The Core Complex of Yeast COMPASS and Human Mixed-Lineage Leukemia (MLL), Structure, Function, and Recognition of the Nucleosome.

Yeast COMPASS (complex of proteins associated with Set1) and human MLL (mixed-lineage leukemia) complexes are histone H3 lysine 4 methyltransferases with critical roles in gene regulation and embryonic development. Both complexes share a conserved C-terminal SET domain, responsible for catalyzing histone H3 K4 methylation on nucleosomes. Notably, their catalytic activity toward nucleosomes is enhanced and optimized with assembly of auxiliary subunits. In this review, we aim to illustrate the recent X-ray and cryo-EM structures of yeast COMPASS and human MLL1 core complexes bound to either unmodified nucleosome core particle (NCP) or H2B mono-ubiquitinated NCP (H2Bub.NCP). We further delineate how each auxiliary component of the complex contributes to the NCP and ubiquitin recognition to maximize the methyltransferase activity.

Human Transient Receptor Potential Ankyrin 1 Channel: Structure, Function, and Physiology.

The transient receptor potential ion channel TRPA1 is a Ca2+-permeable nonselective cation channel widely expressed in sensory neurons, but also in many nonneuronal tissues typically possessing barrier functions, such as the skin, joint synoviocytes, cornea, and the respiratory and intestinal tracts. Here, the primary role of TRPA1 is to detect potential danger stimuli that may threaten the tissue homeostasis and the health of the organism. The ability to directly recognize signals of different modalities, including chemical irritants, extreme temperatures, or osmotic changes resides in the characteristic properties of the ion channel protein complex. Recent advances in cryo-electron microscopy have provided an important framework for understanding the molecular basis of TRPA1 function and have suggested novel directions in the search for its pharmacological regulation. This chapter summarizes the current knowledge of human TRPA1 from a structural and functional perspective and discusses the complex allosteric mechanisms of activation and modulation that play important roles under physiological or pathophysiological conditions. In this context, major challenges for future research on TRPA1 are outlined.

Structure and Dynamics of Type 4a Pili and Type 2 Secretion System Endopili.

Within the highly diverse type four filament (TFF or T4F) superfamily, the machineries of type IVa pili (T4aP) and the type 2 secretion system (T2SS) in diderm bacteria exhibit a substantial sequence similarity despite divergent functions and distinct appearances: T4aP can extend micrometers beyond the outer membrane, whereas the endopili in the T2SS are restricted to the periplasm. The determination of the structure of individual components and entire filaments is crucial to understand how their structure enables them to serve different functions. However, the dynamics of these filaments poses a challenge for their high-resolution structure determination. This review presents different approaches that have been used to study the structure and dynamics of T4aP and T2SS endopili by means of integrative structural biology, cryo-electron microscopy (cryo-EM), and molecular dynamics simulations. Their conserved features and differences are presented. The non-helical stretch in the long-conserved N-terminal helix which is characteristic of all members of the TFF and the impact of calcium on structure, function, and dynamics of these filaments are discussed in detail.

Comprehensive encoding of conformational and compositional protein structural ensembles through the mmCIF data structure.

In the folded state, biomolecules exchange between multiple conformational states crucial for their function. However, most structural models derived from experiments and computational predictions only encode a single state. To represent biomolecules accurately, we must move towards modeling and predicting structural ensembles. Information about structural ensembles exists within experimental data from X-ray crystallography and cryo-electron microscopy. Although new tools are available to detect conformational and compositional heterogeneity within these ensembles, the legacy PDB data structure does not robustly encapsulate this complexity. We propose modifications to the macromolecular crystallographic information file (mmCIF) to improve the representation and interrelation of conformational and compositional heterogeneity. These modifications will enable the capture of macromolecular ensembles in a human and machine-interpretable way, potentially catalyzing breakthroughs for ensemble-function predictions, analogous to the achievements of AlphaFold with single-structure prediction.

Thickness determination of hydroperoxidized lipid bilayers from medium-resolution cryo-TEM images.

As the primary products of lipid oxidation, lipid hydroperoxides constitute an important class of lipids generated by aerobic metabolism. However, despite several years of effort, the structure of the hydroperoxidized bilayer has not yet been observed under electron microscopy. Here we use a 200 kV Cryo-TEM to image small unilamellar vesicles (SUVs) made (i) of pure POPC or SOPC, (ii) of their pure hydroperoxidized form, and (iii) of their equimolar mixtures. We show that the challenges posed by the determination of the thickness of the hydroperoxidized bilayers under these observation conditions can be addressed by an image analysis method that we developed and describe here.

De novo atomic protein structure modeling for cryoEM density maps using 3D transformer and HMM.

