prevent overfitting do not fully protect against it. Alternatively, it is proposed that detecting the bias that leads to overfitting is much easier when addressed at the level of parameter estimation, rather than detecting it once the particle images have been combined into a 3D map. Comparing the results from multiple algorithms (or at least, independent executions of the same algorithm) can detect parameter bias. These multiple executions could then be averaged to give a lower variance estimate of the underlying parameters.

The E. coli DnaK chaperone stimulates the α-complementation of β-galactosidase

pUC18 and pUC19 are well-known high copy-number plasmid vectors routinely used for DNA cloning purposes. We show here that, in E. coli transformed by native pUC18, the α-complementation of β-galactosidase (i.e., mediated by the peptide LacZα18) is intrinsically weak and slow, but is greatly stimulated by the DnaK/DnaJ/GrpE chaperone system. In contrast, the α-complementation mediated by the peptide LacZα19 (in E. coli transformed by the native pUC19) is much more efficient, and therefore does not require the assistance of the DnaK chaperone machinery. The marked difference between these two LacZα peptides is reproduced in cell-free protein expression system coupled with α-complementation. We conclude that: (i) α-complementation of β-galactosidase is DnaK-mediated depending upon the LacZα peptide donor. (ii) DnaK, sensu stricto, is not necessary for α-complementation, but can enhance it to a great extent. (iii) this observation could be used to establish an easy and inexpensive method for screening small molecules libraries in search of DnaK inhibitors and also for deciphering the DnaK-mediated protein quality control mechanism. This article is protected by copyright. All rights reserved.

EMDA: A Python package for Electron Microscopy Data Analysis

An open-source Python library EMDA for cryo-EM map and model manipulation is presented with a specific focus on validation. The use of several functionalities in the library is presented through several examples. The utility of local correlation as a metric for identifying map-model differences and unmodeled regions in maps, and how it is used as a metric of map-model validation is demonstrated. The mapping of local correlation to individual atoms, and its use to draw insights on local signal variations are discussed. EMDA’s likelihood-based map overlay is demonstrated by carrying out a superposition of two domains in two related structures. The overlay is carried out first to bring both maps into the same coordinate frame and then to estimate the relative movement of domains. Finally, the map magnification refinement in EMDA is presented with an example to highlight the importance of adjusting the map magnification in structural comparison studies.

Current Approaches in Supersecondary Structures Investigation

Proteins expressed during the cell cycle determine cell function, topology, and responses to environmental influences. The development and improvement of experimental methods in the field of structural biology provide valuable information about the structure and functions of individual proteins. This work is devoted to the study of supersecondary structures of proteins and determination of their structural motifs, description of experimental methods for their detection, databases, and repositories for storage, as well as methods of molecular dynamics research. The interest in the study of supersecondary structures in proteins is due to their autonomous stability outside the protein globule, which makes it possible to study folding processes, conformational changes in protein isoforms, and aberrant proteins with high productivity.

Cryo-TEM simulations of amorphous radiation-sensitive samples using multislice wave propagation

Image simulation plays a central role in the development and practice of high-resolution electron microscopy, including transmission electron microscopy of frozen-hydrated specimens (cryo-EM). Simulating images with contrast that matches the contrast observed in experimental images remains challenging, especially for amorphous samples. Current state-of-the-art simulators apply post hoc scaling to approximate empirical solvent contrast, attenuated image intensity due to specimen thickness and amplitude contrast. This practice fails for images that require spatially variable scaling, e.g. simulations of a crowded or cellular environment. Modeling both the signal and the noise accurately is necessary to simulate images of biological specimens with contrast that is correct on an absolute scale. The 'frozen plasmon' method is introduced to explicitly model spatially variable inelastic scattering processes in cryo-EM specimens. This approach produces amplitude contrast that depends on the atomic composition of the specimen, reproduces the total inelastic mean free path as observed experimentally and allows for the incorporation of radiation damage in the simulation. These improvements are quantified using the matched filter concept to compare simulation and experiment. The frozen plasmon method, in combination with a new mathematical formulation for accurately sampling the tabulated atomic scattering potentials onto a Cartesian grid, is implemented in the open-source software package cisTEM.

