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

Miffi: Improving the accuracy of CNN-based cryo-EM micrograph filtering with fine-tuning and Fourier space information

Efficient and high-accuracy filtering of cryo-electron microscopy (cryo-EM) micrographs is an emerging challenge with the growing speed of data collection and sizes of datasets. Convolutional neural networks (CNNs) are machine learning models that have been proven successful in many computer vision tasks, and have been previously applied to cryo-EM micrograph filtering. In this work, we demonstrate that two strategies, fine-tuning models from pretrained weights and including the power spectrum of micrographs as input, can greatly improve the attainable prediction accuracy of CNN models. The resulting software package, Miffi, is open-source and freely available for public use (https://github.com/ando-lab/miffi).

Single particle cryo-electron microscopy with an enhanced 200 kV cryo-TEM configuration achieves near-atomic resolution

Single particle cryogenic electron microscopy (cryo-EM) as a structural biology methodology has become increasingly attractive and accessible to investigators in both academia and industry as this ever-advancing technology enables successful structural determination of a wide range of protein and nucleic acid targets. Although data for many high resolution cryo-EM structures are still obtained using a 300 kV cryogenic transmission electron microscope (cryo-TEM), a modern 200 kV cryo-TEM equipped with an advanced direct electron detector and energy filter is a cost-effective choice for most single particle applications, routinely achieving sub 3 angstrom (Å) resolution. Here, we systematically evaluate performance of one such high-end configuration - a 200 kV Glacios microscope coupled with a Falcon 4 direct electron detector and Selectris energy filter (Glacios-F4-S). First, we evaluated data quality on the standard benchmarking sample, rabbit muscle aldolase, using three of the most frequently used cryo-EM data collection software: SerialEM, Leginon and EPU, and found that - despite sample heterogeneity - all final reconstructions yield same overall resolutions of 2.6 Å and map quality when using either of the three software. Furthermore, comparison between Glacios-F4-S and a 300 kV cryo-TEM (Titan Krios with Falcon 4) revealed nominal resolution differences in overall reconstructions of a reconstituted human nucleosome core particle, achieving 2.8 and 2.5 Å, respectively. Finally, we performed comparative data analysis on the human RAD51 paralog complex, BCDX2, a four-protein complex of approximately 150 kilodaltons, and found that a small dataset (≤1,000 micrographs) was sufficient to generate a 3.3 Å reconstruction, with sufficient detail to resolve co-bound ligands, AMP-PNP and Mg +2 . In summary, this study provides evidence that the Glacios-F4-S operates equally well with all standard data collection software, and is sufficient to obtain high resolution structural information of novel macromolecular complexes, readily acquiring single particle data rivaling that of 300 kV cryo-TEMs.

Rapid structural analysis of bacterial ribosomes in situ

Rapid structural analysis of purified proteins and their complexes has become increasingly common thanks to key methodological advances in cryo-electron microscopy (cryo-EM) and associated data processing software packages. In contrast, analogous structural analysis in cells via cryo-electron tomography (cryo-ET) remains challenging due to critical technical bottlenecks, including low-throughput sample preparation and imaging, and laborious data processing methods. Here, we describe the development of a rapid in situ cryo-ET sample preparation and data analysis workflow that results in the routine determination of sub-nm resolution ribosomal structures. We apply this workflow to E. coli, producing a 5.8 Å structure of the 70S ribosome from cells in less than 10 days, and we expect this workflow will be widely applicable to related bacterial samples.

Miffi: Improving the accuracy of CNN-based cryo-EM micrograph filtering with fine-tuning and Fourier space information

Efficient and high-accuracy filtering of cryo-electron microscopy (cryo-EM) micrographs is an emerging challenge with the growing speed of data collection and sizes of datasets. Convolutional neural networks (CNNs) are machine learning models that have been proven successful in many computer vision tasks, and have been previously applied to cryo-EM micrograph filtering. In this work, we demonstrate that two strategies, fine-tuning models from pretrained weights and including the power spectrum of micrographs as input, can greatly improve the attainable prediction accuracy of CNN models. The resulting software package, Miffi, is open-source and freely available for public use (https://github.com/ando-lab/miffi).

