Maximum-likelihood multi-reference refinement for electron microscopy images

Maize Streak Virus: Single and Gemini Capsid Architecture

Geminiviridae are ssDNA plant viruses whose control has both economical and agricultural importance. Their capsids assemble into two distinct architectural forms: (i) a T = 1 icosahedral and (ii) a unique twinned quasi-isometric capsid. Described here are the high-resolution structures of both forms of the maize streak virus using cryo-EM. A comparison of these two forms provides details of the coat protein (CP) and CP-CP and CP-genome interactions that govern the assembly of the architecture of the capsids. Comparative analysis of other representative members of Geminiviridae reveals structural conservation of 60-95% compared to a sequence similarity of 21-30%. This study provides a structural atlas of these plant pathogens and suggests possible antiviral-targetable regions of these capsids.

Focused classifications and refinements in high-resolution single particle cryo-EM analysis

Recent advances in cryo electron microscopy (cryo-EM) and image processing provide new opportunities to analyse drug targets at high resolution. However, structural heterogeneity limits resolution in many practical cases, hence restricting the level at which structural details can be analysed and drug design be performed. As structural disorder is not spread throughout the entire structure of a given macromolecular complex but instead is found in certain regions that move with respect to others and covering molecular scales from domain conformational changes up to the level of side chain conformations in ligand binding pockets, it is possible to focus the attention on those regions and the associated relative movements. Here we show how the usage of focused classifications and refinements provide insights into global conformational arrangements, exemplified on the human ribosome and on the cannabinoid G protein coupled receptor (GPCR), and how they can improve the local map resolution from an essentially disordered region to the 3-4 Å and finally to the 2 Å resolution range. A systematic analysis with variable spherical masks during focused refinement is presented showing that the choice of an optimal mask size helps refining to high resolution. This study covers several practical approaches on 4 examples illustrating how important mask size & shape and including neighbouring structural elements are for a focused analysis of a macromolecular complex. Such methods will be crucial for cryo-EM structure-based drug design of various medical targets and are applicable to single particle cryo-EM and electron tomography data.

Detecting continuous structural heterogeneity in single-molecule localization microscopy data

Fusion of multiple chemically identical complexes, so-called particles, in localization microscopy, can improve the signal-to-noise ratio and overcome under-labeling. To this end, structural homogeneity of the data must be assumed. Biological heterogeneity, however, could be present in the data originating from distinct conformational variations or (continuous) variations in particle shapes. We present a prior-knowledge-free method for detecting continuous structural variations with localization microscopy. Detecting this heterogeneity leads to more faithful fusions and reconstructions of the localization microscopy data as their heterogeneity is taken into account. In experimental datasets, we show the continuous variation of the height of DNA origami tetrahedrons imaged with 3D PAINT and of the radius of Nuclear Pore Complexes imaged in 2D with STORM. In simulation, we study the impact on the heterogeneity detection pipeline of Degree Of Labeling and of structural variations in the form of two independent modes.

Advances in computational approaches to structure determination of alphaviruses and flaviviruses using cryo-electron microscopy

Advancements in the field of cryo-electron microscopy (cryo-EM) have greatly contributed to our current understanding of virus structures and life cycles. In this review, we discuss the application of single particle cryo-electron microscopy (EM) for the structure elucidation of small enveloped icosahedral viruses, namely, alpha- and flaviviruses. We focus on technical advances in cryo-EM data collection, image processing, three-dimensional reconstruction, and refinement strategies for obtaining high-resolution structures of these viruses. Each of these developments enabled new insights into the alpha- and flavivirus architecture, leading to a better understanding of their biology, pathogenesis, immune response, immunogen design, and therapeutic development.

Unsupervised Cryo-EM Images Denoising and Clustering Based on Deep Convolutional Autoencoder and K-Means+

Cryo-electron microscopy (cryo-EM) is a widely used structural determination technique. Because of the extremely low signal-to-noise ratio (SNR) of images captured by cryo-EM, clustering single-particle cryo-EM images with high accuracy is challenging. To address this, we proposed an iterative denoising and clustering method based on a deep convolutional variational autoencoder and K-means++. The proposed method contains two modules: a denoising ResNet variational autoencoder (DRVAE), and Balance size K-means++ (BSK-means++). First, the DRVAE is trained in a fully unsupervised manner to initialize the neural network and obtain preliminary denoised images. Second, BSK-means++ is built for clustering denoised images, and images closer to class centers are divided into reliable samples. Third, the training of DRVAE is continued, while the class-average images are used as pseudo supervision of reliable samples to reserve more detailed information of denoised images. Finally, the second and third steps mentioned above can be performed jointly and iteratively until convergence occurs. The experimental results showed that the proposed method can generate reliable class average images and achieve better clustering accuracy and normalized mutual information than current methods. This study confirmed that DRVAE with BSK-means++ could achieve a good denoise performance on single-particle cryo-EM images, which can help researchers obtain information such as symmetry and heterogeneity of the target particles. In addition, the proposed method avoids the extreme imbalance of class size, which improves the reliability of the clustering result.

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.

Structure of an Intranucleosomal DNA Loop That Senses DNA Damage during Transcription

Transcription through chromatin by RNA polymerase II (Pol II) is accompanied by the formation of small intranucleosomal DNA loops containing the enzyme (i-loops) that are involved in survival of core histones on the DNA and arrest of Pol II during the transcription of damaged DNA. However, the structures of i-loops have not been determined. Here, the structures of the intermediates formed during transcription through a nucleosome containing intact or damaged DNA were studied using biochemical approaches and electron microscopy. After RNA polymerase reaches position +24 from the nucleosomal boundary, the enzyme can backtrack to position +20, where DNA behind the enzyme recoils on the surface of the histone octamer, forming an i-loop that locks Pol II in the arrested state. Since the i-loop is formed more efficiently in the presence of SSBs positioned behind the transcribing enzyme, the loop could play a role in the transcription-coupled repair of DNA damage hidden in the chromatin structure.

