2.8 Å resolution reconstruction of the Thermoplasma acidophilum 20S proteasome using cryo-electron microscopy

Using PELDOR to count spins on multi-nitroxides

We investigated the accuracy and limitation of using the modulation depth of pulsed electron-electron double resonance experiments to count the number of coupled spins. For this purpose, synthesized multi-nitroxide molecules with 2-6 spins were used. We could show that the main limitation on accurately counting larger number of coupled spins at Q-band frequencies is determined by the reproducibility of adjusting and calibrating the pump pulse excitation efficiency. Using broadband sech/tanh or short 10 ns rectangular pump pulses modulation depth suppression effects arising from non-ideal coverage of the dipolar-split signals can be avoided for molecules with intra-molecular spin distances larger than 2 nm. The transverse relaxation times for our model compounds with one to six spins did not depend on the spin number and were all the same. Nevertheless, the signal intensity of the primary Hahn echo signal in a 4-pulse PELDOR sequence decreased strongly with the number of coupled spins. This is due to the dipolar defocusing if more than one spin is excited by the first two pulses at the detection frequency, resulting in a loss of refocused echo intensity of the PELDOR experiments. This effect further reduces the accuracy of using the PELDOR modulation depth for spin counting. Altogether, our results demonstrate that this method is potentially applicable up to hexameric complexes with nitroxides.

Self-supervised machine learning framework for high-throughput electron microscopy

Transmission electron microscopy (TEM) is a crucial analysis method in materials science and structural biology, as it offers a high spatiotemporal resolution for structural characterization and reveals structure-property relationships and structural dynamics at atomic and molecular levels. Despite technical advancements in EM, the nature of the electron beam makes the EM imaging inherently detrimental to materials even in low-dose applications. We introduce SHINE, the Self-supervised High-throughput Image denoising Neural network for Electron microscopy, accelerating minimally invasive low-dose EM of diverse material systems. SHINE uses only a single raw image dataset with intrinsic noise, which makes it suitable for limited-size datasets and eliminates the need for expensive ground-truth training datasets. We quantitatively demonstrate that SHINE overcomes the information limit in the current high-resolution TEM, in situ liquid phase TEM, time-series scanning TEM, and cryo-TEM, facilitating unambiguous high-throughput structure analysis across a broad spectrum of materials.

Complex water networks visualized by cryogenic electron microscopy of RNA

The stability and function of biomolecules are directly influenced by their myriad interactions with water1-16. Here we investigated water through cryogenic electron microscopy (cryo-EM) on a highly solvated molecule: the Tetrahymena ribozyme. By using segmentation-guided water and ion modelling (SWIM)17,18, an approach combining resolvability and chemical parameters, we automatically modelled and cross-validated water molecules and Mg2+ ions in the ribozyme core, revealing the extensive involvement of water in mediating RNA non-canonical interactions. Unexpectedly, in regions where SWIM does not model ordered water, we observed highly similar densities in both cryo-EM maps. In many of these regions, the cryo-EM densities superimpose with complex water networks predicted by molecular dynamics, supporting their assignment as water and suggesting a biophysical explanation for their elusiveness to conventional atomic coordinate modelling. Our study demonstrates an approach to unveil both rigid and flexible waters that surround biomolecules through cryo-EM map densities, statistical and chemical metrics, and molecular dynamics simulations.

Complex Water Networks Visualized through 2.2-2.3 Å Cryogenic Electron Microscopy of RNA

The stability and function of biomolecules are directly influenced by their myriad interactions with water. In this study, we investigated water through cryogenic electron microscopy (cryo-EM) on a highly solvated molecule, the Tetrahymena ribozyme, determined at 2.2 and 2.3 Å resolutions. By employing segmentation-guided water and ion modeling (SWIM), an approach combining resolvability and chemical parameters, we automatically modeled and cross-validated water molecules and Mg2+ ions in the ribozyme core, revealing the extensive involvement of water in mediating RNA non-canonical interactions. Unexpectedly, in regions where SWIM does not model ordered water, we observed highly similar densities in both cryo-EM maps. In many of these regions, the cryo-EM densities superimpose with complex water networks predicted by molecular dynamics (MD), supporting their assignment as water and suggesting a biophysical explanation for their elusiveness to conventional atomic coordinate modeling. Our study demonstrates an approach to unveil both rigid and flexible waters that surround biomolecules through cryo-EM map densities, statistical and chemical metrics, and MD simulations.

Factors affecting macromolecule orientations in thin films formed in cryo-EM

The formation of a vitrified thin film embedded with randomly oriented macromolecules is an essential prerequisite for cryogenic sample electron microscopy. Most commonly, this is achieved using the plunge-freeze method first described nearly 40 years ago. Although this is a robust method, the behaviour of different macromolecules shows great variation upon freezing and often needs to be optimized to obtain an isotropic, high-resolution reconstruction. For a macromolecule in such a film, the probability of encountering the air-water interface in the time between blotting and freezing and adopting preferred orientations is very high. 3D reconstruction using preferentially oriented particles often leads to anisotropic and uninterpretable maps. Currently, there are no general solutions to this prevalent issue, but several approaches largely focusing on sample preparation with the use of additives and novel grid modifications have been attempted. In this study, the effect of physical and chemical factors on the orientations of macromolecules was investigated through an analysis of selected well studied macromolecules, and important parameters that determine the behaviour of proteins on cryo-EM grids were revealed. These insights highlight the nature of the interactions that cause preferred orientations and can be utilized to systematically address orientation bias for any given macromolecule and to provide a framework to design small-molecule additives to enhance sample stability and behaviour.

