Classification of Single Particles from Human Cell Extract Reveals Distinct Structures

Algebraic constraints and algorithms for common lines in cryo-EM

We revisit the topic of common lines between projection images in single-particle cryo-electron microscopy (cryo-EM). We derive a novel low-rank constraint on a certain 2n × n matrix storing properly scaled basis vectors for the common lines between n projection images of one molecular conformation. Using this algebraic constraint and others, we give optimization algorithms to denoise common lines and recover the unknown 3D rotations associated with the images. As an application, we develop a clustering algorithm to partition a set of noisy images into homogeneous communities using common lines, in the case of discrete heterogeneity in cryo-EM. We demonstrate the methods on synthetic and experimental datasets.

Bridging structural and cell biology with cryo-electron microscopy

Most life scientists would agree that understanding how cellular processes work requires structural knowledge about the macromolecules involved. For example, deciphering the double-helical nature of DNA revealed essential aspects of how genetic information is stored, copied and repaired. Yet, being reductionist in nature, structural biology requires the purification of large amounts of macromolecules, often trimmed off larger functional units. The advent of cryogenic electron microscopy (cryo-EM) greatly facilitated the study of large, functional complexes and generally of samples that are hard to express, purify and/or crystallize. Nevertheless, cryo-EM still requires purification and thus visualization outside of the natural context in which macromolecules operate and coexist. Conversely, cell biologists have been imaging cells using a number of fast-evolving techniques that keep expanding their spatial and temporal reach, but always far from the resolution at which chemistry can be understood. Thus, structural and cell biology provide complementary, yet unconnected visions of the inner workings of cells. Here we discuss how the interplay between cryo-EM and cryo-electron tomography, as a connecting bridge to visualize macromolecules in situ, holds great promise to create comprehensive structural depictions of macromolecules as they interact in complex mixtures or, ultimately, inside the cell itself.

Moment-based metrics for molecules computable from cryogenic electron microscopy images

Single-particle cryogenic electron microscopy (cryo-EM) is an imaging technique capable of recovering the high-resolution three-dimensional (3D) structure of biological macromolecules from many noisy and randomly oriented projection images. One notable approach to 3D reconstruction, known as Kam's method, relies on the moments of the two-dimensional (2D) images. Inspired by Kam's method, we introduce a rotationally invariant metric between two molecular structures, which does not require 3D alignment. Further, we introduce a metric between a stack of projection images and a molecular structure, which is invariant to rotations and reflections and does not require performing 3D reconstruction. Additionally, the latter metric does not assume a uniform distribution of viewing angles. We demonstrate the uses of the new metrics on synthetic and experimental datasets, highlighting their ability to measure structural similarity.

DeepSLICEM: Clustering CryoEM particles using deep image and similarity graph representations

Finding the 3D structure of proteins and their complexes has several applications, such as developing vaccines that target viral proteins effectively. Methods such as cryogenic electron microscopy (cryo-EM) have improved in their ability to capture high-resolution images, and when applied to a purified sample containing copies of a macromolecule, they can be used to produce a high-quality snapshot of different 2D orientations of the macromolecule, which can be combined to reconstruct its 3D structure. Instead of purifying a sample so that it contains only one macromolecule, a process that can be difficult, time-consuming, and expensive, a cell sample containing multiple particles can be photographed directly and separated into its constituent particles using computational methods. Previous work, SLICEM, has separated 2D projection images of different particles into their respective groups using 2 methods, clustering a graph with edges weighted by pairwise similarities of common lines of the 2D projections. In this work, we develop DeepSLICEM, a pipeline that clusters rich representations of 2D projections, obtained by combining graphical features from a similarity graph based on common lines, with additional image features extracted from a convolutional neural network. DeepSLICEM explores 6 pretrained convolutional neural networks and one supervised Siamese CNN for image representation, 10 pretrained deep graph neural networks for similarity graph node representations, and 4 methods for clustering, along with 8 methods for directly clustering the similarity graph. On 6 synthetic and experimental datasets, the DeepSLICEM pipeline finds 92 method combinations achieving better clustering accuracy than previous methods from SLICEM. Thus, in this paper, we demonstrate that deep neural networks have great potential for accurately separating mixtures of 2D projections of different macromolecules computationally.