Classification of diffraction patterns using a convolutional neural network in single-particle-imaging experiments performed at X-ray free-electron lasers

Single particle imaging (SPI) at X-ray free-electron lasers is particularly well suited to determining the 3D structure of particles at room temperature. For a successful reconstruction, diffraction patterns originating from a single hit must be isolated from a large number of acquired patterns. It is proposed that this task could be formulated as an image-classification problem and solved using convolutional neural network (CNN) architectures. Two CNN configurations are developed: one that maximizes the F1 score and one that emphasizes high recall. The CNNs are also combined with expectation-maximization (EM) selection as well as size filtering. It is observed that the CNN selections have lower contrast in power spectral density functions relative to the EM selection used in previous work. However, the reconstruction of the CNN-based selections gives similar results. Introducing CNNs into SPI experiments allows the reconstruction pipeline to be streamlined, enables researchers to classify patterns on the fly, and, as a consequence, enables them to tightly control the duration of their experiments. Incorporating non-standard artificial-intelligence-based solutions into an existing SPI analysis workflow may be beneficial for the future development of SPI experiments.

Heterogeneous cryo-EM projection image classification using a two-stage spectral clustering based on novel distance measures

Single-particle cryo-electron microscopy (cryo-EM) has become one of the mainstream technologies in the field of structural biology to determine the three-dimensional (3D) structures of biological macromolecules. Heterogeneous cryo-EM projection image classification is an effective way to discover conformational heterogeneity of biological macromolecules in different functional states. However, due to the low signal-to-noise ratio of the projection images, the classification of heterogeneous cryo-EM projection images is a very challenging task. In this paper, two novel distance measures between projection images integrating the reliability of common lines, pixel intensity and class averages are designed, and then a two-stage spectral clustering algorithm based on the two distance measures is proposed for heterogeneous cryo-EM projection image classification. In the first stage, the novel distance measure integrating common lines and pixel intensities of projection images is used to obtain preliminary classification results through spectral clustering. In the second stage, another novel distance measure integrating the first novel distance measure and class averages generated from each group of projection images is used to obtain the final classification results through spectral clustering. The proposed two-stage spectral clustering algorithm is applied on a simulated and a real cryo-EM dataset for heterogeneous reconstruction. Results show that the two novel distance measures can be used to improve the classification performance of spectral clustering, and using the proposed two-stage spectral clustering algorithm can achieve higher classification and reconstruction accuracy than using RELION and XMIPP.

Machine Learning for Structure Determination in Single-Particle Cryo-Electron Microscopy: A Systematic Review

Recently, single-particle cryo-electron microscopy (cryo-EM) has become an indispensable method for determining macromolecular structures at high resolution to deeply explore the relevant molecular mechanism. Its recent breakthrough is mainly because of the rapid advances in hardware and image processing algorithms, especially machine learning. As an essential support of single-particle cryo-EM, machine learning has powered many aspects of structure determination and greatly promoted its development. In this article, we provide a systematic review of the applications of machine learning in this field. Our review begins with a brief introduction of single-particle cryo-EM, followed by the specific tasks and challenges of its image processing. Then, focusing on the workflow of structure determination, we describe relevant machine learning algorithms and applications at different steps, including particle picking, 2-D clustering, 3-D reconstruction, and other steps. As different tasks exhibit distinct characteristics, we introduce the evaluation metrics for each task and summarize their dynamics of technology development. Finally, we discuss the open issues and potential trends in this promising field.

Electron microscopy analysis of ATP-independent nucleosome unfolding by FACT

FACT is a histone chaperone that participates in nucleosome removal and reassembly during transcription and replication. We used electron microscopy to study FACT, FACT:Nhp6 and FACT:Nhp6:nucleosome complexes, and found that all complexes adopt broad ranges of configurations, indicating high flexibility. We found unexpectedly that the DNA binding protein Nhp6 also binds to the C-terminal tails of FACT subunits, inducing more open geometries of FACT even in the absence of nucleosomes. Nhp6 therefore supports nucleosome unfolding by altering both the structure of FACT and the properties of nucleosomes. Complexes formed with FACT, Nhp6, and nucleosomes also produced a broad range of structures, revealing a large number of potential intermediates along a proposed unfolding pathway. The data suggest that Nhp6 has multiple roles before and during nucleosome unfolding by FACT, and that the process proceeds through a series of energetically similar intermediate structures, ultimately leading to an extensively unfolded form.

Sivkina et al. present a biochemical and biophysical characterization of the interaction of S. cerevisiae histone chaperone FACT with the nucleosome core particle. They show that FACT adopts a more open geometry in the presence of Nhp6, and together they unfold nucleosomes to an almost extended conformation, suggesting a mechanism for FACT-facilitated disassembly of nucleosomes.

Wavelet invariants for statistically robust multi-reference alignment

We propose a nonlinear, wavelet-based signal representation that is translation invariant and robust to both additive noise and random dilations. Motivated by the multi-reference alignment problem and generalizations thereof, we analyze the statistical properties of this representation given a large number of independent corruptions of a target signal. We prove the nonlinear wavelet-based representation uniquely defines the power spectrum but allows for an unbiasing procedure that cannot be directly applied to the power spectrum. After unbiasing the representation to remove the effects of the additive noise and random dilations, we recover an approximation of the power spectrum by solving a convex optimization problem, and thus reduce to a phase retrieval problem. Extensive numerical experiments demonstrate the statistical robustness of this approximation procedure.