Structure of mechanically activated ion channel OSCA2.3 reveals mobile elements in the transmembrane domain

Members of the OSCA/TMEM63 family are mechanically activated ion channels and structures of some OSCA members have revealed the architecture of these channels and structural features that are potentially involved in mechanosensation. However, these structures are all in a similar state and information about the motion of different elements of the structure is limited, preventing a deeper understanding of how these channels work. Here, we used cryoelectron microscopy to determine high-resolution structures of Arabidopsis thaliana OSCA1.2 and OSCA2.3 in peptidiscs. The structure of OSCA1.2 matches previous structures of the same protein in different environments. Yet, in OSCA2.3, the TM6a-TM7 linker adopts a different conformation that constricts the pore on its cytoplasmic side. Furthermore, coevolutionary sequence analysis uncovered a conserved interaction between the TM6a-TM7 linker and the beam-like domain (BLD). Our results reveal conformational heterogeneity and differences in conserved interactions between the TMD and BLD among members of the OSCA family.

Scaling up cryo-EM for biology and chemistry: The journey from niche technology to mainstream method

Cryoelectron microscopy (cryo-EM) methods have made meaningful contributions in a wide variety of scientific research fields. In structural biology, cryo-EM routinely elucidates molecular structure from isolated biological macromolecular complexes or in a cellular context by harnessing the high-resolution power of the electron in order to image samples in a frozen, hydrated environment. For structural chemistry, the cryo-EM method popularly known as microcrystal electron diffraction (MicroED) has facilitated atomic structure generation of peptides and small molecules from their three-dimensional crystal forms. As cryo-EM has grown from an emerging technology, it has undergone modernization to enable multimodal transmission electron microscopy (TEM) techniques becoming more routine, reproducible, and accessible to accelerate research across multiple disciplines. We review recent advances in modern cryo-EM and assess how they are contributing to the future of the field with an eye to the past.

Mass spectrometers as cryoEM grid preparation instruments

Structure determination by single-particle cryoEM has matured into a core structural biology technique. Despite many methodological advancements, most cryoEM grids are still prepared using the plunge-freezing method developed ∼40 years ago. Embedding samples in thin films and exposing them to the air-water interface often leads to sample damage and preferential orientation of the particles. Using native mass spectrometry to create cryoEM samples, potentially avoids these problems and allows the use of mass spectrometry sample isolation techniques during EM grid creation. We review the recent publications that have demonstrated protein complexes can be ionized, flown through the mass spectrometer, gently landed onto EM grids, imaged, and reconstructed in 3D. Although many uncertainties and challenges remain, the combination of cryoEM and MS has great potential.

Development of Single Molecule Techniques for Sensing and Manipulation of CRISPR and Polymerase Enzymes

Clustered regularly interspaced short palindromic repeats (CRISPR) and polymerases are powerful enzymes and their diverse applications in genomics, proteomics, and transcriptomics have revolutionized the biotechnology industry today. CRISPR has been widely adopted for genomic editing applications and Polymerases can efficiently amplify genomic transcripts via polymerase chain reaction (PCR). Further investigations into these enzymes can reveal specific details about their mechanisms that greatly expand their use. Single-molecule techniques are an effective way to probe enzymatic mechanisms because they may resolve intermediary conformations and states with greater detail than ensemble or bulk biosensing techniques. This review discusses various techniques for sensing and manipulation of single biomolecules that can help facilitate and expedite these discoveries. Each platform is categorized as optical, mechanical, or electronic. The methods, operating principles, outputs, and utility of each technique are briefly introduced, followed by a discussion of their applications to monitor and control CRISPR and Polymerases at the single molecule level, and closing with a brief overview of their limitations and future prospects.

Tethered agonist activated ADGRF1 structure and signalling analysis reveal basis for G protein coupling

Adhesion G Protein Coupled Receptors (aGPCRs) have evolved an activation mechanism to translate extracellular force into liberation of a tethered agonist (TA) to effect cell signalling. We report here that ADGRF1 can signal through all major G protein classes and identify the structural basis for a previously reported Gα_q preference by cryo-EM. Our structure shows that Gα_q preference in ADGRF1 may derive from tighter packing at the conserved F569 of the TA, altering contacts between TM helix I and VII, with a concurrent rearrangement of TM helix VII and helix VIII at the site of Gα recruitment. Mutational studies of the interface and of contact residues within the 7TM domain identify residues critical for signalling, and suggest that Gα_s signalling is more sensitive to mutation of TA or binding site residues than Gα_q. Our work advances the detailed molecular understanding of aGPCR TA activation, identifying features that potentially explain preferential signal modulation.