Toward Compositional Contrast by Cryo-STEM

Electron microscopy (EM) is the most versatile tool for the study of matter at scales ranging from subatomic to visible. The high vacuum environment and the charged irradiation require careful stabilization of many specimens of interest. Biological samples are particularly sensitive due to their composition of light elements suspended in an aqueous medium. Early investigators developed techniques of embedding and staining with heavy metal salts for contrast enhancement. Indeed, the Nobel Prize in 1974 recognized Claude, de Duve, and Palade for establishment of the field of cell biology, largely due to their developments in separation and preservation of cellular components for electron microscopy. A decade later, cryogenic fixation was introduced. Vitrification of the water avoids the need for dehydration and provides an ideal matrix in which the organic macromolecules are suspended; the specimen represents a native state, suddenly frozen in time at temperatures below -150 °C. The low temperature maintains a low vapor pressure for the electron microscope, and the amorphous nature of the medium avoids diffraction contrast from crystalline ice. Such samples are extremely delicate, however, and cryo-EM imaging is a race for information in the face of ongoing damage by electron irradiation. Through this journey, cryo-EM enhanced the resolution scale from membranes to molecules and most recently to atoms. Cryo-EM pioneers, Dubochet, Frank, and Henderson, were awarded the Nobel Prize in 2017 for high resolution structure determination of biological macromolecules.A relatively untapped feature of cryo-EM is its preservation of composition. Nothing is added and nothing removed. Analytical spectroscopies based on electron energy loss or X-ray emission can be applied, but the very small interaction cross sections conflict with the weak exposures required to preserve sample integrity. To what extent can we interpret quantitatively the pixel intensities in images themselves? Conventional cryo-transmission electron microscopy (TEM) is limited in this respect, due to the strong dependence of the contrast transfer on defocus and the absence of contrast at low spatial frequencies.Inspiration comes largely from a different modality for cryo-tomography, using soft X-rays. Contrast depends on the difference in atomic absorption between carbon and oxygen in a region of the spectrum between their core level ionization energies, the so-called water window. Three dimensional (3D) reconstruction provides a map of the local X-ray absorption coefficient. The quantitative contrast enables the visualization of organic materials without stain and measurement of their concentration quantitatively. We asked, what aspects of the quantitative contrast might be transferred to cryo-electron microscopy?Compositional contrast is accessible in scanning transmission EM (STEM) via incoherent elastic scattering, which is sensitive to the atomic number Z. STEM can be regarded as a high energy, low angle diffraction measurement performed pixel by pixel with a weakly convergent beam. When coherent diffraction effects are absent, that is, in amorphous materials, a dark field signal measures quantitatively the flux scattered from the specimen integrated over the detector area. Learning to interpret these signals will open a new dimension in cryo-EM. This Account describes our efforts so far to introduce STEM for cryo-EM and tomography of biological specimens. We conclude with some thoughts on further developments.

End-to-end robust joint unsupervised image alignment and clustering

Computing dense pixel-to-pixel image correspondences is a fundamental task of computer vision. Often, the objective is to align image pairs from the same semantic category for manipulation or segmentation purposes. Despite achieving superior performance, existing deep learning alignment methods cannot cluster images; consequently, clustering and pairing images needed to be a separate laborious and expensive step. Given a dataset with diverse semantic categories, we propose a multi-task model, Jim-Net, that can directly learn to cluster and align images without any pixel-level or image-level annotations. We design a pair-matching alignment unsupervised training algorithm that selectively matches and aligns image pairs from the clustering branch. Our unsupervised Jim-Net achieves comparable accuracy with state-of-the-art supervised methods on benchmark 2D image alignment dataset PF-PASCAL. Specifically, we apply Jim-Net to cryo-electron tomography, a revolutionary 3D microscopy imaging technique of native subcellular structures. After extensive evaluation on seven datasets, we demonstrate that Jim-Net enables systematic discovery and recovery of representative macromolecular structures in situ, which is essential for revealing molecular mechanisms underlying cellular functions. To our knowledge, Jim-Net is the first end-to-end model that can simultaneously align and cluster images, which significantly improves the performance as compared to performing each task alone.