Deep learning-based mixed-dimensional Gaussian mixture model for characterizing variability in cryo-EM

Structural flexibility and/or dynamic interactions with other molecules is a critical aspect of protein function. CryoEM provides direct visualization of individual macromolecules sampling different conformational and compositional states. While numerous methods are available for computational classification of discrete states, characterization of continuous conformational changes or large numbers of discrete state without human supervision remains challenging. Here we present e2gmm, a machine learning algorithm to determine a conformational landscape for proteins or complexes using a 3-D Gaussian mixture model mapped onto 2-D particle images in known orientations. Using a deep neural network architecture, e2gmm can automatically resolve the structural heterogeneity within the protein complex and map particles onto a small latent space describing conformational and compositional changes. This system presents a more intuitive and flexible representation than other manifold methods currently in use. We demonstrate this method on both simulated data as well as three biological systems, to explore compositional and conformational changes at a range of scales. The software is distributed as part of EMAN2.

DeepAlign, a 3D alignment method based on regionalized deep learning for Cryo-EM

Cryo Electron Microscopy (Cryo-EM) is currently one of the main tools to reveal the structural information of biological specimens at high resolution. Despite the great development of the techniques involved to solve the biological structures with Cryo-EM in the last years, the reconstructed 3D maps can present lower resolution due to errors committed while processing the information acquired by the microscope. One of the main problems comes from the 3D alignment step, which is an error-prone part of the reconstruction workflow due to the very low signal-to-noise ratio (SNR) common in Cryo-EM imaging. In fact, as we will show in this work, it is not unusual to find a disagreement in the alignment parameters in approximately 20-40% of the processed images, when outputs of different alignment algorithms are compared. In this work, we present a novel method to align sets of single particle images in the 3D space, called DeepAlign. Our proposal is based on deep learning networks that have been successfully used in plenty of problems in image classification. Specifically, we propose to design several deep neural networks on a regionalized basis to classify the particle images in sub-regions and, then, make a refinement of the 3D alignment parameters only inside that sub-region. We show that this method results in accurately aligned images, improving the Fourier shell correlation (FSC) resolution obtained with other state-of-the-art methods while decreasing computational time.

A Three-Dimensional Model of the Yeast Transmembrane Sensor Wsc1 Obtained by SMA-Based Detergent-Free Purification and Transmission Electron Microscopy

The cell wall sensor Wsc1 belongs to a small family of transmembrane proteins, which are crucial to sustain cell integrity in yeast and other fungi. Wsc1 acts as a mechanosensor of the cell wall integrity (CWI) signal transduction pathway which responds to external stresses. Here we report on the purification of Wsc1 by its trapping in water-soluble polymer-stabilized lipid nanoparticles, obtained with an amphipathic styrene-maleic acid (SMA) copolymer. The latter was employed to transfer tagged sensors from their native yeast membranes into SMA/lipid particles (SMALPs), which allows their purification in a functional state, i.e., avoiding denaturation. The SMALPs composition was characterized by fluorescence correlation spectroscopy, followed by two-dimensional image acquisition from single particle transmission electron microscopy to build a three-dimensional model of the sensor. The latter confirms that Wsc1 consists of a large extracellular domain connected to a smaller intracellular part by a single transmembrane domain, which is embedded within the hydrophobic moiety of the lipid bilayer. The successful extraction of a sensor from the yeast plasma membrane by a detergent-free procedure into a native-like membrane environment provides new prospects for in vitro structural and functional studies of yeast plasma proteins which are likely to be applicable to other fungi, including plant and human pathogens.

Algorithmic robustness to preferred orientations in single particle analysis by CryoEM

The presence of preferred orientations in single particle analysis (SPA) by cryo-Electron Microscopy (cryoEM) is currently one of the hurdles preventing many structural analyses from yielding high-resolution structures. Although the existence of preferred orientations is mostly related to the grid preparation, in this technical note, we show that some image processing algorithms used for angular assignment and three-dimensional (3D) reconstruction are more robust than others to these detrimental conditions. We exemplify this argument with three different data sets in which the presence of preferred orientations hindered achieving a 3D reconstruction without artifacts or, even worse, a 3D reconstruction could never be achieved.

Exploiting prior knowledge about biological macromolecules in cryo-EM structure determination

Three-dimensional reconstruction of the electron-scattering potential of biological macromolecules from electron cryo-microscopy (cryo-EM) projection images is an ill-posed problem. The most popular cryo-EM software solutions to date rely on a regularization approach that is based on the prior assumption that the scattering potential varies smoothly over three-dimensional space. Although this approach has been hugely successful in recent years, the amount of prior knowledge that it exploits compares unfavorably with the knowledge about biological structures that has been accumulated over decades of research in structural biology. Here, a regularization framework for cryo-EM structure determination is presented that exploits prior knowledge about biological structures through a convolutional neural network that is trained on known macromolecular structures. This neural network is inserted into the iterative cryo-EM structure-determination process through an approach that is inspired by regularization by denoising. It is shown that the new regularization approach yields better reconstructions than the current state of the art for simulated data, and options to extend this work for application to experimental cryo-EM data are discussed.

Selecting XFEL single-particle snapshots by geometric machine learning

A promising new route for structural biology is single-particle imaging with an X-ray Free-Electron Laser (XFEL). This method has the advantage that the samples do not require crystallization and can be examined at room temperature. However, high-resolution structures can only be obtained from a sufficiently large number of diffraction patterns of individual molecules, so-called single particles. Here, we present a method that allows for efficient identification of single particles in very large XFEL datasets, operates at low signal levels, and is tolerant to background. This method uses supervised Geometric Machine Learning (GML) to extract low-dimensional feature vectors from a training dataset, fuse test datasets into the feature space of training datasets, and separate the data into binary distributions of "single particles" and "non-single particles." As a proof of principle, we tested simulated and experimental datasets of the Coliphage PR772 virus. We created a training dataset and classified three types of test datasets: First, a noise-free simulated test dataset, which gave near perfect separation. Second, simulated test datasets that were modified to reflect different levels of photon counts and background noise. These modified datasets were used to quantify the predictive limits of our approach. Third, an experimental dataset collected at the Stanford Linear Accelerator Center. The single-particle identification for this experimental dataset was compared with previously published results and it was found that GML covers a wide photon-count range, outperforming other single-particle identification methods. Moreover, a major advantage of GML is its ability to retrieve single particles in the presence of structural variability.