Improved Denoising of Cryo-Electron Microscopy Micrographs with Simulation-Aware Pretraining

Cryo-electron microscopy (cryo-EM) has emerged as a potent technique for determining the structure and functionality of biological macromolecules. However, limited by the physical imaging conditions, such as low electron beam dose, micrographs in cryo-EM typically contend with an extremely low signal-to-noise ratio (SNR), impeding the efficiency and efficacy of subsequent analyses. Therefore, there is a growing demand for an efficient denoising algorithm designed for cryo-EM micrographs, aiming to enhance the quality of macromolecular analysis. However, owing to the absence of a comprehensive and well-defined dataset with ground truth images, supervised image denoising methods exhibit limited generalization when applied to experimental micrographs. To tackle this challenge, we introduce a simulation-aware image denoising (SaID) pretrained model designed to enhance the SNR of cryo-EM micrographs where the training is solely based on an accurately simulated dataset. First, we propose a parameter calibration algorithm for simulated dataset generation, aiming to align simulation parameters with those of experimental micrographs. Second, leveraging the accurately simulated dataset, we propose to train a deep general denoising model that can well generalize to real experimental cryo-EM micrographs. Comprehensive experimental results demonstrate that our pretrained denoising model achieves excellent denoising performance on experimental cryo-EM micrographs, significantly streamlining downstream analysis.

Classification of helical polymers with deep-learning language models

Many macromolecules in biological systems exist in the form of helical polymers. However, the inherent polymorphism and heterogeneity of samples complicate the reconstruction of helical polymers from cryo-EM images. Currently, available 2D classification methods are effective at separating particles of interest from contaminants, but they do not effectively differentiate between polymorphs, resulting in heterogeneity in the 2D classes. As such, it is crucial to develop a method that can computationally divide a dataset of polymorphic helical structures into homogenous subsets. In this work, we utilized deep-learning language models to embed the filaments as vectors in hyperspace and group them into clusters. Tests with both simulated and experimental datasets have demonstrated that our method - HLM (Helical classification with Language Model) can effectively distinguish different types of filaments, in the presence of many contaminants and low signal-to-noise ratios. We also demonstrate that HLM can isolate homogeneous subsets of particles from a publicly available dataset, resulting in the discovery of a previously unreported filament variant with an extra density around the tau filaments.

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.

Conformational space exploration of cryo-EM structures by variability refinement

Cryo-EM observation of biological samples enables visualization of sample heterogeneity, in the form of discrete states that are separable, or continuous heterogeneity as a result of local protein motion before flash freezing. Variability analysis of this continuous heterogeneity describes the variance between a particle stack and a volume, and results in a map series describing the various steps undertaken by the sample in the particle stack. While this observation is absolutely stunning, it is very hard to pinpoint structural details to elements of the maps. In order to bridge the gap between observation and explanation, we designed a tool that refines an ensemble of structures into all the maps from variability analysis. Using this bundle of structures, it is easy to spot variable parts of the structure, as well as the parts that are not moving. Comparison with molecular dynamics simulations highlights the fact that the movements follow the same directions, albeit with different amplitudes. Ligand can also be investigated using this method. Variability refinement is available in the Phenix software suite, accessible under the program name phenix.varref.

Cryo-EM for Small Molecules

In recent years, protein structure analysis using cryo-electron microscopy(cryo-EM) has expanded and improved. In this review, we discuss many recent improvements to the field, the problems those improvements hope to solve, and some of the still unanswered questions. Most notably, this review will discuss improvements in resolving small or fragmented protein structures, as well as methods to improve the signal-to-noise ratio of the data by increasing image contrast using carbon-based systems. We will also describe how, in the last 5 years, methodological improvements have allowed for better 3D image resolution by capturing a continuum of 3D images. We will provide examples of these methods in practice and discuss how these improved methods may be used in small-molecule drug discovery and development. © 2022 Wiley Periodicals LLC.

Analysis and comparison of electron radiation damage assessments in Cryo-EM by single particle analysis and micro-crystal electron diffraction

Electron radiation damage to macromolecules is an inevitable resolution limit factor in all major structural determination applications using cryo-electron microscopy (cryo-EM). Single particle analysis (SPA) and micro-crystal electron diffraction (MicroED) have been employed to assess radiation damage with a variety of protein complexes. Although radiation induced sidechain density loss and resolution decay were observed by both methods, the minimum dose of electron irradiation reducing high-resolution limit reported by SPA is more than ten folds higher than measured by MicroED using the conventional dose concept, and there is a gap between the attained resolutions assessed by these two methods. We compared and analyzed these two approaches side-by-side in detail from several aspects to identify some crucial determinants and to explain this discrepancy. Probability of a high energy electron being inelastically scattered by a macromolecule is proportional to number of layers of the molecules in its transmission path. As a result, the same electron dose could induce much more site-specific damage to macromolecules in 3D protein crystal than single particle samples. Major differences in data collection and processing scheme are the key factors to different levels of sensitivity to radiation damage at high resolution between the two methods. High resolution electron diffraction in MicroED dataset is very sensitive to global damage to 3D protein crystals with low dose accumulation, and its intensity attenuation rates at atomic resolution shell could be applied for estimating ratio of damaged and total selected single particles for SPA. More in-depth systematically radiation damage assessments using SPA and MicroED will benefit all applications of cryo-EM, especially cellular structure analysis by tomography.

Higher resolution in cryo-EM by the combination of macromolecular prior knowledge and image-processing tools

Single-particle cryo-electron microscopy has become a powerful technique for the 3D structure determination of biological molecules. The last decade has seen an astonishing development of both hardware and software, and an exponential growth of new structures obtained at medium-high resolution. However, the knowledge accumulated in this field over the years has hardly been utilized as feedback in the reconstruction of new structures. In this context, this article explores the use of the deep-learning approach deepEMhancer as a regularizer in the RELION refinement process. deepEMhancer introduces prior information derived from macromolecular structures, and contributes to noise reduction and signal enhancement, as well as a higher degree of isotropy. These features have a direct effect on image alignment and reduction of overfitting during iterative refinement. The advantages of this combination are demonstrated for several membrane proteins, for which it is especially useful because of their high disorder and flexibility.

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.