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

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

Learning to automate cryo-electron microscopy data collection with Ptolemy

Over the past decade, cryo-electron microscopy (cryoEM) has emerged as an important method for determining near-native, near-atomic resolution 3D structures of biological macromolecules. To meet the increasing demand for cryoEM, automated methods that improve throughput and efficiency of microscope operation are needed. Currently, the targeting algorithms provided by most data-collection software require time-consuming manual tuning of parameters for each grid, and, in some cases, operators must select targets completely manually. However, the development of fully automated targeting algorithms is non-trivial, because images often have low signal-to-noise ratios and optimal targeting strategies depend on a range of experimental parameters and macromolecule behaviors that vary between projects and collection sessions. To address this, Ptolemy provides a pipeline to automate low- and medium-magnification targeting using a suite of purpose-built computer vision and machine-learning algorithms, including mixture models, convolutional neural networks and U-Nets. Learned models in this pipeline are trained on a large set of images from real-world cryoEM data-collection sessions, labeled with locations selected by human operators. These models accurately detect and classify regions of interest in low- and medium-magnification images, and generalize to unseen sessions, as well as to images collected on different microscopes at another facility. This open-source, modular pipeline can be integrated with existing microscope control software to enable automation of cryoEM data collection and can serve as a foundation for future cryoEM automation software.

Smart data collection for CryoEM

This report provides an overview of the discussions, presentations, and consensus thinking from the Workshop on Smart Data Collection for CryoEM held at the New York Structural Biology Center on April 6-7, 2022. The goal of the workshop was to address next generation data collection strategies that integrate machine learning and real-time processing into the workflow to reduce or eliminate the need for operator intervention.

Evolving Experimental Techniques for Structure-Based Drug Design

Structure-based drug design (SBDD) is a prominent method in rational drug development and has traditionally benefitted from the atomic models of protein targets obtained using X-ray crystallography at cryogenic temperatures. In this perspective, we highlight recent advances in the development of structural techniques that are capable of probing dynamic information about protein targets. First, we discuss advances in the field of X-ray crystallography including serial room-temperature crystallography as a method for obtaining high-resolution conformational dynamics of protein-inhibitor complexes. Next, we look at cryogenic electron microscopy (cryoEM), another high-resolution technique that has recently been used to study proteins and protein complexes that are too difficult to crystallize. Finally, we present small-angle X-ray scattering (SAXS) as a potential high-throughput screening tool to identify inhibitors that target protein complexes and protein oligomerization.

Artificial Intelligence in Cryo-Electron Microscopy

Cryo-electron microscopy (cryo-EM) has become an unrivaled tool for determining the structure of macromolecular complexes. The biological function of macromolecular complexes is inextricably tied to the flexibility of these complexes. Single particle cryo-EM can reveal the conformational heterogeneity of a biochemically pure sample, leading to well-founded mechanistic hypotheses about the roles these complexes play in biology. However, the processing of increasingly large, complex datasets using traditional data processing strategies is exceedingly expensive in both user time and computational resources. Current innovations in data processing capitalize on artificial intelligence (AI) to improve the efficiency of data analysis and validation. Here, we review new tools that use AI to automate the data analysis steps of particle picking, 3D map reconstruction, and local resolution determination. We discuss how the application of AI moves the field forward, and what obstacles remain. We also introduce potential future applications of AI to use cryo-EM in understanding protein communities in cells.

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.

Recent Technical Advances in Sample Preparation for Single-Particle Cryo-EM

Cryo-sample preparation is a vital step in the process of obtaining high-resolution structures of macromolecules by using the single-particle cryo–electron microscopy (cryo-EM) method; however, cryo-sample preparation is commonly hampered by high uncertainty and low reproducibility. Specifically, the existence of air-water interfaces during the sample vitrification process could cause protein denaturation and aggregation, complex disassembly, adoption of preferred orientations, and other serious problems affecting the protein particles, thereby making it challenging to pursue high-resolution 3D reconstruction. Therefore, sample preparation has emerged as a critical research topic, and several new methods for application at various preparation stages have been proposed to overcome the aforementioned hurdles. Here, we summarize the methods developed for enhancing the quality of cryo-samples at distinct stages of sample preparation, and we offer insights for developing future strategies based on diverse viewpoints. We anticipate that cryo-sample preparation will no longer be a limiting step in the single-particle cryo-EM field as increasing numbers of methods are developed in the near future, which will ultimately benefit the entire research community.