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.

An advanced workflow for single-particle imaging with the limited data at an X-ray free-electron laser

An improved analysis for single-particle imaging (SPI) experiments, using the limited data, is presented here. Results are based on a study of bacteriophage PR772 performed at the Atomic, Molecular and Optical Science instrument at the Linac Coherent Light Source as part of the SPI initiative. Existing methods were modified to cope with the shortcomings of the experimental data: inaccessibility of information from half of the detector and a small fraction of single hits. The general SPI analysis workflow was upgraded with the expectation-maximization based classification of diffraction patterns and mode decomposition on the final virus-structure determination step. The presented processing pipeline allowed us to determine the 3D structure of bacteriophage PR772 without symmetry constraints with a spatial resolution of 6.9 nm. The obtained resolution was limited by the scattering intensity during the experiment and the relatively small number of single hits.

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.

Structural basis of bacterial σ28 -mediated transcription reveals roles of the RNA polymerase zinc-binding domain

In bacteria, σ²⁸ is the flagella-specific sigma factor that targets RNA polymerase (RNAP) to control the expression of flagella-related genes involving bacterial motility and chemotaxis. However, the structural mechanism of σ²⁸ -dependent promoter recognition remains uncharacterized. Here, we report cryo-EM structures of E. coli σ²⁸ -dependent transcribing complexes on a complete flagella-specific promoter. These structures reveal how σ²⁸ -RNAP recognizes promoter DNA through strong interactions with the -10 element, but weak contacts with the -35 element, to initiate transcription. In addition, we observed a distinct architecture in which the β' zinc-binding domain (ZBD) of RNAP stretches out from its canonical position to interact with the upstream non-template strand. Further in vitro and in vivo assays demonstrate that this interaction has the overall effect of facilitating closed-to-open isomerization of the RNAP-promoter complex by compensating for the weak interaction between σ4 and -35 element. This suggests that ZBD relocation may be a general mechanism employed by σ⁷⁰ family factors to enhance transcription from promoters with weak σ4/-35 element interactions.

Visualization of two architectures in class-II CAP-dependent transcription activation

Transcription activation by cyclic AMP (cAMP) receptor protein (CAP) is the classic paradigm of transcription regulation in bacteria. CAP was suggested to activate transcription on class-II promoters via a recruitment and isomerization mechanism. However, whether and how it modifies RNA polymerase (RNAP) to initiate transcription remains unclear. Here, we report cryo–electron microscopy (cryo-EM) structures of an intact Escherichia coli class-II CAP-dependent transcription activation complex (CAP-TAC) with and without de novo RNA transcript. The structures reveal two distinct architectures of TAC and raise the possibility that CAP binding may induce substantial conformational changes in all the subunits of RNAP and transiently widen the main cleft of RNAP to facilitate DNA promoter entering and formation of the initiation open complex. These structural changes vanish during further RNA transcript synthesis. The observations in this study may reveal a possible on-pathway intermediate and suggest a possibility that CAP activates transcription by inducing intermediate state, in addition to the previously proposed stabilization mechanism.

Cryo-EM structures of transcription activation complexes comprising cyclic AMP receptor protein (CAP) bound to a class-II promoter reveal two distinct architectures and suggest a possibility that CAP activates transcription by inducing an intermediate state, an important supplement to the previously proposed stabilization mechanism.

Single-particle cryo-electron microscopy: Mathematical theory, computational challenges, and opportunities

In recent years, an abundance of new molecular structures have been elucidated using cryo-electron microscopy (cryo-EM), largely due to advances in hardware technology and data processing techniques. Owing to these new exciting developments, cryo-EM was selected by Nature Methods as Method of the Year 2015, and the Nobel Prize in Chemistry 2017 was awarded to three pioneers in the field. The main goal of this article is to introduce the challenging and exciting computational tasks involved in reconstructing 3-D molecular structures by cryo-EM. Determining molecular structures requires a wide range of computational tools in a variety of fields, including signal processing, estimation and detection theory, high-dimensional statistics, convex and non-convex optimization, spectral algorithms, dimensionality reduction, and machine learning. The tools from these fields must be adapted to work under exceptionally challenging conditions, including extreme noise levels, the presence of missing data, and massively large datasets as large as several Terabytes. In addition, we present two statistical models: multi-reference alignment and multi-target detection, that abstract away much of the intricacies of cryo-EM, while retaining some of its essential features. Based on these abstractions, we discuss some recent intriguing results in the mathematical theory of cryo-EM, and delineate relations with group theory, invariant theory, and information theory.

SrmB Rescues Trapped Ribosome Assembly Intermediates

RNA helicases play various roles in ribosome biogenesis depending on the ribosome assembly pathway and stress state of the cell. However, it is unclear how most RNA helicases interact with ribosome assembly intermediates or participate in other cell processes to regulate ribosome assembly. SrmB is a DEAD-box helicase that acts early in the ribosome assembly process, although very little is known about its mechanism of action. Here, we use a combined quantitative mass spectrometry/cryo-electron microscopy approach to detail the protein inventory, rRNA modification state, and structures of 40S ribosomal intermediates that form upon SrmB deletion. We show that the binding site of SrmB is unperturbed by SrmB deletion, but the peptidyl transferase center, the uL7/12 stalk, and 30S contact sites all show severe assembly defects. Taking into account existing data on SrmB function and the experiments presented here, we propose several mechanisms by which SrmB could guide assembling particles from kinetic traps to competent subunits during the 50S ribosome assembly process.