IceBreaker: Software for high-resolution single-particle cryo-EM with non-uniform ice

Despite the abundance of available software tools, optimal particle selection is still a vital issue in single-particle cryoelectron microscopy (cryo-EM). Regardless of the method used, most pickers struggle when ice thickness varies on a micrograph. IceBreaker allows users to estimate the relative ice gradient and flatten it by equalizing the local contrast. It allows the differentiation of particles from the background and improves overall particle picking performance. Furthermore, we introduce an additional parameter corresponding to local ice thickness for each particle. Particles with a defined ice thickness can be grouped and filtered based on this parameter during processing. These functionalities are especially valuable for on-the-fly processing to automatically pick as many particles as possible from each micrograph and to select optimal regions for data collection. Finally, estimated ice gradient distributions can be stored separately and used to inspect the quality of prepared samples.

End-to-end orientation estimation from 2D cryo-EM images

Cryo-electron microscopy (cryo-EM) is a Nobel Prize-winning technique for determining high-resolution 3D structures of biological macromolecules. A 3D structure is reconstructed from hundreds of thousands of noisy 2D projection images. However, existing 3D reconstruction methods are still time-consuming, and one of the major computational bottlenecks is recovering the unknown orientation of the particle in each 2D image. The dominant methods typically exploit an expensive global search on each image to estimate the missing orientations. Here, a novel end-to-end supervised learning method is introduced to directly recover the missing orientations from 2D cryo-EM images. A neural network is used to approximate the mapping from images to orientations. A robust loss function is proposed for optimizing the parameters of the network, which can handle both asymmetric and symmetric 3D structures. Experiments on synthetic data sets with various symmetry types confirm that the neural network is capable of recovering orientations from 2D cryo-EM images, and the results on a real cryo-EM data set further demonstrate its potential under more challenging imaging conditions.

OUP accepted manuscript

Motivation

Cryo-electron microscopy (cryo-EM) is a widely used technology for ultrastructure determination, which constructs the 3D structures of protein and macromolecular complex from a set of 2D micrographs. However, limited by the electron beam dose, the micrographs in cryo-EM generally suffer from the extremely low signal-to-noise ratio (SNR), which hampers the efficiency and effectiveness of downstream analysis. Especially, the noise in cryo-EM is not simple additive or multiplicative noise whose statistical characteristics are quite different from the ones in natural image, extremely shackling the performance of conventional denoising methods.

Results

Here, we introduce the Noise-Transfer2Clean (NT2C), a denoising deep neural network (DNN) for cryo-EM to enhance image contrast and restore specimen signal, whose main idea is to improve the denoising performance by correctly learning the noise distribution of cryo-EM images and transferring the statistical nature of noise into the denoiser. Especially, to cope with the complex noise model in cryo-EM, we design a contrast-guided noise and signal re-weighted algorithm to achieve clean-noisy data synthesis and data augmentation, making our method authentically achieve signal restoration based on noise’s true properties. Our work verifies the feasibility of denoising based on mining the complex cryo-EM noise patterns directly from the noise patches. Comprehensive experimental results on simulated datasets and real datasets show that NT2C achieved a notable improvement in image denoising, especially in background noise removal, compared with the commonly used methods. Moreover, a case study on the real dataset demonstrates that NT2C can greatly alleviate the obstacles caused by the SNR to particle picking and simplify the identifying of particles.

Availabilityand implementation

The code is available at https://github.com/Lihongjia-ict/NoiseTransfer2Clean/.

Supplementary information

Supplementary data are available at Bioinformatics online.

Evaluation of automated particle picking for cryogenic electron microscopy using high-precision transmission electron microscope simulation based on a multi-slice method

This work describes the GRIPS automated particle-picking software for cryogenic electron microscopy and the evaluation of this software using elbis, a high-precision transmission electron microscope (TEM) image simulator. The goal was to develop a method that can pick particles under a small defocus condition where the particles are not clearly visible or under a condition where the particles are exhibiting preferred orientation. The proposed method handles these issues by repeatedly performing three processes, namely extraction, two-dimensional classification and positioning, and by introducing mask processing to exclude areas with particles that have already been picked. TEM images for evaluation were generated with a high-precision TEM image simulator. TEM images containing both particles and amorphous ice were simulated by randomly placing O atoms in the specimen. The experimental results indicate that the proposed method can be used to pick particles correctly under a relatively small defocus condition. Moreover, the results show that the mask processing introduced in the proposed method is valid for particles exhibiting preferred orientation. It is further shown that the proposed method is applicable to data collected from real samples.

High-power near-concentric Fabry-Perot cavity for phase contrast electron microscopy

Transmission electron microscopy (TEM) of vitrified biological macromolecules (cryo-EM) is limited by the weak phase contrast signal that is available from such samples. Using a phase plate would thus substantially improve the signal-to-noise ratio. We have previously demonstrated the use of a high-power Fabry-Perot cavity as a phase plate for TEM. We now report improvements to our laser cavity that allow us to achieve record continuous wave intensities of over 450 GW/cm², sufficient to produce the optimal 90° phase shift for 300 keV electrons. In addition, we have performed the first cryo-EM reconstruction using a laser phase plate, demonstrating that the stability of this laser phase plate is sufficient for use during standard cryo-EM data collection.

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.

3D variability analysis: Resolving continuous flexibility and discrete heterogeneity from single particle cryo-EM

Single particle cryo-EM excels in determining static structures of protein molecules, but existing 3D reconstruction methods have been ineffective in modelling flexible proteins. We introduce 3D variability analysis (3DVA), an algorithm that fits a linear subspace model of conformational change to cryo-EM data at high resolution. 3DVA enables the resolution and visualization of detailed molecular motions of both large and small proteins, revealing new biological insight from single particle cryo-EM data. Experimental results demonstrate the ability of 3DVA to resolve multiple flexible motions of α-helices in the sub-50 kDa transmembrane domain of a GPCR complex, bending modes of a sodium ion channel, five types of symmetric and symmetry-breaking flexibility in a proteasome, large motions in a spliceosome complex, and discrete conformational states of a ribosome assembly. 3DVA is implemented in the cryoSPARC software package.