Better, Faster, Cheaper: Recent Advances in Cryo-Electron Microscopy

Cryo-electron microscopy (cryo-EM) continues its remarkable growth as a method for visualizing biological objects, which has been driven by advances across the entire pipeline. Developments in both single-particle analysis and in situ tomography have enabled more structures to be imaged and determined to better resolutions, at faster speeds, and with more scientists having improved access. This review highlights recent advances at each stageof the cryo-EM pipeline and provides examples of how these techniques have been used to investigate real-world problems, including antibody development against the SARS-CoV-2 spike during the recent COVID-19 pandemic.

Data-driven determination of number of discrete conformations in single-particle cryo-EM

BACKGROUND AND OBJECTIVE

One of the strengths of single-particle cryo-EM compared to other structural determination techniques is its ability to image heterogeneous samples containing multiple molecular species, different oligomeric states or distinct conformations. This is achieved using routines for in-silico 3D classification that are now well established in the field and have successfully been used to characterize the structural heterogeneity of important biomolecules. These techniques, however, rely on expert-user knowledge and trial-and-error experimentation to determine the correct number of conformations, making it a labor intensive, subjective, and difficult to reproduce procedure.

METHODS

We propose an approach to address the problem of automatically determining the number of discrete conformations present in heterogeneous single-particle cryo-EM datasets. We do this by systematically evaluating all possible partitions of the data and selecting the result that maximizes the average variance of similarities measured between particle images and the corresponding 3D reconstructions.

RESULTS

Using this strategy, we successfully analyzed datasets of heterogeneous protein complexes, including: 1) in-silico mixtures obtained by combining closely related antibody-bound HIV-1 Env trimers and other important membrane channels, and 2) naturally occurring mixtures from diverse and dynamic protein complexes representing varying degrees of structural heterogeneity and conformational plasticity.

CONCLUSIONS

The availability of unsupervised strategies for 3D classification combined with existing approaches for fully automatic pre-processing and 3D refinement, represents an important step towards converting single-particle cryo-EM into a high-throughput technique.

The progressive spectral signal-to-noise ratio of cryo-electron micrograph movies as a tool to assess quality and radiation damage

BACKGROUND AND OBJECTIVE

The quality of a cryoEM reconstruction is fundamentally a function of the signal-to-noise ratio (SNR) of the original micrographs. The SNR embodies multiple aspects of image formation, including microscope details and alignment, specimen composition and thickness, how it is recorded, and how the specimen degrades during imaging. With the advent of direct electron detectors and the recording of a series of images for each micrograph (a movie), we have an opportunity to count every electron and derive fully quantitative results. After alignment of the movie frames of a micrograph, we can calculate the SNR, or its spatial frequency equivalent, the spectral SNR (SSNR). This SSNR reflects residual movement between frames and the progressive effect of radiation damage. The goal is to develop a quantitative analysis of the SSNR and radiation damage to assess and improve the quality of micrographs.

METHODS

Several test cases were selected from the EMPIAR database and ten micrograph movies downloaded for each case. The movie frames were aligned as rigid bodies to compensate for stage and support movement. The SSNR for subsets of frames was then calculated to assess the effect of residual movement. The progressive SSNR (PSSNR) was subsequently calculated to determine the decrease in signal accumulation as a result of radiation damage.

RESULTS

In all cases the alignment of the movie frames compensated for global movement to the extent that the effect on the SSNR is negligible. The subset SSNR can be used as a tool to further confirm the extent of residual movement. The progressive SSNR indicates an increase in value up to an asymptote, consistent with the theory for radiation damage. Fitting these curves gives the inherent SNR before exposure, and the critical dose, which decreases with spatial frequency with an exponential parameter roughly between one and two.

CONCLUSIONS

The implementation of the PSSNR for movie frames provides a tool for assessing micrograph quality and progression of radiation damage. The estimation of the critical dose further quantifies radiation damage and may shed some light on the mechanisms of damage. These are likely both a function of the specimen composition and the imaging parameters used.