Filling Adeno-Associated Virus Capsids: Estimating Success by Cryo-Electron Microscopy

Adeno-associated viruses (AAVs) have been employed successfully as gene therapy vectors in treating various genetic diseases for almost two decades. However, transgene packaging is usually imperfect, and developing a rapid and accurate method for measuring the proportion of DNA encapsidation is an important step for improving the downstream process of large scale vector production. In this study, we used two-dimensional class averages and three-dimensional classes, intermediate outputs in the single particle cryo-electron microscopy (cryo-EM) image reconstruction pipeline, to determine the proportion of DNA-packaged and empty capsid populations. Two different preparations of AAV3 were analyzed to estimate the minimum number of particles required to be sampled by cryo-EM in order for robust calculation of the proportion of the full versus empty capsids in any given sample. Cost analysis applied to the minimum amount of data required for a valid ratio suggests that cryo-EM is an effective approach to analyze vector preparations.

Heterogeneous multireference alignment for images with application to 2-D classification in single particle reconstruction

Motivated by the task of 2-D classification in single particle reconstruction by cryo-electron microscopy (cryo-EM), we consider the problem of heterogeneous multireference alignment of images. In this problem, the goal is to estimate a (typically small) set of target images from a (typically large) collection of observations. Each observation is a rotated, noisy version of one of the target images. For each individual observation, neither the rotation nor which target image has been rotated are known. As the noise level in cryo-EM data is high, clustering the observations and estimating individual rotations is challenging. We propose a framework to estimate the target images directly from the observations, completely bypassing the need to cluster or register the images. The framework consists of two steps. First, we estimate rotation-invariant features of the images, such as the bispectrum. These features can be estimated to any desired accuracy, at any noise level, provided sufficiently many observations are collected. Then, we estimate the images from the invariant features. Numerical experiments on synthetic cryo-EM datasets demonstrate the effectiveness of the method. Ultimately, we outline future developments required to apply this method to experimental data.

Cryo-EM structure and dynamics of eukaryotic DNA polymerase δ holoenzyme

DNA polymerase δ (Polδ) plays pivotal roles in eukaryotic DNA replication and repair. Polδ is conserved from yeast to humans, and mutations in human Polδ have been implicated in various cancers. Saccharomyces cerevisiae Polδ consists of catalytic Pol3 and the regulatory Pol31 and Pol32 subunits. Here, we present the near atomic resolution (3.2 Å) cryo-EM structure of yeast Polδ holoenzyme in the act of DNA synthesis. The structure reveals an unexpected arrangement in which the regulatory subunits (Pol31 and Pol32) lie next to the exonuclease domain of Pol3 but do not engage the DNA. The Pol3 C-terminal domain contains a 4Fe-4S cluster and emerges as the keystone of Polδ assembly. We also show that the catalytic and regulatory subunits rotate relative to each other and that this is an intrinsic feature of the Polδ architecture. Collectively, the structure provides a framework for understanding DNA transactions at the replication fork.

Flexible workflows for on-the-fly electron-microscopy single-particle image processing using Scipion

Electron microscopy of macromolecular structures is an approach that is in increasing demand in the field of structural biology. The automation of image acquisition has greatly increased the potential throughput of electron microscopy. Here, the focus is on the possibilities in Scipion to implement flexible and robust image-processing workflows that allow the electron-microscope operator and the user to monitor the quality of image acquisition, assessing very simple acquisition measures or obtaining a first estimate of the initial volume, or the data resolution and heterogeneity, without any need for programming skills. These workflows can implement intelligent automatic decisions and they can warn the user of possible acquisition failures. These concepts are illustrated by analysis of the well known 2.2 Å resolution β-galactosidase data set.

Coherent diffractive imaging of microtubules using an X-ray laser

X-ray free electron lasers (XFELs) create new possibilities for structural studies of biological objects that extend beyond what is possible with synchrotron radiation. Serial femtosecond crystallography has allowed high-resolution structures to be determined from micro-meter sized crystals, whereas single particle coherent X-ray imaging requires development to extend the resolution beyond a few tens of nanometers. Here we describe an intermediate approach: the XFEL imaging of biological assemblies with helical symmetry. We collected X-ray scattering images from samples of microtubules injected across an XFEL beam using a liquid microjet, sorted these images into class averages, merged these data into a diffraction pattern extending to 2 nm resolution, and reconstructed these data into a projection image of the microtubule. Details such as the 4 nm tubulin monomer became visible in this reconstruction. These results illustrate the potential of single-molecule X-ray imaging of biological assembles with helical symmetry at room temperature.

Engineering the PP7 Virus Capsid as a Peptide Display Platform

As self-assembling polyvalent nanoscale structures that can tolerate substantial genetic and chemical modification, virus-like particles are useful in a variety of fields. Here we describe the genetic modification and structural characterization of the Leviviridae PP7 capsid protein as a platform for the presentation of functional polypeptides. This particle was shown to tolerate the display of sequences from 1 kDa (a cell penetrating peptide) to 14 kDa (the Fc-binding double Z-domain) on its exterior surface as C-terminal genetic fusions to the coat protein. In addition, a dimeric construct allowed the presentation of exogenous loops between capsid monomers and the simultaneous presentation of two different peptides at different positions on the icosahedral structure. The PP7 particle is thereby significantly more tolerant of these types of polypeptide additions than Qβ and MS2, the other Leviviridae-derived VLPs in common use.