DRPnet: automated particle picking in cryo-electron micrographs using deep regression

BACKGROUND

Identification and selection of protein particles in cryo-electron micrographs is an important step in single particle analysis. In this study, we developed a deep learning-based particle picking network to automatically detect particle centers from cryoEM micrographs. This is a challenging task due to the nature of cryoEM data, having low signal-to-noise ratios with variable particle sizes, shapes, distributions, grayscale variations as well as other undesirable artifacts.

RESULTS

We propose a double convolutional neural network (CNN) cascade for automated detection of particles in cryo-electron micrographs. This approach, entitled Deep Regression Picker Network or "DRPnet", is simple but very effective in recognizing different particle sizes, shapes, distributions and grayscale patterns corresponding to 2D views of 3D particles. Particles are detected by the first network, a fully convolutional regression network (FCRN), which maps the particle image to a continuous distance map that acts like a probability density function of particle centers. Particles identified by FCRN are further refined to reduce false particle detections by the second classification CNN. DRPnet's first CNN pretrained with only a single cryoEM dataset can be used to detect particles from different datasets without retraining. Compared to RELION template-based autopicking, DRPnet results in better particle picking performance with drastically reduced user interactions and processing time. DRPnet also outperforms the state-of-the-art particle picking networks in terms of the supervised detection evaluation metrics recall, precision, and F-measure. To further highlight quality of the picked particle sets, we compute and present additional performance metrics assessing the resulting 3D reconstructions such as number of 2D class averages, efficiency/angular coverage, Rosenthal-Henderson plots and local/global 3D reconstruction resolution.

CONCLUSION

DRPnet shows greatly improved time-savings to generate an initial particle dataset compared to manual picking, followed by template-based autopicking. Compared to other networks, DRPnet has equivalent or better performance. DRPnet excels on cryoEM datasets that have low contrast or clumped particles. Evaluating other performance metrics, DRPnet is useful for higher resolution 3D reconstructions with decreased particle numbers or unknown symmetry, detecting particles with better angular orientation coverage.

Setting up and operating a cryo-EM laboratory

Cryo-electron microscopy (cryo-EM) has become the technique of choice for structural biology of macromolecular assemblies, after the 'resolution revolution' that has occurred in this field since 2012. With a suitable instrument, an appropriate electron detector and, last but not least, a cooperative sample it is now possible to collect images from which macromolecular structures can be determined to better than 2 Å resolution, where reliable atomic models can be built. By electron tomography and sub-tomogram averaging of cryo-samples, it is also possible to reconstruct subcellular structures to sub-nanometre resolution. This review describes the infrastructure that is needed to achieve this goal. Ideally, a cryo-EM lab will have a dedicated 300 kV electron microscope for data recording and a 200 kV instrument for screening cryo-samples, both with direct electron detectors, and at least one 120 kV EM for negative-stain screening at room temperature. Added to this should be ancillary equipment for specimen preparation, including a light microscope, carbon coater, plasma cleaner, glow discharge unit, a device for fast, robotic sample freezing, liquid nitrogen storage Dewars and a ready supply of clean liquid nitrogen. In practice, of course, the available budget will determine the number and types of microscopes and how elaborate the lab can be. The cryo-EM lab should be designed with adequate space for the electron microscopes and ancillary equipment, and should allow for sufficient storage space. Each electron microscope room should be connected to the image-processing computers by fibre-optic cables for the rapid transfer of large datasets. The cryo-EM lab should be overseen by a facility manager whose responsibilities include the day-to-day tasks to ensure that all microscopes are operating perfectly, organising service and repairs to minimise downtime, and controlling the budget. Large facilities will require additional support staff who help to oversee the operation of the facility and instruct new users.

FSC-Q: a CryoEM map-to-atomic model quality validation based on the local Fourier shell correlation

In recent years, advances in cryoEM have dramatically increased the resolution of reconstructions and, with it, the number of solved atomic models. It is widely accepted that the quality of cryoEM maps varies locally; therefore, the evaluation of the maps-derived structural models must be done locally as well. In this article, a method for the local analysis of the map-to-model fit is presented. The algorithm uses a comparison of two local resolution maps. The first is the local FSC (Fourier shell correlation) between the full map and the model, while the second is calculated between the half maps normally used in typical single particle analysis workflows. We call the quality measure "FSC-Q", and it is a quantitative estimation of how much of the model is supported by the signal content of the map. Furthermore, we show that FSC-Q may be helpful to detect overfitting. It can be used to complement other methods, such as the Q-score method that estimates the resolvability of atoms.

Topaz-Denoise: general deep denoising models for cryoEM and cryoET

Cryo-electron microscopy (cryoEM) is becoming the preferred method for resolving protein structures. Low signal-to-noise ratio (SNR) in cryoEM images reduces the confidence and throughput of structure determination during several steps of data processing, resulting in impediments such as missing particle orientations. Denoising cryoEM images can not only improve downstream analysis but also accelerate the time-consuming data collection process by allowing lower electron dose micrographs to be used for analysis. Here, we present Topaz-Denoise, a deep learning method for reliably and rapidly increasing the SNR of cryoEM images and cryoET tomograms. By training on a dataset composed of thousands of micrographs collected across a wide range of imaging conditions, we are able to learn models capturing the complexity of the cryoEM image formation process. The general model we present is able to denoise new datasets without additional training. Denoising with this model improves micrograph interpretability and allows us to solve 3D single particle structures of clustered protocadherin, an elongated particle with previously elusive views. We then show that low dose collection, enabled by Topaz-Denoise, improves downstream analysis in addition to reducing data collection time. We also present a general 3D denoising model for cryoET. Topaz-Denoise and pre-trained general models are now included in Topaz. We expect that Topaz-Denoise will be of broad utility to the cryoEM community for improving micrograph and tomogram interpretability and accelerating analysis.