Single-Particle Cryo-EM of Membrane Proteins

In the recent years, the protein databank has been fueled by the exponential growth of high-resolution electron cryo-microscopy (cryo-EM) structures. This trend will be further accelerated through the continuous software and method developments and the increasing availability of imaging centers, which will open cryo-EM to a wide array of researchers with their diverse scientific goals and questions. Especially for structural biology of membrane proteins, cryo-EM offers significant advantages as it can overcome multiple limitations of classical methods. Most importantly, in cryo-EM, the sample is prepared as a vitrified suspension, which abolishes the need for crystallization, reduces the required sample amount and allows usage of a wide arsenal of hydrophobic environments. Despite recent improvements, high-resolution cryo-EM still poses some significant challenges, and standardized procedures, especially for the characterization of membrane proteins, are missing. While there can be no ultimate recipe toward a high-resolution cryo-EM structure for every membrane protein, certain factors seem to be universally relevant. Here, we share the protocols that have been successfully used in our laboratory. We hope that this may be a useful resource to other researchers in the field and may increase their chances of success.

Mycobacterium tuberculosis ferritin: a suitable workhorse protein for cryo-EM development

The use of cryo-EM continues to expand worldwide and calls for good-quality standard proteins with simple protocols for their production. Here, a straightforward expression and purification protocol is presented that provides an apoferritin, bacterioferritin B (BfrB), from Mycobacterium tuberculosis with high yield and purity. A 2.12 Å resolution cryo-EM structure of BfrB is reported, showing the typical cage-like oligomer constituting of 24 monomers related by 432 symmetry. However, it also contains a unique C-terminal extension (164-181), which loops into the cage region of the shell and provides extra stability to the protein. Part of this region was ambiguous in previous crystal structures but could be built within the cryo-EM map. These findings and this protocol could serve the growing cryo-EM community in characterizing and pushing the limits of their electron microscopes and workflows.

Improving particle quality in cryo-EM analysis using a PEGylation method

Cryo-electron microscopy (cryo-EM) is widely used for structural biology studies and has been developed extensively in recent years. However, its sample vitrification process is a major limitation because it causes severe particle aggregation and/or denaturation. This effect is thought to occur because particles tend to stick to the "deadly" air-water interface during vitrification. Here, we report a method for PEGylation of proteins that can efficiently protect particles against such problems during vitrification. This method alleviates the laborious process of fine-tuning the vitrification conditions, allowing for analysis of samples that would otherwise be discarded.

Macromolecular refinement of X-ray and cryoelectron microscopy structures with Phenix/OPLS3e for improved structure and ligand quality

With the advent of the resolution revolution in cryoelectron microscopy (cryo-EM), low-resolution refinement is common, and likewise increases the need for a reliable force field. Here, we report on the incorporation of the OPLS3e force field with the VSGB2.1 solvation model in the structure determination package Phenix. Our results show significantly improved structure quality and reduced ligand strain at lower resolution for X-ray refinement. For refinement of cryo-EM-based structures, we find comparable quality structures, goodness-of-fit, and reduced ligand strain. We also show how structure quality and ligand strain are related to the map-model cross-correlation as a function of data weight, and how that can detect overfitting. Signs of overfitting are found in over half of our cryo-EM dataset, which can be remedied by a re-refinement at a lower data weight. Finally, a start-to-end script for refining structures with Phenix/OPLS3e is available in the Schrödinger 2020-3 distribution.

Beam image-shift accelerated data acquisition for near-atomic resolution single-particle cryo-electron tomography

Tomographic reconstruction of cryopreserved specimens imaged in an electron microscope followed by extraction and averaging of sub-volumes has been successfully used to derive atomic models of macromolecules in their biological environment. Eliminating biochemical isolation steps required by other techniques, this method opens up the cell to in-situ structural studies. However, the need to compensate for errors in targeting introduced during mechanical navigation of the specimen significantly slows down tomographic data collection thus limiting its practical value. Here, we introduce protocols for tilt-series acquisition and processing that accelerate data collection speed by up to an order of magnitude and improve map resolution compared to existing approaches. We achieve this by using beam-image shift to multiply the number of areas imaged at each stage position, by integrating geometrical constraints during imaging to achieve high precision targeting, and by performing per-tilt astigmatic CTF estimation and data-driven exposure weighting to improve final map resolution. We validated our beam image-shift electron cryo-tomography (BISECT) approach by determining the structure of a low molecular weight target (~300 kDa) at 3.6 Å resolution where density for individual side chains is clearly resolved.