Robustness of signal detection in cryo-electron microscopy via a bi-objective-function approach

Background

The detection of weak signals and selection of single particles from low-contrast micrographs of frozen hydrated biomolecules by cryo-electron microscopy (cryo-EM) represents a major practical bottleneck in cryo-EM data analysis. Template-based particle picking by an objective function using fast local correlation (FLC) allows computational extraction of a large number of candidate particles from micrographs. Another independent objective function based on maximum likelihood estimates (MLE) can be used to align the images and verify the presence of a signal in the selected particles. Despite the widespread applications of the two objective functions, an optimal combination of their utilities has not been exploited. Here we propose a bi-objective function (BOF) approach that combines both FLC and MLE and explore the potential advantages and limitations of BOF in signal detection from cryo-EM data.

Results

The robustness of the BOF strategy in particle selection and verification was systematically examined with both simulated and experimental cryo-EM data. We investigated how the performance of the BOF approach is quantitatively affected by the signal-to-noise ratio (SNR) of cryo-EM data and by the choice of initialization for FLC and MLE. We quantitatively pinpointed the critical SNR (~ 0.005), at which the BOF approach starts losing its ability to select and verify particles reliably. We found that the use of a Gaussian model to initialize the MLE suppresses the adverse effects of reference dependency in the FLC function used for template-matching.

Conclusion

The BOF approach, which combines two distinct objective functions, provides a sensitive way to verify particles for downstream cryo-EM structure analysis. Importantly, reference dependency of the FLC does not necessarily transfer to the MLE, enabling the robust detection of weak signals. Our insights into the numerical behavior of the BOF approach can be used to improve automation efficiency in the cryo-EM data processing pipeline for high-resolution structural determination.

Electronic supplementary material

The online version of this article (10.1186/s12859-019-2714-8) contains supplementary material, which is available to authorized users.

The FtsK-like motor TraB is a DNA-dependent ATPase that forms higher-order assemblies

TraB is an FtsK-like DNA translocase responsible for conjugative plasmid transfer in mycelial Streptomyces Unlike other conjugative systems, which depend on a type IV secretion system, Streptomyces requires only TraB protein to transfer the plasmid as dsDNA. The γ-domain of this protein specifically binds to repeated 8-bp motifs on the plasmid sequence, following a mechanism that is reminiscent of the FtsK/SpoIIIE chromosome segregation system. In this work, we purified and characterized the enzymatic activity of TraB, revealing that it is a DNA-dependent ATPase that is highly stimulated by dsDNA substrates. Interestingly, we found that unlike the SpoIIIE protein, the γ-domain of TraB does not confer sequence-specific ATPase stimulation. We also found that TraB binds G-quadruplex DNA structures with higher affinity than TraB-recognition sequences (TRSs). An EM-based structural analysis revealed that TraB tends to assemble as large complexes comprising four TraB hexamers, which might be a prerequisite for DNA translocation across cell membranes. In summary, our findings shed light on the molecular mechanism used by the DNA-translocating motor TraB, which may be shared by other membrane-associated machineries involved in DNA binding and translocation.

Multicolor single-particle reconstruction of protein complexes

Single-particle reconstruction (SPR) from electron microscopy images is widely used in structural biology, but lacks direct information on protein identity. To address this limitation, we developed a computational and analytical framework that reconstructs and co-aligns multiple proteins from 2D superresolution fluorescence images. We demonstrate our method by generating multicolor 3D reconstructions of several proteins within the human centriole, revealing their relative locations, dimensions and orientations.

Nuclear RNR-α antagonizes cell proliferation by directly inhibiting ZRANB3

Since the origins of DNA-based life, the enzyme ribonucleotide reductase (RNR) has spurred proliferation because of its rate-limiting role in de novo deoxynucleoside-triphosphate (dNTP) biosynthesis. Paradoxically, the large subunit, RNR-α, of this obligatory two-component complex in mammals plays a context-specific antiproliferative role. There is little explanation for this dichotomy. Here, we show that RNR-α has a previously unrecognized DNA-replication inhibition function, leading to growth retardation. This underappreciated biological activity functions in the nucleus, where RNR-α interacts with ZRANB3. This process suppresses ZRANB3's function in unstressed cells, which we show to promote DNA synthesis. This nonreductase function of RNR-α is promoted by RNR-α hexamerization-induced by a natural and synthetic nucleotide of dA/ClF/CLA/FLU-which elicits rapid RNR-α nuclear import. The newly discovered nuclear signaling axis is a primary defense against elevated or imbalanced dNTP pools that can exert mutagenic effects irrespective of the cell cycle.

Rapid near-atomic resolution single-particle 3D reconstruction with SIMPLE

Cryogenic electron microscopy (cryo-EM) and single-particle analysis enables determination of near-atomic resolution structures of biological molecules. However, large computational requirements limit throughput and rapid testing of new image processing tools. We developed PRIME, an algorithm part of the SIMPLE software suite, for determination of the relative 3D orientations of single-particle projection images. PRIME has primarily found use for generation of an initial ab initio 3D reconstruction. Here we show that the strategy behind PRIME, iterative estimation of per-particle orientation distributions with stochastic hill climbing, provides a competitive approach to near-atomic resolution single-particle 3D reconstruction. A number of mathematical techniques for accelerating the convergence rate are introduced, leading to a speedup of nearly two orders of magnitude. We benchmarked our developments on numerous publicly available data sets and conclude that near-atomic resolution ab initio 3D reconstructions can be obtained with SIMPLE in a matter of hours, using standard over-the-counter CPU workstations.