WITHDRAWN: Structural studies of vitrified biological proteins and macromolecules - A review on the microimaging aspects of cryo-electron microscopy

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High-Throughput Cryo-EM Enabled by User-Free Preprocessing Routines

Single-particle cryoelectron microscopy (cryo-EM) continues to grow into a mainstream structural biology technique. Recent developments in data collection strategies alongside new sample preparation devices herald a future where users will collect multiple datasets per microscope session. To make cryo-EM data processing more automatic and user-friendly, we have developed an automatic pipeline for cryo-EM data preprocessing and assessment using a combination of deep-learning and image-analysis tools. We have verified the performance of this pipeline on a number of datasets and extended its scope to include sample screening by the user-free assessment of the qualities of a series of datasets under different conditions. We propose that our workflow provides a decision-free solution for cryo-EM, making data preprocessing more generalized and robust in the high-throughput era as well as more convenient for users from a range of backgrounds.

Simulation-Based Methods for Model Building and Refinement in Cryoelectron Microscopy

Advances in cryoelectron microscopy (cryo-EM) have revolutionized the structural investigation of large macromolecular assemblies. In this review, we first provide a broad overview of modeling methods used for flexible fitting of molecular models into cryo-EM density maps. We give special attention to approaches rooted in molecular simulations-atomistic molecular dynamics and Monte Carlo. Concise descriptions of the methods are given along with discussion of their advantages, limitations, and most popular alternatives. We also describe recent extensions of the widely used molecular dynamics flexible fitting (MDFF) method and discuss how different model-building techniques could be incorporated into new hybrid modeling schemes and simulation workflows. Finally, we provide two illustrative examples of model-building and refinement strategies employing MDFF, cascade MDFF, and RosettaCM. These examples come from recent cryo-EM studies that elucidated transcription preinitiation complexes and shed light on the functional roles of these assemblies in gene expression and gene regulation.

Estimation of high-order aberrations and anisotropic magnification from cryo-EM data sets in RELION-3.1

Methods are presented that detect three types of aberrations in single-particle cryo-EM data sets: symmetrical and antisymmetrical optical aberrations and magnification anisotropy. Because these methods only depend on the availability of a preliminary 3D reconstruction from the data, they can be used to correct for these aberrations for any given cryo-EM data set, a posteriori. Using five publicly available data sets, it is shown that considering these aberrations improves the resolution of the 3D reconstruction when these effects are present. The methods are implemented in version 3.1 of the open-source software package RELION.

Robust ultraclean atomically thin membranes for atomic-resolution electron microscopy

The fast development of high-resolution electron microscopy (EM) demands a background-noise-free substrate to support the specimens, where atomically thin graphene membranes can serve as an ideal candidate. Yet the preparation of robust and ultraclean graphene EM grids remains challenging. Here we present a polymer- and transfer-free direct-etching method for batch fabrication of robust ultraclean graphene grids through membrane tension modulation. Loading samples on such graphene grids enables the detection of single metal atoms and atomic-resolution imaging of the iron core of ferritin molecules at both room- and cryo-temperature. The same kind of hydrophilic graphene grid allows the formation of ultrathin vitrified ice layer embedded most protein particles at the graphene-water interface, which facilitates cryo-EM 3D reconstruction of archaea 20S proteasomes at a record high resolution of ~2.36 Å. Our results demonstrate the significant improvements in image quality using the graphene grids and expand the scope of EM imaging.

High-resolution electron microscopy requires robust and noise-free substrates to support the specimens. Here, the authors present a polymer- and transfer-free direct-etching method for fabrication of graphene grids with ultraclean surfaces and demonstrate cryo-EM at record high resolution.

Measuring local-directional resolution and local anisotropy in cryo-EM maps

The introduction of local resolution has enormously helped the understanding of cryo-EM maps. Still, for any given pixel it is a global, aggregated value, that makes impossible the individual analysis of the contribution of the different projection directions. We introduce MonoDir, a fully automatic, parameter-free method that, starting only from the final cryo-EM map, decomposes local resolution into the different projection directions, providing a detailed level of analysis of the final map. Many applications of directional local resolution are possible, and we concentrate here on map quality and validation.

It is important to analyse the local resolution of cryo-EM maps. Here the authors present MonoDir, a fully automatic and parameter free method for the directional local resolution analysis of cryo-EM maps that requires only the final map as input and they also propose indicators for assessing map quality.

Developments, applications, and prospects of cryo-electron microscopy

Cryo‐electron microscopy (cryo‐EM) is a structural biological method that is used to determine the 3D structures of biomacromolecules. After years of development, cryo‐EM has made great achievements, which has led to a revolution in structural biology. In this article, the principle, characteristics, history, current situation, workflow, and common problems of cryo‐EM are systematically reviewed. In addition, the new development direction of cryo‐EM—cryo‐electron tomography (cryo‐ET), is discussed in detail. Also, cryo‐EM is prospected from the following aspects: the structural analysis of small proteins, the improvement of resolution and efficiency, and the relationship between cryo‐EM and drug development. This review is dedicated to giving readers a comprehensive understanding of the development and application of cryo‐EM, and to bringing them new insights.

Structures of MERS-CoV spike glycoprotein in complex with sialoside attachment receptors

The Middle East respiratory syndrome coronavirus (MERS-CoV) causes severe and often lethal respiratory illness in humans, and no vaccines or specific treatments are available. Infections are initiated via binding of the MERS-CoV spike (S) glycoprotein to sialosides and dipeptidyl-peptidase 4 (the attachment and entry receptors, respectively). To understand MERS-CoV engagement of sialylated receptors, we determined the cryo-EM structures of S in complex with 5-N-acetyl neuraminic acid, 5-N-glycolyl neuraminic acid, sialyl-LewisX, α2,3-sialyl-N-acetyl-lactosamine and α2,6-sialyl-N-acetyl-lactosamine at 2.7-3.0 Å resolution. We show that recognition occurs via a conserved groove that is essential for MERS-CoV S-mediated attachment to sialosides and entry into human airway epithelial cells. Our data illuminate MERS-CoV S sialoside specificity and suggest that selectivity for α2,3-linked over α2,6-linked receptors results from enhanced interactions with the former class of oligosaccharides. This study provides a structural framework explaining MERS-CoV attachment to sialoside receptors and identifies a site of potential vulnerability to inhibitors of viral entry.