Enabling cryo-EM density interpretation from yeast native cell extracts by proteomics data and AlphaFold structures

In the cellular context, proteins participate in communities to perform their function. The detection and identification of these communities as well as in-community interactions has long been the subject of investigation, mainly through proteomics analysis with mass spectrometry. With the advent of cryogenic electron microscopy and the "resolution revolution," their visualization has recently been made possible, even in complex, native samples. The advances in both fields have resulted in the generation of large amounts of data, whose analysis requires advanced computation, often employing machine learning approaches to reach the desired outcome. In this work, we first performed a robust proteomics analysis of mass spectrometry (MS) data derived from a yeast native cell extract and used this information to identify protein communities and inter-protein interactions. Cryo-EM analysis of the cell extract provided a reconstruction of a biomolecule at medium resolution (∼8 Å (FSC = 0.143)). Utilizing MS-derived proteomics data and systematic fitting of AlphaFold-predicted atomic models, this density was assigned to the 2.6 MDa complex of yeast fatty acid synthase. Our proposed workflow identifies protein complexes in native cell extracts from Saccharomyces cerevisiae by combining proteomics, cryo-EM, and AI-guided protein structure prediction.

Structural biology of an organ

Su et al.1 use a build-and-retrieve approach to both identify and determine structures of ten macromolecular machines in the human liver. The authors' method will launch researchers forward in understanding the structural biology of the cell (or organ).

The role of human lipoproteins for hepatitis C virus persistence

Hepatitis C virus (HCV) is a hepatotropic virus that establishes a chronic infection in most individuals. Effective treatments are available; however, many patients are not aware of their infection. Consequently, they do not receive treatment and HCV transmission remains high, particularly among groups at high risk of exposure such as people who inject intravenous drugs. A prophylactic vaccine may reduce HCV transmission, but is currently not available. HCV has evolved immune evasion strategies, which facilitate persistence and complicate development of a protective vaccine. The peculiar association of HCV particles with human lipoproteins is thought to facilitate evasion from humoral immune response and viral homing to liver cells. A better understanding of these aspects provides the basis for development of protective vaccination strategies. Here, we review key information about the composition of HCV particles, the mechanisms mediating lipoprotein incorporation, and the functional consequences of this interaction.

The protein organization of a red blood cell

Red blood cells (RBCs) (erythrocytes) are the simplest primary human cells, lacking nuclei and major organelles and instead employing about a thousand proteins to dynamically control cellular function and morphology in response to physiological cues. In this study, we define a canonical RBC proteome and interactome using quantitative mass spectrometry and machine learning. Our data reveal an RBC interactome dominated by protein homeostasis, redox biology, cytoskeletal dynamics, and carbon metabolism. We validate protein complexes through electron microscopy and chemical crosslinking and, with these data, build 3D structural models of the ankyrin/Band 3/Band 4.2 complex that bridges the spectrin cytoskeleton to the RBC membrane. The model suggests spring-like compression of ankyrin may contribute to the characteristic RBC cell shape and flexibility. Taken together, our study provides an in-depth view of the global protein organization of human RBCs and serves as a comprehensive resource for future research.

Characterizing Endogenous Protein Complexes with Biological Mass Spectrometry

Biological mass spectrometry (MS) encompasses a range of methods for characterizing proteins and other biomolecules. MS is uniquely powerful for the structural analysis of endogenous protein complexes, which are often heterogeneous, poorly abundant, and refractive to characterization by other methods. Here, we focus on how biological MS can contribute to the study of endogenous protein complexes, which we define as complexes expressed in the physiological host and purified intact, as opposed to reconstituted complexes assembled from heterologously expressed components. Biological MS can yield information on complex stoichiometry, heterogeneity, topology, stability, activity, modes of regulation, and even structural dynamics. We begin with a review of methods for isolating endogenous complexes. We then describe the various biological MS approaches, focusing on the type of information that each method yields. We end with future directions and challenges for these MS-based methods.