Exploring cryo-electron microscopy with molecular dynamics

Single particle analysis cryo-electron microscopy (EM) and molecular dynamics (MD) have been complimentary methods since cryo-EM was first applied to the field of structural biology. The relationship started by biasing structural models to fit low-resolution cryo-EM maps of large macromolecular complexes not amenable to crystallization. The connection between cryo-EM and MD evolved as cryo-EM maps improved in resolution, allowing advanced sampling algorithms to simultaneously refine backbone and sidechains. Moving beyond a single static snapshot, modern inferencing approaches integrate cryo-EM and MD to generate structural ensembles from cryo-EM map data or directly from the particle images themselves. We summarize the recent history of MD innovations in the area of cryo-EM modeling. The merits for the myriad of MD based cryo-EM modeling methods are discussed, as well as, the discoveries that were made possible by the integration of molecular modeling with cryo-EM. Lastly, current challenges and potential opportunities are reviewed.

Applications of deep learning in electron microscopy

We review the growing use of machine learning in electron microscopy (EM) driven in part by the availability of fast detectors operating at kiloHertz frame rates leading to large data sets that cannot be processed using manually implemented algorithms. We summarize the various network architectures and error metrics that have been applied to a range of EM-related problems including denoising and inpainting. We then provide a review of the application of these in both physical and life sciences, highlighting how conventional networks and training data have been specifically modified for EM.

Structural insights into the Venus flytrap mechanosensitive ion channel Flycatcher1

Flycatcher1 (FLYC1), a MscS homolog, has recently been identified as a candidate mechanosensitive (MS) ion channel involved in Venus flytrap prey recognition. FLYC1 is a larger protein and its sequence diverges from previously studied MscS homologs, suggesting it has unique structural features that contribute to its function. Here, we characterize FLYC1 by cryo-electron microscopy, molecular dynamics simulations, and electrophysiology. Akin to bacterial MscS and plant MSL1 channels, we find that FLYC1 central core includes side portals in the cytoplasmic cage that regulate ion preference and conduction, by identifying critical residues that modulate channel conductance. Topologically unique cytoplasmic flanking regions can adopt ‘up’ or ‘down’ conformations, making the channel asymmetric. Disruption of an up conformation-specific interaction severely delays channel deactivation by 40-fold likely due to stabilization of the channel open state. Our results illustrate novel structural features and likely conformational transitions that regulate mechano-gating of FLYC1.

Flycatcher1 (FLYC1) is a candidate mechanosensitive channel involved in Venus flytrap touch-induced prey capture. Here, the authors report structural and functional details of FLYC1, with insights into gating conformational transitions.

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.

Cloud computing platforms to support cryo-EM structure determination

Leveraging the power of single-particle cryo-electron microscopy (cryo-EM) requires robust and accessible computational infrastructure. Here, we summarize the cloud computing landscape and picture the outlook of a hybrid cryo-EM computing workflow, and make suggestions to the community to facilitate a future for cryo-EM that integrates into cloud computing infrastructure.

Cryo-EM Analyses Permit Visualization of Structural Polymorphism of Biological Macromolecules

The functions of biological macromolecules are often associated with conformational malleability of the structures. This phenomenon of chemically identical molecules with different structures is coined structural polymorphism. Conventionally, structural polymorphism is observed directly by structural determination at the density map level from X-ray crystal diffraction. Although crystallography approach can report the conformation of a macromolecule with the position of each atom accurately defined in it, the exploration of structural polymorphism and interpreting biological function in terms of crystal structures is largely constrained by the crystal packing. An alternative approach to studying the macromolecule of interest in solution is thus desirable. With the advancement of instrumentation and computational methods for image analysis and reconstruction, cryo-electron microscope (cryo-EM) has been transformed to be able to produce “in solution” structures of macromolecules routinely with resolutions comparable to crystallography but without the need of crystals. Since the sample preparation of single-particle cryo-EM allows for all forms co-existing in solution to be simultaneously frozen, the image data contain rich information as to structural polymorphism. The ensemble of structure information can be subsequently disentangled through three-dimensional (3D) classification analyses. In this review, we highlight important examples of protein structural polymorphism in relation to allostery, subunit cooperativity and function plasticity recently revealed by cryo-EM analyses, and review recent developments in 3D classification algorithms including neural network/deep learning approaches that would enable cryo-EM analyese in this regard. Finally, we brief the frontier of cryo-EM structure determination of RNA molecules where resolving the structural polymorphism is at dawn.