A shared vision for macromolecular crystallography over the next five years

Macromolecular crystallography (MX) is the dominant means of determining the three-dimensional structures of biological macromolecules, but the method has reached a critical juncture. New diffraction-limited storage rings and upgrades to the existing sources will provide beamlines with higher flux and brilliance, and even the largest detectors can collect at rates of several hundred hertz. Electron cryomicroscopy is successfully competing for structural biologists' most exciting projects. As a result, formerly scarce beam time is becoming increasingly abundant, and beamlines must innovate to attract users and ensure continued funding. Here, we will show how data collection has changed over the preceding five years and how alternative methods have emerged. We then explore how MX at synchrotrons might develop over the next five years. We predict that, despite the continued dominance of rotation crystallography, applications previously considered niche or experimental, such as serial crystallography, pink-beam crystallography, and crystallography at energies above 25 keV and below 5 keV, will rise in prominence as beamlines specialize to offer users the best value. Most of these emerging methods will require new hardware and software. With these advances, MX will more efficiently provide the high-resolution structures needed for drug development. MX will also be able to address a broader range of questions than before and contribute to a deeper understanding of biological processes in the context of integrative structural biology.

DeepRes: a new deep-learning- and aspect-based local resolution method for electron-microscopy maps

In this article, a method is presented to estimate a new local quality measure for 3D cryoEM maps that adopts the form of a 'local resolution' type of information. The algorithm (DeepRes) is based on deep-learning 3D feature detection. DeepRes is fully automatic and parameter-free, and avoids the issues of most current methods, such as their insensitivity to enhancements owing to B-factor sharpening (unless the 3D mask is changed), among others, which is an issue that has been virtually neglected in the cryoEM field until now. In this way, DeepRes can be applied to any map, detecting subtle changes in local quality after applying enhancement processes such as isotropic filters or substantially more complex procedures, such as model-based local sharpening, non-model-based methods or denoising, that may be very difficult to follow using current methods. It performs as a human observer expects. The comparison with traditional local resolution indicators is also addressed.

Positive-unlabeled convolutional neural networks for particle picking in cryo-electron micrographs

Cryo-electron microscopy is a popular method for protein structure determination. Identifying a sufficient number of particles for analysis can take months of manual effort. Current computational approaches find many false positives and require significant ad hoc post-processing, especially for unusually-shaped particles. To address these shortcomings, we develop Topaz, an efficient and accurate particle picking pipeline using neural networks trained with a general-purpose positive-unlabeled (PU) learning method. This framework enables particle detection models to be trained with few, sparsely labeled particles and no labeled negatives. Topaz retrieves many more real particles than conventional picking methods while maintaining low false positive rates, is capable of picking challenging unusually-shaped proteins (e.g. small, non-globular, and asymmetric), produces more representative particle sets, and does not require post hoc curation. We demonstrate the performance of Topaz on two difficult datasets and three conventional datasets. Topaz is modular, standalone, free, and open source (http://topaz.csail.mit.edu)

A Super-Clustering Approach for Fully Automated Single Particle Picking in Cryo-EM

Structure determination of proteins and macromolecular complexes by single-particle cryo-electron microscopy (cryo-EM) is poised to revolutionize structural biology. An early challenging step in the cryo-EM pipeline is the detection and selection of particles from two-dimensional micrographs (particle picking). Most existing particle-picking methods require human intervention to deal with complex (irregular) particle shapes and extremely low signal-to-noise ratio (SNR) in cryo-EM images. Here, we design a fully automated super-clustering approach for single particle picking (SuperCryoEMPicker) in cryo-EM micrographs, which focuses on identifying, detecting, and picking particles of the complex and irregular shapes in micrographs with extremely low signal-to-noise ratio (SNR). Our method first applies advanced image processing procedures to improve the quality of the cryo-EM images. The binary mask image-highlighting protein particles are then generated from each individual cryo-EM image using the super-clustering (SP) method, which improves upon base clustering methods (i.e., k-means, fuzzy c-means (FCM), and intensity-based cluster (IBC) algorithm) via a super-pixel algorithm. SuperCryoEMPicker is tested and evaluated on micrographs of β-galactosidase and 80S ribosomes, which are examples of cryo-EM data exhibiting complex and irregular particle shapes. The results show that the super-particle clustering method provides a more robust detection of particles than the base clustering methods, such as k-means, FCM, and IBC. SuperCryoEMPicker automatically and effectively identifies very complex particles from cryo-EM images of extremely low SNR. As a fully automated particle detection method, it has the potential to relieve researchers from laborious, manual particle-labeling work and therefore is a useful tool for cryo-EM protein structure determination.

Automatic local resolution-based sharpening of cryo-EM maps

MOTIVATION

Recent technological advances and computational developments have allowed the reconstruction of Cryo-Electron Microscopy (cryo-EM) maps at near-atomic resolution. On a typical workflow and once the cryo-EM map has been calculated, a sharpening process is usually performed to enhance map visualization, a step that has proven very important in the key task of structural modeling. However, sharpening approaches, in general, neglects the local quality of the map, which is clearly suboptimal.