Accurately building 3D atomic structures from cryo-EM density maps is a crucial step in cryo-EM-based protein structure determination. Converting density maps into 3D atomic structures for proteins lacking accurate homologous or predicted structures as templates remains a significant challenge. Here, we introduce Cryo2Struct, a fully automated de novo cryo-EM structure modeling method. Cryo2Struct utilizes a 3D transformer to identify atoms and amino acid types in cryo-EM density maps, followed by an innovative Hidden Markov Model (HMM) to connect predicted atoms and build protein backbone structures. Cryo2Struct produces substantially more accurate and complete protein structural models than the widely used ab initio method Phenix. Additionally, its performance in building atomic structural models is robust against changes in the resolution of density maps and the size of protein structures.

Preparation of Bunyavirus-Infected Cells for Electron Cryo-Tomography.

Cellular electron cryo-tomography (cryoET) produces high-resolution three-dimensional images of subcellular structures in a near-native frozen-hydrated state. These three-dimensional images are obtained by recording a series of two-dimensional tilt images on a transmission electron cryo-microscope that are subsequently back-projected to form a tomogram. Key to a successful experiment is however a high-quality sample. This chapter outlines a basic workflow for the preparation of cellular cryoET samples. It covers the preparation of infected cells on electron cryo-microscopy grids and the vitrification by plunge-freezing and clipping of grids into AutoGrid rims. It also provides a general overview of the workflow for thinning the vitrified cells by focused ion beam (FIB) milling. Although this book is dedicated to Rift Valley fever virus research, the present protocol may also be applied to any other research subject where high-resolution structural insight into intracellular processes is desired.

Structural characterization of lateral phase separation in polymer-lipid hybrid membranes.

Hierarchic self-assembly is the main mechanism used to create diverse structures using soft materials. This is a case for both synthetic materials and biomolecular systems, as exemplified by the non-covalent organization of lipids into membranes. In nature, lipids often assemble into single bilayers, but other nanostructures are encountered, such as bilayer stacks and tubular and vesicular aggregates. Synthetic block copolymers can be engineered to recapitulate many of the structures, forms, and functions of lipid systems. When block copolymers are amphiphilic, they can be inserted or co-assembled into hybrid membranes that exhibit synergistic structural, permeability, and mechanical properties. One example is the emergence of lateral phase separation akin to the raft formation in biomembranes. When higher-order structures, such as hybrid membranes, are formed, this lateral phase separation can be correlated across membranes in the stack. This chapter outlines a set of important methods, such as X-ray Scattering, Atomic Force Microscopy, and Cryo-Electron Microscopy, that are relevant to characterizing and evaluating lateral and correlated phase separation in hybrid membranes at the nano and mesoscales. Understanding the phase behavior of polymer-lipid hybrid materials could lead to innovative advancements in biomimetic membrane separation systems.

Phase separation in model lipid membranes investigated with cryogenic electron microscopy.

We describe a method for investigating lateral membrane heterogeneity using cryogenic electron microscopy (cryo-EM) images of liposomes. The method takes advantage of differences in the thickness and molecular density of ordered and disordered phases that are resolvable in phase contrast cryo-EM. Compared to biophysical techniques like FRET or neutron scattering that yield ensemble-averaged information, cryo-EM provides direct visualization of individual vesicles and can therefore reveal variability that would otherwise be obscured by averaging. Moreover, because the contrast mechanism involves inherent properties of the lipid phases themselves, no extrinsic probes are required. We explain and discuss various complementary analyses of spatially resolved thickness and intensity measurements that enable an assessment of the membrane's phase state. The method opens a window to nanodomain structure in synthetic and biological membranes that should lead to an improved understanding of lipid raft phenomena.

Cryogenic electron microscopy reveals morphologically distinct subtypes of extracellular vesicles among porcine ejaculate fractions.