Ligand discrimination and gating in cyclic nucleotide-gated ion channels from apo and partial agonist-bound cryo-EM structures

Cyclic nucleotide-modulated channels have important roles in visual signal transduction and pacemaking. Binding of cyclic nucleotides (cAMP/cGMP) elicits diverse functional responses in different channels within the family despite their high sequence and structure homology. The molecular mechanisms responsible for ligand discrimination and gating are unknown due to lack of correspondence between structural information and functional states. Using single particle cryo-electron microscopy and single-channel recording, we assigned functional states to high-resolution structures of SthK, a prokaryotic cyclic nucleotide-gated channel. The structures for apo, cAMP-bound, and cGMP-bound SthK in lipid nanodiscs, correspond to no, moderate, and low single-channel activity, respectively, consistent with the observation that all structures are in resting, closed states. The similarity between apo and ligand-bound structures indicates that ligand-binding domains are strongly coupled to pore and SthK gates in an allosteric, concerted fashion. The different orientations of cAMP and cGMP in the ‘resting’ and ‘activated’ structures suggest a mechanism for ligand discrimination.

Ion channels are essential for transmitting signals in the nervous system and brain. One large group of ion channels includes members that are activated by cyclic nucleotides, small molecules used to transmit signals within cells. These cyclic nucleotide-gated channels play an important role in regulating our ability to see and smell.

The activity of these ion channels has been studied for years, but scientists have only recently been able to look into their structure. Since structural biology methods require purified, well-behaved proteins, the members of this ion channel family selected for structural studies do not necessarily match those whose activity has been well established. There is a need for a good model that would allow both the structure and activity of a cyclic nucleotide-gated ion channel to be characterized.

The cyclic nucleotide-gated ion channel, SthK, from bacteria called Spirochaeta thermophila, was identified as such model because both its activity and its structure are accessible. Rheinberger et al. have used cryo electron microscopy to solve several high-resolution structures of SthK channels. In two of the structures, SthK was bound to either one of two types of activating cyclic nucleotides – cAMP or cGMP – and in another structure, no cyclic nucleotides were bound. Separately recording the activity of individual channels allowed the activity states likely to be represented by these structures to be identified.

Combining the results of the experiments revealed no activity from channels in an unbound state, low levels of activity for channels bound to cGMP, and moderate activity for channels bound to cAMP. Rheinberger et al. show that the channel, under the conditions experienced in cryo electron microscopy, is closed in all of the states studied. Unexpectedly, the binding of cyclic nucleotides produced no structural change even in the cyclic nucleotide-binding pocket of the channel, a region that was previously observed to undergo such changes when this region alone was crystallized. Rheinberger et al. deduce from this that the four subunits that make up the channel likely undergo the conformational change towards an open state all at once, rather than one by one.

The structures and the basic functional characterization of SthK channels provide a strong starting point for future research into determining the entire opening and closing cycle for a cyclic nucleotide-gated channel. Human equivalents of the channel are likely to work in similar ways. The results presented by Rheinberger et al. could therefore be built upon to help address diseases that result from deficiencies in cyclic nucleotide-gated channels, such as loss of vision due to retinal degradation (retinitis pigmentosa or progressive cone dystrophy) and achromatopsia.

Likelihood-based structural analysis of electron microscopy images

Likelihood-based analysis of single-particle electron microscopy images has contributed much to the recent improvements in resolution. By treating particle orientations and classes probabilistically, uncertainties in the reconstruction process are explicitly accounted for, and the risk of bias towards the initial model is diminished. As a result, the quality and reliability of the reconstructions have greatly improved at manageable computational cost. Likelihood-based analysis of electron microscopy images also offers a route to direct coordinate refinement for dynamic systems, as an alternative to 3D density reconstruction. Here, we review recent developments in the algorithms used for reconstructions of high-resolution maps, and in the integrative framework of combining likelihood methods with simulations to address conformational variability in cryo-electron microscopy.

cisTEM, user-friendly software for single-particle image processing

We have developed new open-source software called cisTEM (computational imaging system for transmission electron microscopy) for the processing of data for high-resolution electron cryo-microscopy and single-particle averaging. cisTEM features a graphical user interface that is used to submit jobs, monitor their progress, and display results. It implements a full processing pipeline including movie processing, image defocus determination, automatic particle picking, 2D classification, ab-initio 3D map generation from random parameters, 3D classification, and high-resolution refinement and reconstruction. Some of these steps implement newly-developed algorithms; others were adapted from previously published algorithms. The software is optimized to enable processing of typical datasets (2000 micrographs, 200 k – 300 k particles) on a high-end, CPU-based workstation in half a day or less, comparable to GPU-accelerated processing. Jobs can also be scheduled on large computer clusters using flexible run profiles that can be adapted for most computing environments. cisTEM is available for download from cistem.org.

Gaussian-input Gaussian mixture model for representing density maps and atomic models

A new Gaussian mixture model (GMM) has been developed for better representations of both atomic models and electron microscopy 3D density maps. The standard GMM algorithm employs an EM algorithm to determine the parameters. It accepted a set of 3D points with weights, corresponding to voxel or atomic centers. Although the standard algorithm worked reasonably well; however, it had three problems. First, it ignored the size (voxel width or atomic radius) of the input, and thus it could lead to a GMM with a smaller spread than the input. Second, the algorithm had a singularity problem, as it sometimes stopped the iterative procedure due to a Gaussian function with almost zero variance. Third, a map with a large number of voxels required a long computation time for conversion to a GMM. To solve these problems, we have introduced a Gaussian-input GMM algorithm, which considers the input atoms or voxels as a set of Gaussian functions. The standard EM algorithm of GMM was extended to optimize the new GMM. The new GMM has identical radius of gyration to the input, and does not suddenly stop due to the singularity problem. For fast computation, we have introduced a down-sampled Gaussian functions (DSG) by merging neighboring voxels into an anisotropic Gaussian function. It provides a GMM with thousands of Gaussian functions in a short computation time. We also have introduced a DSG-input GMM: the Gaussian-input GMM with the DSG as the input. This new algorithm is much faster than the standard algorithm.