Label-free visual proteomics: Coupling MS- and EM-based approaches in structural biology

Combining diverse experimental structural and interactomic methods allows for the construction of comprehensible molecular encyclopedias of biological systems. Typically, this involves merging several independent approaches that provide complementary structural and functional information from multiple perspectives and at different resolution ranges. A particularly potent combination lies in coupling structural information from cryoelectron microscopy or tomography (cryo-EM or cryo-ET) with interactomic and structural information from mass spectrometry (MS)-based structural proteomics. Cryo-EM/ET allows for sub-nanometer visualization of biological specimens in purified and near-native states, while MS provides bioanalytical information for proteins and protein complexes without introducing additional labels. Here we highlight recent achievements in protein structure and interactome determination using cryo-EM/ET that benefit from additional MS analysis. We also give our perspective on how combining cryo-EM/ET and MS will continue bridging gaps between molecular and cellular studies by capturing and describing 3D snapshots of proteomes and interactomes.

Structural features of nucleosomes in interphase and metaphase chromosomes

Structural heterogeneity of nucleosomes in functional chromosomes is unknown. Here, we devise the template-, reference- and selection-free (TRSF) cryo-EM pipeline to simultaneously reconstruct cryo-EM structures of protein complexes from interphase or metaphase chromosomes. The reconstructed interphase and metaphase nucleosome structures are on average indistinguishable from canonical nucleosome structures, despite DNA sequence heterogeneity, cell-cycle-specific posttranslational modifications, and interacting proteins. Nucleosome structures determined by a decoy-classifying method and structure variability analyses reveal the nucleosome structural variations in linker DNA, histone tails, and nucleosome core particle configurations, suggesting that the opening of linker DNA, which is correlated with H2A C-terminal tail positioning, is suppressed in chromosomes. High-resolution (3.4-3.5 Å) nucleosome structures indicate DNA-sequence-independent stabilization of superhelical locations ±0-1 and ±3.5-4.5. The linker histone H1.8 preferentially binds to metaphase chromatin, from which chromatosome cryo-EM structures with H1.8 at the on-dyad position are reconstituted. This study presents the structural characteristics of nucleosomes in chromosomes.

Determination of the Near-Atomic Structure of Non-Purified Oligomeric E. coli Proteins

The roughly purified extract of E. coli proteins has been studied by cryoelectron microscopy, the class-sums containing 2D projections of two proteins (β-galactosidase and 2-oxoglutarate dehydrogenase complex catalytic domain (ODC-CD)), identified in an extract by tandem mass spectrometry, have been distinguished. The structures of these proteins have been solved at near-atomic resolution. De novo simulation of the ODC-CD structure yielded an atomic model that revealed differences in the positions of some amino acid residues of the active center, in comparison with the known crystal structures.

Integration of Mass Spectrometry Data for Structural Biology

Mass spectrometry (MS) is increasingly being used to probe the structure and dynamics of proteins and the complexes they form with other macromolecules. There are now several specialized MS methods, each with unique sample preparation, data acquisition, and data processing protocols. Collectively, these methods are referred to as structural MS and include cross-linking, hydrogen-deuterium exchange, hydroxyl radical footprinting, native, ion mobility, and top-down MS. Each of these provides a unique type of structural information, ranging from composition and stoichiometry through to residue level proximity and solvent accessibility. Structural MS has proved particularly beneficial in studying protein classes for which analysis by classic structural biology techniques proves challenging such as glycosylated or intrinsically disordered proteins. To capture the structural details for a particular system, especially larger multiprotein complexes, more than one structural MS method with other structural and biophysical techniques is often required. Key to integrating these diverse data are computational strategies and software solutions to facilitate this process. We provide a background to the structural MS methods and briefly summarize other structural methods and how these are combined with MS. We then describe current state of the art approaches for the integration of structural MS data for structural biology. We quantify how often these methods are used together and provide examples where such combinations have been fruitful. To illustrate the power of integrative approaches, we discuss progress in solving the structures of the proteasome and the nuclear pore complex. We also discuss how information from structural MS, particularly pertaining to protein dynamics, is not currently utilized in integrative workflows and how such information can provide a more accurate picture of the systems studied. We conclude by discussing new developments in the MS and computational fields that will further enable in-cell structural studies.