Seminal plasma (SP) is rich in extracellular vesicles (EVs), which are still poorly studied, especially in livestock species. To better understand their functional role in both spermatozoa and endometrial epithelial cells, proper characterization of EVs is an essential step. The objective was to phenotypically characterize porcine seminal EVs (sEVs) using cryogenic electron microscopy (cryo-EM), which allows visualization of EVs in their native state. Porcine ejaculates are released in fractions, each containing SP from different source. This allows characterization sEVs released from various male reproductive tissues. Two experiments were performed, the first with SP from the entire ejaculate (n:6) and the second with SP from three ejaculate fractions (n:15): the first 10 mL of the sperm-rich ejaculate fraction (SRF-P1) with SP mainly from the epididymis, the remainder of the SRF (SRF-P2) with SP mainly from the prostate, and the post-SRF with SP mainly from the seminal vesicles. The sEVs were isolated by size exclusion chromatography and 1840 cryo-EM sEV images were acquired using a Jeol-JEM-2200FS/CR-EM. The size, electron density, complexity, and peripheral corona layer were measured in each sEV using the ImageJ software. The first experiment showed that sEVs were structurally and morphologically heterogeneous, although most (83.1%) were small (less than 200 nm), rounded, and poorly electrodense, and some have a peripheral coronal layer. There were also larger sEVs (16.9%) that were irregularly shaped, more electrodense, and few with a peripheral coronal layer. The second experiment showed that small sEVs were more common in SRF-P1 and SRF-P2, indicating that they originated mainly from the epididymis and prostate. Large sEVs were more abundant in post-SRF, indicating that they originated mainly from seminal vesicles. Porcine sEVs are structurally and morphologically heterogeneous. This would be explained by the diversity of reproductive organs of origin.

GOLEM: Automated and Robust Cryo-EM-Guided Ligand Docking with Explicit Water Molecules.

A detailed understanding of ligand-protein interaction is essential for developing rational drug-design strategies. In recent years, technological advances in cryo-electron microscopy (cryo-EM) brought a new era to the structural determination of biological macromolecules and assemblies at high resolution, marking cryo-EM as a promising tool for studying ligand-protein interactions. However, even in high-resolution cryo-EM results, the densities for the bound small-molecule ligands are often of lower quality due to their relatively dynamic and flexible nature, frustrating their accurate coordinate assignment. To address the challenge of ligand modeling in cryo-EM maps, here we report the development of GOLEM (Genetic Optimization of Ligands in Experimental Maps), an automated and robust ligand docking method that predicts a ligand's pose and conformation in cryo-EM maps. GOLEM employs a Lamarckian genetic algorithm to perform a hybrid global/local search for exploring the ligand's conformational, orientational, and positional space. As an important feature, GOLEM explicitly considers water molecules and places them at optimal positions and orientations. GOLEM takes into account both molecular energetics and the correlation with the cryo-EM maps in its scoring function to optimally place the ligand. We have validated GOLEM against multiple cryo-EM structures with a wide range of map resolutions and ligand types, returning ligand poses in excellent agreement with the densities. As a VMD plugin, GOLEM is free of charge and accessible to the community. With these features, GOLEM will provide a valuable tool for ligand modeling in cryo-EM efforts toward drug discovery.

Accurate Prediction of Protein Structural Flexibility by Deep Learning Integrating Intricate Atomic Structures and Cryo-EM Density Information.

The dynamics of proteins are crucial for understanding their mechanisms. However, computationally predicting protein dynamic information has proven challenging. Here, we propose a neural network model, RMSF-net, which outperforms previous methods and produces the best results in a large-scale protein dynamics dataset; this model can accurately infer the dynamic information of a protein in only a few seconds. By learning effectively from experimental protein structure data and cryo-electron microscopy (cryo-EM) data integration, our approach is able to accurately identify the interactive bidirectional constraints and supervision between cryo-EM maps and PDB models in maximizing the dynamic prediction efficacy. Rigorous 5-fold cross-validation on the dataset demonstrates that RMSF-net achieves test correlation coefficients of 0.746 ± 0.127 at the voxel level and 0.765 ± 0.109 at the residue level, showcasing its ability to deliver dynamic predictions closely approximating molecular dynamics simulations. Additionally, it offers real-time dynamic inference with minimal storage overhead on the order of megabytes. RMSF-net is a freely accessible tool and is anticipated to play an essential role in the study of protein dynamics.

The myth of high-resolution liquid phase biological electron microscopy.

Cryo-electron microscopy (cryo-EM) has transformed structural biology over the past 12 years, with it now being routine rather than exceptional to reach a near-atomic level of resolution for proteins and macromolecular complexes. Samples are immobilized by vitrification and this sample can be maintained at liquid nitrogen temperatures in the vacuum of the electron microscope with negligible sublimation. Due to the low electron doses needed to avoid radiation damage, averaging over tens of thousands to hundreds of thousands of particle images is used to achieve a high signal-to-noise ratio. An alternative approach has been proposed where samples are at room temperature in the liquid state, maintained in the vacuum of the electron microscope by thin film enclosures that are relatively transparent to electrons while preventing evaporation of the liquid. A paper has argued that using this liquid-phase approach, higher resolution (3.2 Å) can be achieved than using cryo-EM (3.4 Å) when imaging and reconstructing adeno-associated virus particles. I show here that these assertions are untrue, and that basic principles in mathematics and physics would need to be violated to achieve the stated resolution in the liquid state. Thus, high resolution liquid phase EM of macromolecules remains science fiction.