Tomographic reconstructions of cryopreserved specimens enable in-situ structural studies. Here, the authors present the beam image-shift electron cryo-tomography (BISECT) approach that accelerates data collection speed and improves the map resolution compared to earlier approaches and present the in vitro structure of a 300 kDa protein complex that was solved at 3.6 Å resolution as a test case.

Advances in cryo-EM and ED with a cold-field emission beam and energy filtration -Refinements of the CRYO ARM 300 system in RIKEN SPring-8 center

We have designed and evaluated a cryo-electron microscopy (cryo-EM) system for higher-resolution single particle analysis and high-precision electron 3D crystallography. The system comprises a JEOL CRYO ARM 300 electron microscope-the first machine of this model-and a direct detection device camera, a scintillator-coupled camera, GPU clusters connected with a camera control computer and software for automated-data collection and efficient and accurate operation. The microscope provides parallel illumination of a highly coherent 300-kV electron beam to a sample from a cold-field emission gun and filters out energy-loss electrons through the sample with an in-column energy filter. The gun and filter are highly effective in improving imaging and diffraction, respectively, and have provided high quality data since July 2018. We here report on the characteristics of the cryo-EM system, updates, our progress and future plan for running such cryo-EM machines in RIKEN SPring-8 Center.

Structural interpretation of cryo-EM image reconstructions

The productivity of single-particle cryo-EM as a structure determination method has rapidly increased as many novel biological structures are being elucidated. The ultimate result of the cryo-EM experiment is an atomic model that should faithfully represent the computed image reconstruction. Although the principal approach of atomic model building and refinement from maps resembles that of the X-ray crystallographic methods, there are important differences due to the unique properties resulting from the 3D image reconstructions. In this review, we discuss the practiced work-flow from the cryo-EM image reconstruction to the atomic model. We give an overview of (i) resolution determination methods in cryo-EM including local and directional resolution variation, (ii) cryo-EM map contrast optimization including complementary map types that can help in identifying ambiguous density features, (iii) atomic model building and (iv) refinement in various resolution regimes including (v) their validation and (vi) discuss differences between X-ray and cryo-EM maps. Based on the methods originally developed for X-ray crystallography, the path from 3D image reconstruction to atomic coordinates has become an integral and important part of the cryo-EM structure determination work-flow that routinely delivers atomic models.

CASSPER is a semantic segmentation-based particle picking algorithm for single-particle cryo-electron microscopy

Particle identification and selection, which is a prerequisite for high-resolution structure determination of biological macromolecules via single-particle cryo-electron microscopy poses a major bottleneck for automating the steps of structure determination. Here, we present a generalized deep learning tool, CASSPER, for the automated detection and isolation of protein particles in transmission microscope images. This deep learning tool uses Semantic Segmentation and a collection of visually prepared training samples to capture the differences in the transmission intensities of protein, ice, carbon, and other impurities found in the micrograph. CASSPER is a semantic segmentation based method that does pixel-level classification and completely eliminates the need for manual particle picking. Integration of Contrast Limited Adaptive Histogram Equalization (CLAHE) in CASSPER enables high-fidelity particle detection in micrographs with variable ice thickness and contrast. A generalized CASSPER model works with high efficiency on unseen datasets and can potentially pick particles on-the-fly, enabling data processing automation.

SINGLE: Atomic-resolution structure identification of nanocrystals by graphene liquid cell EM

Analysis of the three-dimensional (3D) structures of nanocrystals with solution-phase transmission electron microscopy is beginning to reveal their unique physiochemical properties. We developed a "one-particle Brownian 3D reconstruction method" based on imaging of ensembles of colloidal nanocrystals using graphene liquid cell electron microscopy. Projection images of differently rotated nanocrystals are acquired using a direct electron detector with high temporal (<2.5 ms) resolution and analyzed to obtain an ensemble of 3D reconstructions. Here, we introduce computational methods required for successful atomic-resolution 3D reconstruction: (i) tracking of the individual particles throughout the time series, (ii) subtraction of the interfering background of the graphene liquid cell, (iii) identification and rejection of low-quality images, and (iv) tailored strategies for 2D/3D alignment and averaging that differ from those used in biological cryo-electron microscopy. Our developments are made available through the open-source software package SINGLE.