Microchip-based structure determination of low-molecular weight proteins using cryo-electron microscopy

Interest in cryo-Electron Microscopy (EM) imaging has skyrocketed in recent years due to its pristine views of macromolecules and materials. As advances in instrumentation and computing algorithms spurred this progress, there is renewed focus to address specimen-related challenges. Here we contribute a microchip-based toolkit to perform complementary structural and biochemical analysis on low-molecular weight proteins. As a model system, we used the SARS-CoV-2 nucleocapsid (N) protein (48 kDa) due to its stability and important role in therapeutic development. Cryo-EM structures of the N protein monomer revealed a flexible N-terminal "top hat" motif and a helical-rich C-terminal domain. To complement our structural findings, we engineered microchip-based immunoprecipitation assays that led to the discovery of the first antibody binding site on the N protein. The data also facilitated molecular modeling of a variety of pandemic and common cold-related coronavirus proteins. Such insights may guide future pandemic-preparedness protocols through immuno-engineering strategies to mitigate viral outbreaks.

Detecting Protein Communities in Native Cell Extracts by Machine Learning: A Structural Biologist's Perspective

Native cell extracts hold great promise for understanding the molecular structure of ordered biological systems at high resolution. This is because higher-order biomolecular interactions, dubbed as protein communities, may be retained in their (near-)native state, in contrast to extensively purifying or artificially overexpressing the proteins of interest. The distinct machine-learning approaches are applied to discover protein-protein interactions within cell extracts, reconstruct dedicated biological networks, and report on protein community members from various organisms. Their validation is also important, e.g., by the cross-linking mass spectrometry or cell biology methods. In addition, the cell extracts are amenable to structural analysis by cryo-electron microscopy (cryo-EM), but due to their inherent complexity, sorting structural signatures of protein communities derived by cryo-EM comprises a formidable task. The application of image-processing workflows inspired by machine-learning techniques would provide improvements in distinguishing structural signatures, correlating proteomic and network data to structural signatures and subsequently reconstructed cryo-EM maps, and, ultimately, characterizing unidentified protein communities at high resolution. In this review article, we summarize recent literature in detecting protein communities from native cell extracts and identify the remaining challenges and opportunities. We argue that the progress in, and the integration of, machine learning, cryo-EM, and complementary structural proteomics approaches would provide the basis for a multi-scale molecular description of protein communities within native cell extracts.

Cryo-EM as a tool to study bacterial efflux systems and the membrane proteome

Antibiotic resistance is an emerging threat to global health. Current treatment regimens for these types of bacterial infections are becoming increasingly inadequate. Thus, new innovative technologies are needed to help identify and characterize novel drugs and drug targets which are critical in order to combat multidrug-resistant bacterial strains. Bacterial efflux systems have emerged as an attractive target for drug design, as blocking their export function significantly increases the potency of administered antibiotics. However, in order to develop potent and tolerable efflux pump inhibitors with high efficacy, detailed structural information is required for both the apo- and substrate-bound forms of these membrane proteins. The emergence of cryo-electron microscopy (cryo-EM) has greatly advanced the field of membrane protein structural biology. It has significantly enhanced the ability to solve large multi-protein complexes as well as extract meaningful data from a heterogeneous sample, such as identification of several assembly states of the bacterial ribosome, from a single data set. This technique can be expanded to solve the structures of substrate-bound efflux pumps and entire efflux systems from previously unusable membrane protein sample preparations. Subsequently, cryo-EM combined with other biophysical techniques has the potential to markedly advance the field of membrane protein structural biology. The ability to discern complete transport machineries, enzymatic signal transduction pathways, and other membrane-associated complexes will help us fully understand the complexities of the membrane proteome.