Domain topology of human Rasal

Rasal is a modular multi-domain protein of the GTPase-activating protein 1 (GAP1) family; its four known members, GAP1m, Rasal, GAP1IP4BP and Capri, have a Ras GTPase-activating domain (RasGAP). This domain supports the intrinsically slow GTPase activity of Ras by actively participating in the catalytic reaction. In the case of Rasal, GAP1IP4BP and Capri, their remaining domains are responsible for converting the RasGAP domains into dual Ras- and Rap-GAPs, via an incompletely understood mechanism. Although Rap proteins are small GTPase homologues of Ras, their catalytic residues are distinct, which reinforces the importance of determining the structure of full-length GAP1 family proteins. To date, these proteins have not been crystallized, and their size is not adequate for nuclear magnetic resonance (NMR) or for high-resolution cryo-electron microscopy (cryoEM). Here we present the low resolution structure of full-length Rasal, obtained by negative staining electron microscopy, which allows us to propose a model of its domain topology. These results help to understand the role of the different domains in controlling the dual GAP activity of GAP1 family proteins.

The impact of recent improvements in cryo-electron microscopy technology on the understanding of bacterial ribosome assembly

Cryo-electron microscopy (cryo-EM) had played a central role in the study of ribosome structure and the process of translation in bacteria since the development of this technique in the mid 1980s. Until recently cryo-EM structures were limited to ∼10 Å in the best cases. However, the recent advent of direct electron detectors has greatly improved the resolution of cryo-EM structures to the point where atomic resolution is now achievable. This improved resolution will allow cryo-EM to make groundbreaking contributions in essential aspects of ribosome biology, including the assembly process. In this review, we summarize important insights that cryo-EM, in combination with chemical and genetic approaches, has already brought to our current understanding of the ribosomal assembly process in bacteria using previous detector technology. More importantly, we discuss how the higher resolution structures now attainable with direct electron detectors can be leveraged to propose precise testable models regarding this process. These structures will provide an effective platform to develop new antibiotics that target this fundamental cellular process.

Single-particle cryo-EM-Improved ab initio 3D reconstruction with SIMPLE/PRIME

Cryogenic electron microscopy (cryo-EM) and single-particle analysis now enables the determination of high-resolution structures of macromolecular assemblies that have resisted X-ray crystallography and other approaches. We developed the SIMPLE open-source image-processing suite for analysing cryo-EM images of single-particles. A core component of SIMPLE is the probabilistic PRIME algorithm for identifying clusters of images in 2D and determine relative orientations of single-particle projections in 3D. Here, we extend our previous work on PRIME and introduce new stochastic optimization algorithms that improve the robustness of the approach. Our refined method for identification of homogeneous subsets of images in accurate register substantially improves the resolution of the cluster centers and of the ab initio 3D reconstructions derived from them. We now obtain maps with a resolution better than 10 Å by exclusively processing cluster centers. Excellent parallel code performance on over-the-counter laptops and CPU workstations is demonstrated.

Single-particle cryo-EM using alignment by classification (ABC): the structure of Lumbricus terrestris haemoglobin

Single-particle cryogenic electron microscopy (cryo-EM) can now yield near-atomic resolution structures of biological complexes. However, the reference-based alignment algorithms commonly used in cryo-EM suffer from reference bias, limiting their applicability (also known as the 'Einstein from random noise' problem). Low-dose cryo-EM therefore requires robust and objective approaches to reveal the structural information contained in the extremely noisy data, especially when dealing with small structures. A reference-free pipeline is presented for obtaining near-atomic resolution three-dimensional reconstructions from heterogeneous ('four-dimensional') cryo-EM data sets. The methodologies integrated in this pipeline include a posteriori camera correction, movie-based full-data-set contrast transfer function determination, movie-alignment algorithms, (Fourier-space) multivariate statistical data compression and unsupervised classification, 'random-startup' three-dimensional reconstructions, four-dimensional structural refinements and Fourier shell correlation criteria for evaluating anisotropic resolution. The procedures exclusively use information emerging from the data set itself, without external 'starting models'. Euler-angle assignments are performed by angular reconstitution rather than by the inherently slower projection-matching approaches. The comprehensive 'ABC-4D' pipeline is based on the two-dimensional reference-free 'alignment by classification' (ABC) approach, where similar images in similar orientations are grouped by unsupervised classification. Some fundamental differences between X-ray crystallography versus single-particle cryo-EM data collection and data processing are discussed. The structure of the giant haemoglobin from Lumbricus terrestris at a global resolution of ∼3.8 Å is presented as an example of the use of the ABC-4D procedure.

Massively parallel unsupervised single-particle cryo-EM data clustering via statistical manifold learning

Structural heterogeneity in single-particle cryo-electron microscopy (cryo-EM) data represents a major challenge for high-resolution structure determination. Unsupervised classification may serve as the first step in the assessment of structural heterogeneity. However, traditional algorithms for unsupervised classification, such as K-means clustering and maximum likelihood optimization, may classify images into wrong classes with decreasing signal-to-noise-ratio (SNR) in the image data, yet demand increased computational costs. Overcoming these limitations requires further development of clustering algorithms for high-performance cryo-EM data processing. Here we introduce an unsupervised single-particle clustering algorithm derived from a statistical manifold learning framework called generative topographic mapping (GTM). We show that unsupervised GTM clustering improves classification accuracy by about 40% in the absence of input references for data with lower SNRs. Applications to several experimental datasets suggest that our algorithm can detect subtle structural differences among classes via a hierarchical clustering strategy. After code optimization over a high-performance computing (HPC) environment, our software implementation was able to generate thousands of reference-free class averages within hours in a massively parallel fashion, which allows a significant improvement on ab initio 3D reconstruction and assists in the computational purification of homogeneous datasets for high-resolution visualization.