Solving the α-helix correspondence problem at medium-resolution Cryo-EM maps through modeling and 3D matching

Cryo-electron microscopy (cryo-EM) has recently emerged as a prominent biophysical method for macromolecular structure determination. Many research efforts have been devoted to produce cryo-EM images, density maps, at near-atomic resolution. Despite many advances in technology, the resolution of the generated density maps may not be sufficiently adequate and informative to directly construct the atomic structure of proteins. At medium-resolution (∼4-10 Å), secondary structure elements (α-helices and β-sheets) are discernible, whereas finding the correspondence of secondary structure elements detected in the density map with those on the sequence remains a challenging problem. In this paper, an automatic framework is proposed to solve α-helix correspondence problem in three-dimensional space. Through modeling of the sequence with the aid of a novel strategy, the α-helix correspondence problem is initially transformed into a complete weighted bipartite graph matching problem. An innovative correlation-based scoring function based on a well-known and robust statistical method is proposed for weighting the graph. Moreover, two local optimization algorithms, which are Greedy and Improved Greedy algorithms, have been presented to find α-helix correspondence. A widely used data set including 16 reconstructed and 4 experimental cryo-EM maps were chosen to verify the accuracy and reliability of the proposed automatic method. The experimental results demonstrate that the automatic method is highly efficient (86.25% accuracy), robust (11.3% error rate), fast (∼1.4 s), and works independently from cryo-EM skeleton.

Automated Single-Particle Reconstruction of Heterogeneous Inorganic Nanoparticles

Single-particle reconstruction can be used to perform three-dimensional (3D) imaging of homogeneous populations of nano-sized objects, in particular viruses and proteins. Here, it is demonstrated that it can also be used to obtain 3D reconstructions of heterogeneous populations of inorganic nanoparticles. An automated acquisition scheme in a scanning transmission electron microscope is used to collect images of thousands of nanoparticles. Particle images are subsequently semi-automatically clustered in terms of their properties and separate 3D reconstructions are performed from selected particle image clusters. The result is a 3D dataset that is representative of the full population. The study demonstrates a methodology that allows 3D imaging and analysis of inorganic nanoparticles in a fully automated manner that is truly representative of large particle populations.

Cryo-EM as a powerful tool for drug discovery

The recent revolution in cryo-EM has produced an explosion of structures at near-atomic or better resolution. This has allowed cryo-EM structures to provide visualization of bound small-molecule ligands in the macromolecules, and these new structures have provided unprecedented insights into the molecular mechanisms of complex biochemical processes. They have also had a profound impact on drug discovery, defining the binding modes and mechanisms of action of well-known drugs as well as driving the design and development of new compounds. This review will summarize and highlight some of these structures. Most excitingly, the latest cryo-EM technology has produced structures at 1.2 Å resolution, further solidifying cryo-EM as a powerful tool for drug discovery. Therefore, cryo-EM will play an ever-increasing role in drug discovery in the coming years.

SIMPLE 3.0. Stream single-particle cryo-EM analysis in real time

We here introduce the third major release of the SIMPLE (Single-particle IMage Processing Linux Engine) open-source software package for analysis of cryogenic transmission electron microscopy (cryo-EM) movies of single-particles (Single-Particle Analysis, SPA). Development of SIMPLE 3.0 has been focused on real-time data processing using minimal CPU computing resources to allow easy and cost-efficient scaling of processing as data rates escalate. Our stream SPA tool implements the steps of anisotropic motion correction and CTF estimation, rapid template-based particle identification and 2D clustering with automatic class rejection. SIMPLE 3.0 additionally features an easy-to-use web-based graphical user interface (GUI) that can be run on any device (workstation, laptop, tablet or phone) and supports a remote multi-user environment over the network. The new project-based execution model automatically records the executed workflow and represents it as a flow diagram in the GUI. This facilitates meta-data handling and greatly simplifies usage. Using SIMPLE 3.0, it is possible to automatically obtain a clean SP data set amenable to high-resolution 3D reconstruction directly upon completion of the data acquisition, without the need for extensive image processing post collection. Only minimal standard CPU computing resources are required to keep up with a rate of ∼300 Gatan K3 direct electron detector movies per hour. SIMPLE 3.0 is available for download from simplecryoem.com.