Integrative structure of a 10-megadalton eukaryotic pyruvate dehydrogenase complex from native cell extracts

The pyruvate dehydrogenase complex (PDHc) is a giant enzymatic assembly involved in pyruvate oxidation. PDHc components have been characterized in isolation, but the complex's quaternary structure has remained elusive due to sheer size, heterogeneity, and plasticity. Here, we identify fully assembled Chaetomium thermophilum α-keto acid dehydrogenase complexes in native cell extracts and characterize their domain arrangements utilizing mass spectrometry, activity assays, crosslinking, electron microscopy (EM), and computational modeling. We report the cryo-EM structure of the PDHc core and observe unique features of the previously unknown native state. The asymmetric reconstruction of the 10-MDa PDHc resolves spatial proximity of its components, agrees with stoichiometric data (60 E2p:12 E3BP:∼20 E1p: ≤ 12 E3), and proposes a minimum reaction path among component enzymes. The PDHc shows the presence of a dynamic pyruvate oxidation compartment, organized by core and peripheral protein species. Our data provide a framework for further understanding PDHc and α-keto acid dehydrogenase complex structure and function.

Cryo-electron microscopy of cholinesterases, present and future

Acetylcholinesterase (AChE) and butyrylcholinesterase (BChE) exist in a variety of oligomeric forms, each with defined cellular and subcellular distributions. Although crystal structures of AChE and BChE have been available for many years, structures of the physiologically relevant ChE tetramer were only recently solved by cryo‐electron microscopy (cryo‐EM) single‐particle analysis. Here, we briefly review how these structures contribute to our understanding of cholinesterase oligomerization, highlighting the advantages of using cryo‐EM to resolve structures of protein assemblies that cannot be expressed recombinantly. We argue that the next frontier in cholinesterase structural biology is to image membrane‐anchored ChE oligomers directly in their native environment—the cell.

EMPAS: Electron Microscopy Screening for Endogenous Protein Architectures

In cells, proteins form macromolecular complexes to execute their own unique roles in biological processes. Conventional structural biology methods adopt a bottom-up approach starting from defined sets of proteins to investigate the structures and interactions of protein complexes. However, this approach does not reflect the diverse and complex landscape of endogenous molecular architectures. Here, we introduce a top-down approach called Electron Microscopy screening for endogenous Protein ArchitectureS (EMPAS) to investigate the diverse and complex landscape of endogenous macromolecular architectures in an unbiased manner. By applying EMPAS, we discovered a spiral architecture and identified it as AdhE. Furthermore, we performed screening to examine endogenous molecular architectures of human embryonic stem cells (hESCs), mouse brains, cyanobacteria and plant leaves, revealing their diverse repertoires of molecular architectures. This study suggests that EMPAS may serve as a tool to investigate the molecular architectures of endogenous macromolecular proteins.

Shotgun EM of mycobacterial protein complexes during stationary phase stress

There is little structural information about the protein complexes conferring resistance in Mycobacterium tuberculosis (Mtb) to anti-microbial oxygen and nitrogen radicals in the phagolysosome. Here, we expose the model Mycobacterium, Mycobacterium smegmatis, to simulated oxidative-stress conditions and apply a shotgun EM method for the structural detection of the resulting protein assemblies. We identified: glutamine synthetase I, essential for Mtb virulence; bacterioferritin A, critical for Mtb iron regulation; aspartyl aminopeptidase M18, a protease; and encapsulin, which produces a cage-like structure to enclose cargo proteins. After further investigation, we found that encapsulin carries dye-decolourising peroxidase, a protein antioxidant, as its primary cargo under the conditions tested.

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.