RESULTS

Here, a new method for local sharpening of cryo-EM density maps is proposed. The algorithm, named LocalDeblur, is based on a local resolution-guided Wiener restoration approach of the original map. The method is fully automatic and, from the user point of view, virtually parameter-free, without requiring either a starting model or introducing any additional structure factor correction or boosting. Results clearly show a significant impact on map interpretability, greatly helping modeling. In particular, this local sharpening approach is especially suitable for maps that present a broad resolution range, as is often the case for membrane proteins or macromolecules with high flexibility, all of them otherwise very suitable and interesting specimens for cryo-EM. To our knowledge, and leaving out the use of local filters, it represents the first application of local resolution in cryo-EM sharpening.

AVAILABILITY AND IMPLEMENTATION

The source code (LocalDeblur) can be found at https://github.com/I2PC/xmipp and can be run using Scipion (http://scipion.cnb.csic.es) (release numbers greater than or equal 1.2.1).

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Structural insights into coronavirus entry

Coronaviruses (CoVs) have caused outbreaks of deadly pneumonia in humans since the beginning of the 21st century. The severe acute respiratory syndrome coronavirus (SARS-CoV) emerged in 2002 and was responsible for an epidemic that spread to five continents with a fatality rate of 10% before being contained in 2003 (with additional cases reported in 2004). The Middle-East respiratory syndrome coronavirus (MERS-CoV) emerged in the Arabian Peninsula in 2012 and has caused recurrent outbreaks in humans with a fatality rate of 35%. SARS-CoV and MERS-CoV are zoonotic viruses that crossed the species barrier using bats/palm civets and dromedary camels, respectively. No specific treatments or vaccines have been approved against any of the six human coronaviruses, highlighting the need to investigate the principles governing viral entry and cross-species transmission as well as to prepare for zoonotic outbreaks which are likely to occur due to the large reservoir of CoVs found in mammals and birds. Here, we review our understanding of the infection mechanism used by coronaviruses derived from recent structural and biochemical studies.

AutoCryoPicker: an unsupervised learning approach for fully automated single particle picking in Cryo-EM images

Background

An important task of macromolecular structure determination by cryo-electron microscopy (cryo-EM) is the identification of single particles in micrographs (particle picking). Due to the necessity of human involvement in the process, current particle picking techniques are time consuming and often result in many false positives and negatives. Adjusting the parameters to eliminate false positives often excludes true particles in certain orientations. The supervised machine learning (e.g. deep learning) methods for particle picking often need a large training dataset, which requires extensive manual annotation. Other reference-dependent methods rely on low-resolution templates for particle detection, matching and picking, and therefore, are not fully automated. These issues motivate us to develop a fully automated, unbiased framework for particle picking.

Results

We design a fully automated, unsupervised approach for single particle picking in cryo-EM micrographs. Our approach consists of three stages: image preprocessing, particle clustering, and particle picking. The image preprocessing is based on multiple techniques including: image averaging, normalization, cryo-EM image contrast enhancement correction (CEC), histogram equalization, restoration, adaptive histogram equalization, guided image filtering, and morphological operations. Image preprocessing significantly improves the quality of original cryo-EM images. Our particle clustering method is based on an intensity distribution model which is much faster and more accurate than traditional K-means and Fuzzy C-Means (FCM) algorithms for single particle clustering. Our particle picking method, based on image cleaning and shape detection with a modified Circular Hough Transform algorithm, effectively detects the shape and the center of each particle and creates a bounding box encapsulating the particles.

Conclusions

AutoCryoPicker can automatically and effectively recognize particle-like objects from noisy cryo-EM micrographs without the need of labeled training data or human intervention making it a useful tool for cryo-EM protein structure determination.

Electronic supplementary material

The online version of this article (10.1186/s12859-019-2926-y) contains supplementary material, which is available to authorized users.

Structural basis for human coronavirus attachment to sialic acid receptors

Coronaviruses cause respiratory tract infections in humans and outbreaks of deadly pneumonia worldwide. Infections are initiated by the transmembrane spike (S) glycoprotein, which binds to host receptors and fuses the viral and cellular membranes. To understand the molecular basis of coronavirus attachment to oligosaccharide receptors, we determined cryo-EM structures of coronavirus OC43 S glycoprotein trimer in isolation and in complex with a 9-O-acetylated sialic acid. We show that the ligand binds with fast kinetics to a surface-exposed groove and that interactions at the identified site are essential for S-mediated viral entry into host cells, but free monosaccharide does not trigger fusogenic conformational changes. The receptor-interacting site is conserved in all coronavirus S glycoproteins that engage 9-O-acetyl-sialogycans, with an architecture similar to those of the ligand-binding pockets of coronavirus hemagglutinin esterases and influenza virus C/D hemagglutinin-esterase fusion glycoproteins. Our results demonstrate these viruses evolved similar strategies to engage sialoglycans at the surface of target cells.

Structural and functional analyses reveal how 9-O-acetyl sialic acid is recognized by the human coronavirus OC43 S glycoprotein and how this interaction promotes viral entry.

Simultaneous Determination of Protein Structure and Dynamics Using Cryo-Electron Microscopy

Cryo-electron microscopy is rapidly emerging as a powerful technique to determine the structures of complex macromolecular systems elusive to other techniques. Because many of these systems are highly dynamical, characterizing their movements is also a crucial step to unravel their biological functions. To achieve this goal, we report an integrative modeling approach to simultaneously determine structure and dynamics of macromolecular systems from cryo-electron microscopy density maps. By quantifying the level of noise in the data and dealing with their ensemble-averaged nature, this approach enables the integration of multiple sources of information to model ensembles of structures and infer their populations. We illustrate the method by characterizing structure and dynamics of the integral membrane receptor STRA6, thus providing insights into the mechanisms by which it interacts with retinol binding protein and translocates retinol across the membrane.