Application of Cryogenic Transmission Electron Microscopy for Evaluation of Vaccine Delivery Carriers

Cryogenic transmission electron microscopy (Cryo-TEM) enables visualizing the physicochemical structure of nanocarriers in solution. Here, we demonstrate the typical applications of Cryo-TEM in characterizing archaeosome-based vesicles as antigen carriers, including the morphology and size of vaccine carriers. Cryo-TEM tomography, incorporated with immunogold labeling for identifying and localizing the antigens, reveals the antigen distribution within archaeosomes in three dimensions (3D).

Pre-pro is a fast pre-processor for single-particle cryo-EM by enhancing 2D classification

2D classification plays a pivotal role in analyzing single particle cryo-electron microscopy images. Here, we introduce a simple and loss-less pre-processor that incorporates a fast dimension-reduction (2SDR) de-noiser to enhance 2D classification. By implementing this 2SDR pre-processor prior to a representative classification algorithm like RELION and ISAC, we compare the performances with and without the pre-processor. Tests on multiple cryo-EM experimental datasets show the pre-processor can make classification faster, improve yield of good particles and increase the number of class-average images to generate better initial models. Testing on the nanodisc-embedded TRPV1 dataset with high heterogeneity using a 3D reconstruction workflow with an initial model from class-average images highlights the pre-processor improves the final resolution to 2.82 Å, close to 0.9 Nyquist. Those findings and analyses suggest the 2SDR pre-processor, of minimal cost, is widely applicable for boosting 2D classification, while its generalization to accommodate neural network de-noisers is envisioned.

Structural impact of K63 ubiquitin on yeast translocating ribosomes under oxidative stress

Subpopulations of ribosomes are responsible for fine tuning the control of protein synthesis in dynamic environments. K63 ubiquitination of ribosomes has emerged as a new posttranslational modification that regulates protein synthesis during cellular response to oxidative stress. K63 ubiquitin, a type of ubiquitin chain that functions independently of the proteasome, modifies several sites at the surface of the ribosome, however, we lack a molecular understanding on how this modification affects ribosome structure and function. Using cryoelectron microscopy (cryo-EM), we resolved the first three-dimensional (3D) structures of K63 ubiquitinated ribosomes from oxidatively stressed yeast cells at 3.5-3.2 Å resolution. We found that K63 ubiquitinated ribosomes are also present in a polysome arrangement, similar to that observed in yeast polysomes, which we determined using cryoelectron tomography (cryo-ET). We further showed that K63 ubiquitinated ribosomes are captured uniquely at the rotated pretranslocation stage of translation elongation. In contrast, cryo-EM structures of ribosomes from mutant cells lacking K63 ubiquitin resolved at 4.4-2.7 Å showed 80S ribosomes represented in multiple states of translation, suggesting that K63 ubiquitin regulates protein synthesis at a selective stage of elongation. Among the observed structural changes, ubiquitin mediates the destabilization of proteins in the 60S P-stalk and in the 40S beak, two binding regions of the eukaryotic elongation factor eEF2. These changes would impact eEF2 function, thus, inhibiting translocation. Our findings help uncover the molecular effects of K63 ubiquitination on ribosomes, providing a model of translation control during oxidative stress, which supports elongation halt at pretranslocation.

1.8 Å resolution structure of β-galactosidase with a 200 kV CRYO ARM electron microscope

We report the determination of the structure of Escherichia coli β-galactosidase at a resolution of ∼1.8 Å using data collected on a 200 kV CRYO ARM microscope equipped with a K3 direct electron detector. The data were collected in a single 24 h session by recording images from an array of 7 × 7 holes at each stage position using the automated data collection program SerialEM. In addition to the expected features such as holes in the densities of aromatic residues, the map also shows density bumps corresponding to the locations of hydrogen atoms. The hydrogen densities are useful in assigning absolute orientations for residues such as glutamine or asparagine by removing the uncertainty in the fitting of the amide groups, and are likely to be especially relevant in the context of structure-guided drug design. These findings validate the use of electron microscopes operating at 200 kV for imaging protein complexes at atomic resolution using cryo-EM.