Structural and mechanistic basis for protein glutamylation by the kinase fold

The kinase domain transfers phosphate from ATP to substrates. However, the Legionella effector SidJ adopts a kinase fold, yet catalyzes calmodulin (CaM)-dependent glutamylation to inactivate the SidE ubiquitin ligases. The structural and mechanistic basis in which the kinase domain catalyzes protein glutamylation is unknown. Here we present cryo-EM reconstructions of SidJ:CaM:SidE reaction intermediate complexes. We show that the kinase-like active site of SidJ adenylates an active-site Glu in SidE, resulting in the formation of a stable reaction intermediate complex. An insertion in the catalytic loop of the kinase domain positions the donor Glu near the acyl-adenylate for peptide bond formation. Our structural analysis led us to discover that the SidJ paralog SdjA is a glutamylase that differentially regulates the SidE ligases during Legionella infection. Our results uncover the structural and mechanistic basis in which the kinase fold catalyzes non-ribosomal amino acid ligations and reveal an unappreciated level of SidE-family regulation.

Bipartite binding and partial inhibition links DEPTOR and mTOR in a mutually antagonistic embrace

The mTORC1 kinase complex regulates cell growth, proliferation, and survival. Because mis-regulation of DEPTOR, an endogenous mTORC1 inhibitor, is associated with some cancers, we reconstituted mTORC1 with DEPTOR to understand its function. We find that DEPTOR is a unique partial mTORC1 inhibitor that may have evolved to preserve feedback inhibition of PI3K. Counterintuitively, mTORC1 activated by RHEB or oncogenic mutation is much more potently inhibited by DEPTOR. Although DEPTOR partially inhibits mTORC1, mTORC1 prevents this inhibition by phosphorylating DEPTOR, a mutual antagonism that requires no exogenous factors. Structural analyses of the mTORC1/DEPTOR complex showed DEPTOR's PDZ domain interacting with the mTOR FAT region, and the unstructured linker preceding the PDZ binding to the mTOR FRB domain. The linker and PDZ form the minimal inhibitory unit, but the N-terminal tandem DEP domains also significantly contribute to inhibition.

Challenges in sample preparation and structure determination of amyloids by cryo-EM

Amyloids share a common architecture but play disparate biological roles in processes ranging from bacterial defense mechanisms to protein misfolding diseases. Their structures are highly polymorphic, which makes them difficult to study by X-ray diffraction or NMR spectroscopy. Our understanding of amyloid structures is due in large part to recent advances in the field of cryo-EM, which allows for determining the polymorphs separately. In this review, we highlight the main stepping stones leading to the substantial number of high-resolution amyloid fibril structures known today as well as recent developments regarding automation and software in cryo-EM. We discuss that sample preparation should move closer to physiological conditions to understand how amyloid aggregation and disease are linked. We further highlight new approaches to address heterogeneity and polymorphism of amyloid fibrils in EM image processing and give an outlook to the upcoming challenges in researching the structural biology of amyloids.

Understanding the invisible hands of sample preparation for cryo-EM

Cryo-electron microscopy (cryo-EM) is rapidly becoming an attractive method in the field of structural biology. With the exploding popularity of cryo-EM, sample preparation must evolve to prevent congestion in the workflow. The dire need for improved microscopy samples has led to a diversification of methods. This Review aims to categorize and explain the principles behind various techniques in the preparation of vitrified samples for the electron microscope. Various aspects and challenges in the workflow are discussed, from sample optimization and carriers to deposition and vitrification. Reliable and versatile specimen preparation remains a challenge, and we hope to give guidelines and posit future directions for improvement.