Electron Microscopy Methods for Virus Diagnosis and High Resolution Analysis of Viruses

The term "virosphere" describes both the space where viruses are found and the space they influence, and can extend to their impact on the environment, highlighting the complexity of the interactions involved. Studying the biology of viruses and the etiology of virus disease is crucial to the prevention of viral disease, efficient and reliable virus diagnosis, and virus control. Electron microscopy (EM) is an essential tool in the detection and analysis of virus replication. New EM methods and ongoing technical improvements offer a broad spectrum of applications, allowing in-depth investigation of viral impact on not only the host but also the environment. Indeed, using the most up-to-date electron cryomicroscopy methods, such investigations are now close to atomic resolution. In combination with bioinformatics, the transition from 2D imaging to 3D remodeling allows structural and functional analyses that extend and augment our knowledge of the astonishing diversity in virus structure and lifestyle. In combination with confocal laser scanning microscopy, EM enables live imaging of cells and tissues with high-resolution analysis. Here, we describe the pivotal role played by EM in the study of viruses, from structural analysis to the biological relevance of the viral metagenome (virome).

Thresholding of cryo-EM density maps by false discovery rate control

Cryo-EM now commonly generates close-to-atomic resolution as well as intermediate resolution maps from macromolecules observed in isolation and in situ. Interpreting these maps remains a challenging task owing to poor signal in the highest resolution shells and the necessity to select a threshold for density analysis. In order to facilitate this process, a statistical framework for the generation of confidence maps by multiple hypothesis testing and false discovery rate (FDR) control has been developed. In this way, three-dimensional confidence maps contain signal separated from background noise in the form of local detection rates of EM density values. It is demonstrated that confidence maps and FDR-based thresholding can be used for the interpretation of near-atomic resolution single-particle structures as well as lower resolution maps determined by subtomogram averaging. Confidence maps represent a conservative way of interpreting molecular structures owing to minimized noise. At the same time they provide a detection error with respect to background noise, which is associated with the density and is particularly beneficial for the interpretation of weaker cryo-EM densities in cases of conformational flexibility and lower occupancy of bound molecules and ions in the structure.

A Local Agreement Filtering Algorithm for Transmission EM Reconstructions

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We propose an algorithm, LAFTER, that recovers features with more signal than noise from half maps.

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LAFTER is shown to recover features over a wide range of FSCs and local signal-to-noise ratios.

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We suggest effective local noise suppression be evaluated by comparing the filter-sum xFSC to C_ref.

We present LAFTER, an algorithm for de-noising single particle reconstructions from cryo-EM.

Single particle analysis entails the reconstruction of high-resolution volumes from tens of thousands of particle images with low individual signal-to-noise. Imperfections in this process result in substantial variations in the local signal-to-noise ratio within the resulting reconstruction, complicating the interpretation of molecular structure. An effective local de-noising filter could therefore improve interpretability and maximise the amount of useful information obtained from cryo-EM maps.

LAFTER is a local de-noising algorithm based on a pair of serial real-space filters. It compares independent half-set reconstructions to identify and retain shared features that have power greater than the noise. It is capable of recovering features across a wide range of signal-to-noise ratios, and we demonstrate recovery of the strongest features at Fourier shell correlation (FSC) values as low as 0.144 over a 256³-voxel cube. A fast and computationally efficient implementation of LAFTER is freely available.

We also propose a new way to evaluate the effectiveness of real-space filters for noise suppression, based on the correspondence between two FSC curves: 1) the FSC between the filtered and unfiltered volumes, and 2) C_ref, the FSC between the unfiltered volume and a hypothetical noiseless volume, which can readily be estimated from the FSC between two half-set reconstructions.

A Multi-model Approach to Assessing Local and Global Cryo-EM Map Quality

There does not currently exist a standardized indicator of how well cryo-EM-derived models represent the density from which they were generated. We present a straightforward methodology that utilizes freely available tools to generate a suite of independent models and to evaluate their convergence in an EM density. These analyses provide both a quantitative and qualitative assessment of the precision of the models and their representation of the density, respectively, while concurrently providing a platform for assessing both global and local EM map quality. We further use standardized datasets to provide an expected deviation within a suite of models refined against EM maps reported to be at 5 Å resolution or better. Associating multiple atomic models with a deposited EM map provides a rapid and accessible reporter of convergence, a strong indicator of highly resolved molecular detail, and is an important step toward an FSC-independent assessment of map and model quality.

An Algorithm for Enhancing the Image Contrast of Electron Tomography

Three-dimensional (3D) reconstruction of a single protein molecule is essential for understanding the relationship between the structural dynamics and functions of the protein. Electron tomography (ET) provides a tool for imaging an individual particle of protein from a series of tilted angles. Individual-particle electron tomography (IPET) provides an approach for reconstructing a 3D density map from a single targeted protein particle (without averaging from different particles of this type of protein), in which the target particle was imaged from a series of tilting angles. However, owing to radiation damage limitations, low-dose images (high noise, and low image contrast) are often challenging to be aligned for 3D reconstruction at intermediate resolution (1-3 nm). Here, we propose a computational method to enhance the image contrast, without increasing any experimental dose, for IPET 3D reconstruction. Using an edge-preserving smoothing-based multi-scale image decomposition algorithm, this method can detect the object against a high-noise background and enhance the object image contrast without increasing the noise level or significantly decreasing the image resolution. The method was validated by using both negative staining (NS) ET and cryo-ET images. The successful 3D reconstruction of a small molecule (<100 kDa) indicated that this method can be used as a supporting tool to current ET 3D reconstruction methods for studying protein dynamics via structure determination from each individual particle of the same type of protein.

Map segmentation, automated model-building and their application to the Cryo-EM Model Challenge

A recently-developed method for identifying a compact, contiguous region representing the unique part of a density map was applied to 218 Cryo-EM maps with resolutions of 4.5 Å or better. The key elements of the segmentation procedure are (1) identification of all regions of density above a threshold and (2) choice of a unique set of these regions, taking symmetry into consideration, that maximize connectivity and compactness. This segmentation approach was then combined with tools for automated map sharpening and model-building to generate models for the 12 maps in the 2016 Cryo-EM Model Challenge in a fully automated manner. The resulting models have completeness from 24% to 82% and RMS distances from reference interpretations of 0.6 Å-2.1 Å.