Computational Methods for Single-Particle Electron Cryomicroscopy

Single-particle electron cryomicroscopy (cryo-EM) is an increasingly popular technique for elucidating the three-dimensional structure of proteins and other biologically significant complexes at near-atomic resolution. It is an imaging method that does not require crystallization and can capture molecules in their native states. In single-particle cryo-EM, the three-dimensional molecular structure needs to be determined from many noisy two-dimensional tomographic projections of individual molecules, whose orientations and positions are unknown. The high level of noise and the unknown pose parameters are two key elements that make reconstruction a challenging computational problem. Even more challenging is the inference of structural variability and flexible motions when the individual molecules being imaged are in different conformational states. This review discusses computational methods for structure determination by single-particle cryo-EM and their guiding principles from statistical inference, machine learning, and signal processing that also play a significant role in many other data science applications.

Low-dose phase retrieval of biological specimens using cryo-electron ptychography

Cryo-electron microscopy is an essential tool for high-resolution structural studies of biological systems. This method relies on the use of phase contrast imaging at high defocus to improve information transfer at low spatial frequencies at the expense of higher spatial frequencies. Here we demonstrate that electron ptychography can recover the phase of the specimen with continuous information transfer across a wide range of the spatial frequency spectrum, with improved transfer at lower spatial frequencies, and as such is more efficient for phase recovery than conventional phase contrast imaging. We further show that the method can be used to study frozen-hydrated specimens of rotavirus double-layered particles and HIV-1 virus-like particles under low-dose conditions (5.7 e/Å2) and heterogeneous objects in an Adenovirus-infected cell over large fields of view (1.14 × 1.14 μm), thus making it suitable for studies of many biologically important structures.

Could Egg White Lysozyme be Solved by Single Particle Cryo-EM?

The combination of high-end cryogenic transmission electron microscopes (cryo-EM), direct electron detectors, and advanced image algorithms allows researchers to obtain the 3D structures of much smaller macromolecules than years ago. However, there are still major challenges for the single-particle cryo-EM method to achieve routine structure determinations for macromolecules much smaller than 100 kDa, which are the majority of all plant and animal proteins. These challenges include sample characteristics such as sample heterogeneity, beam damage, ice layer thickness, stability, and quality, as well as hardware limitations such as detector performance, beam, and phase plate quality. Here, single particle data sets were simulated for samples that were ideal in terms of homogeneity, distribution, and stability, but with realistic parameters for ice layer, dose, detector performance, and beam characteristics. Reference data were calculated for human apo-ferritin using identical parameters reported for an experimental data set downloaded from EMPIAR. Processing of the simulated data set resulted in a value of 1.86 Å from 20 214 particles, similar to a 2 Å density map obtained from 29 224 particles selected from real micrographs. Simulated data sets were then generated for a 14 kDa protein, hen egg white lysozyme (HEWL), with and without an ideal phase plate (PP). Whereas we could not obtain a high-resolution 3D reconstruction of HEWL for the data set without PP, the one with PP resulted in a 2.78 Å resolution density map from 225 751 particles. Our simulator and simulations could help in pushing the size limits of cryo-EM.

GemSpot: A Pipeline for Robust Modeling of Ligands into Cryo-EM Maps

Producing an accurate atomic model of biomolecule-ligand interactions from maps generated by cryoelectron microscopy (cryo-EM) often presents challenges inherent to the methodology and the dynamic nature of ligand binding. Here, we present GemSpot, an automated pipeline of computational chemistry methods that take into account EM map potentials, quantum mechanics energy calculations, and water molecule site prediction to generate candidate poses and provide a measure of the degree of confidence. The pipeline is validated through several published cryo-EM structures of complexes in different resolution ranges and various types of ligands. In all cases, at least one identified pose produced both excellent interactions with the target and agreement with the map. GemSpot will be valuable for the robust identification of ligand poses and drug discovery efforts through cryo-EM.