Mechanism of filament formation in UPA-promoted CARD8 and NLRP1 inflammasomes

NLRP1 and CARD8 are related cytosolic sensors that upon activation form supramolecular signalling complexes known as canonical inflammasomes, resulting in caspase-1 activation, cytokine maturation and/or pyroptotic cell death. NLRP1 and CARD8 use their C-terminal (CT) fragments containing a caspase recruitment domain (CARD) and the UPA (conserved in UNC5, PIDD, and ankyrins) subdomain for self-oligomerization, which in turn form the platform to recruit the inflammasome adaptor ASC (apoptosis-associated speck-like protein containing a CARD) or caspase-1, respectively. Here, we report cryo-EM structures of NLRP1-CT and CARD8-CT assemblies, in which the respective CARDs form central helical filaments that are promoted by oligomerized, but flexibly linked, UPAs surrounding the filaments. Through biochemical and cellular approaches, we demonstrate that the UPA itself reduces the threshold needed for NLRP1-CT and CARD8-CT filament formation and signalling. Structural analyses provide insights on the mode of ASC recruitment by NLRP1-CT and the contrasting direct recruitment of caspase-1 by CARD8-CT. We also discover that subunits in the central NLRP1CARD filament dimerize with additional exterior CARDs, which roughly doubles its thickness and is unique among all known CARD filaments. Finally, we engineer and determine the structure of an ASCCARD-caspase-1CARD octamer, which suggests that ASC uses opposing surfaces for NLRP1, versus caspase-1, recruitment. Together these structures capture the architecture and specificity of the active NLRP1 and CARD8 inflammasomes in addition to key heteromeric CARD-CARD interactions governing inflammasome signalling.

TranSPHIRE: automated and feedback-optimized on-the-fly processing for cryo-EM

Single particle cryo-EM requires full automation to allow high-throughput structure determination. Although software packages exist where parts of the cryo-EM pipeline are automated, a complete solution that offers reliable on-the-fly processing, resulting in high-resolution structures, does not exist. Here we present TranSPHIRE: A software package for fully-automated processing of cryo-EM datasets during data acquisition. TranSPHIRE transfers data from the microscope, automatically applies the common pre-processing steps, picks particles, performs 2D clustering, and 3D refinement parallel to image recording. Importantly, TranSPHIRE introduces a machine learning-based feedback loop to re-train its picking model to adapt to any given data set live during processing. This elegant approach enables TranSPHIRE to process data more effectively, producing high-quality particle stacks. TranSPHIRE collects and displays all metrics and microscope settings to allow users to quickly evaluate data during acquisition. TranSPHIRE can run on a single work station and also includes the automated processing of filaments.

High-resolution cryo-EM using beam-image shift at 200 keV

Recent advances in single-particle cryo-electron microscopy (cryo-EM) data collection utilize beam-image shift to improve throughput. Despite implementation on 300 keV cryo-EM instruments, it remains unknown how well beam-image-shift data collection affects data quality on 200 keV instruments and the extent to which aberrations can be computationally corrected. To test this, a cryo-EM data set for aldolase was collected at 200 keV using beam-image shift and analyzed. This analysis shows that the instrument beam tilt and particle motion initially limited the resolution to 4.9 Å. After particle polishing and iterative rounds of aberration correction in RELION, a 2.8 Å resolution structure could be obtained. This analysis demonstrates that software correction of microscope aberrations can provide a significant improvement in resolution at 200 keV.

Automating Decision Making in the Cryo-EM Pre-processing Pipeline

In this issue of Structure, Li et al. (2020) describe machine learning methods that automate decision making in the cryo-EM pre-processing pipeline, eliminating the need for user-subjective decisions. They successfully tested this pipeline for a wide range of datasets, demonstrating the proof of concept and the efficiency of this approach.

Advances in methods for atomic resolution macromolecular structure determination

Recent technical advances have dramatically increased the power and scope of structural biology. New developments in high-resolution cryo-electron microscopy, serial X-ray crystallography, and electron diffraction have been especially transformative. Here we highlight some of the latest advances and current challenges at the frontiers of atomic resolution methods for elucidating the structures and dynamical properties of macromolecules and their complexes.

Structural Biology in the Multi-Omics Era

Rapid developments in cryogenic electron microscopy have opened new avenues to probe the structures of protein assemblies in their near native states. Recent studies have begun applying single -particle analysis to heterogeneous mixtures, revealing the potential of structural-omics approaches that combine the power of mass spectrometry and electron microscopy. Here we highlight advances and challenges in sample preparation, data processing, and molecular modeling for handling increasingly complex mixtures. Such advances will help structural-omics methods extend to cellular-level models of structural biology.