Integrative biology of native cell extracts: a new era for structural characterization of life processes

Advances in electron microscopy have provided unprecedented access to the structural characterization of large, flexible and heterogeneous complexes. Until recently, cryo-electron microscopy (cryo-EM) has been applied to understand molecular organization in either highly purified, isolated biomolecules or in situ. An emerging field is developing, bridging the gap between the two approaches, and focuses on studying molecular organization in native cell extracts. This field has demonstrated its potential by resolving the structure of fungal fatty acid synthase (FAS) at 4.7 Å [Fourier shell correlation (FSC) = 0.143]; FAS was not only less than 50% enriched, but also retained higher-order binders, previously unknown. Although controversial in the sense that the lysis step might introduce artifacts, cell extracts preserve aspects of cellular function. In addition, cell extracts are accessible, besides cryo-EM, to modern proteomic methods, chemical cross-linking, network biology and biophysical modeling. We expect that automation in imaging cell extracts, along with the integration of molecular/cell biology approaches, will provide remarkable achievements in the study of closer-to-life biomolecular states of pronounced biotechnological and medical importance. Such steps will, eventually, bring us a step closer to the biophysical description of cellular processes in an integrative, holistic approach.

Separating distinct structures of multiple macromolecular assemblies from cryo-EM projections

Single particle analysis for structure determination in cryo-electron microscopy is traditionally applied to samples purified to near homogeneity as current reconstruction algorithms are not designed to handle heterogeneous mixtures of structures from many distinct macromolecular complexes. We extend on long established methods and demonstrate that relating two-dimensional projection images by their common lines in a graphical framework is sufficient for partitioning distinct protein and multiprotein complexes within the same data set. The feasibility of this approach is first demonstrated on a large set of synthetic reprojections from 35 unique macromolecular structures spanning a mass range of hundreds to thousands of kilodaltons. We then apply our algorithm on cryo-EM data collected from a mixture of five protein complexes and use existing methods to solve multiple three-dimensional structures ab initio. Incorporating methods to sort single particle cryo-EM data from extremely heterogeneous mixtures will alleviate the need for stringent purification and pave the way toward investigation of samples containing many unique structures.

Challenges and opportunities in cryo-EM single-particle analysis

Cryogenic electron microscopy (cryo-EM) enables structure determination of macromolecular objects and their assemblies. Although the techniques have been developing for nearly four decades, they have gained widespread attention in recent years due to technical advances on numerous fronts, enabling traditional microscopists to break into the world of molecular structural biology. Many samples can now be routinely analyzed at near-atomic resolution using standard imaging and image analysis techniques. However, numerous challenges to conventional workflows remain, and continued technical advances open entirely novel opportunities for discovery and exploration. Here, I will review some of the main methods surrounding cryo-EM with an emphasis specifically on single-particle analysis, and I will highlight challenges, open questions, and opportunities for methodology development.

Future prospects of structural studies to advance our understanding of phage biology

Bacteriophages, being the most abundant biological entities on the Earth, play a major role in regulating populations of bacteria and thus influence the evolution and stability of ecosystems. Phage infections of pathogenic bacteria can both exacerbate and alleviate the severity of the disease. The structural characterisations of phage particles and individual proteins have enabled the understanding of many aspects of phage biology. Due to methodological limitations, most of the structures were determined from purified samples in vitro. However, studies performed outside the cellular context cannot capture the complex and dynamic interactions of the macromolecules that are required for their biological functions. Current developments in structural biology, in particular cryo-electron microscopy, allow in situ high-resolution studies of phage-infected cells. Here we discuss open questions in phage biology that could be addressed by structural biology techniques and their potential to enable the use of tailed phages in industrial applications and human therapy.

Electron microscopy snapshots of single particles from single cells

Cryo-electron microscopy (cryo-EM) has become an indispensable tool for structural studies of biological macromolecules. Two additional predominant methods are available for studying the architectures of multiprotein complexes: 1) single-particle analysis of purified samples and 2) tomography of whole cells or cell sections. The former can produce high-resolution structures but is limited to highly purified samples, whereas the latter can capture proteins in their native state but has a low signal-to-noise ratio and yields lower-resolution structures. Here, we present a simple, adaptable method combining microfluidic single-cell extraction with single-particle analysis by EM to characterize protein complexes from individual Caenorhabditis elegans embryos. Using this approach, we uncover 3D structures of ribosomes directly from single embryo extracts. Moreover, we investigated structural dynamics during development by counting the number of ribosomes per polysome in early and late embryos. This approach has significant potential applications for counting protein complexes and studying protein architectures from single cells in developmental, evolutionary, and disease contexts.