Methods for the preparation and analysis of a bifunctional class II diterpene synthase, copalyl diphosphate synthase from Penicillium fellutanum.

Terpenes comprise the largest class of natural products and are used in applications spanning the areas of medicine, cosmetics, fuels, flavorings, and more. Copalyl diphosphate synthase from the Penicillium genus is the first bifunctional terpene synthase identified to have both prenyltransferase and class II cyclase activities within the same polypeptide chain. Prior studies of bifunctional terpene synthases reveal that these systems achieve greater catalytic efficiency by channeling geranylgeranyl diphosphate between the prenyltransferase and cyclase domains. A molecular-level understanding of substrate transit phenomena in these systems is highly desirable, but a long disordered polypeptide segment connecting the prenyltranferase and cyclase domains thwarts the crystallization of full-length enzymes. Accordingly, these systems are excellent candidates for structural analysis using cryo-electron microscopy (cryo-EM). Notably, these systems form hexameric or octameric oligomers, so the quaternary structure of the full-length enzyme may influence substrate transit between catalytic domains. Here, we describe methods for the preparation of bifunctional hexameric copalyl diphosphate synthase from Penicillium fellutanum (PfCPS). We also outline approaches for the preparation of cryo-EM grids, data collection, and data processing to yield two-dimensional and three-dimensional reconstructions.

Validation of electron-microscopy maps using solution small-angle X-ray scattering.

The determination of the atomic resolution structure of biomacromolecules is essential for understanding details of their function. Traditionally, such a structure determination has been performed with crystallographic or nuclear resonance methods, but during the last decade, cryogenic transmission electron microscopy (cryo-TEM) has become an equally important tool. As the blotting and flash-freezing of the samples can induce conformational changes, external validation tools are required to ensure that the vitrified samples are representative of the solution. Although many validation tools have already been developed, most of them rely on fully resolved atomic models, which prevents early screening of the cryo-TEM maps. Here, a novel and automated method for performing such a validation utilizing small-angle X-ray scattering measurements, publicly available through the new software package AUSAXS, is introduced and implemented. The method has been tested on both simulated and experimental data, where it was shown to work remarkably well as a validation tool. The method provides a dummy atomic model derived from the EM map which best represents the solution structure.

CryoSegNet: accurate cryo-EM protein particle picking by integrating the foundational AI image segmentation model and attention-gated U-Net.

Picking protein particles in cryo-electron microscopy (cryo-EM) micrographs is a crucial step in the cryo-EM-based structure determination. However, existing methods trained on a limited amount of cryo-EM data still cannot accurately pick protein particles from noisy cryo-EM images. The general foundational artificial intelligence-based image segmentation model such as Meta's Segment Anything Model (SAM) cannot segment protein particles well because their training data do not include cryo-EM images. Here, we present a novel approach (CryoSegNet) of integrating an attention-gated U-shape network (U-Net) specially designed and trained for cryo-EM particle picking and the SAM. The U-Net is first trained on a large cryo-EM image dataset and then used to generate input from original cryo-EM images for SAM to make particle pickings. CryoSegNet shows both high precision and recall in segmenting protein particles from cryo-EM micrographs, irrespective of protein type, shape and size. On several independent datasets of various protein types, CryoSegNet outperforms two top machine learning particle pickers crYOLO and Topaz as well as SAM itself. The average resolution of density maps reconstructed from the particles picked by CryoSegNet is 3.33 Å, 7% better than 3.58 Å of Topaz and 14% better than 3.87 Å of crYOLO. It is publicly available at https://github.com/jianlin-cheng/CryoSegNet.

Use of phase plate cryo-EM reveals conformation diversity of therapeutic IgG with 50 kDa Fab fragment resolved below 6 Å.

While cryogenic electron microscopy (cryo-EM) is fruitfully used for harvesting high-resolution structures of sizable macromolecules, its application to small or flexible proteins composed of small domains like immunoglobulin (IgG) remain challenging. Here, we applied single particle cryo-EM to Rituximab, a therapeutic IgG mediating anti-tumor toxicity, to explore its solution conformations. We found Rituximab molecules exhibited aggregates in cryo-EM specimens contrary to its solution behavior, and utilized a non-ionic detergent to successfully disperse them as isolated particles amenable to single particle analysis. As the detergent adversely reduced the protein-to-solvent contrast, we employed phase plate contrast to mitigate the impaired protein visibility. Assisted by phase plate imaging, we obtained a canonical three-arm IgG structure with other structures displaying variable arm densities co-existing in solution, affirming high flexibility of arm-connecting linkers. Furthermore, we showed phase plate imaging enables reliable structure determination of Fab to sub-nanometer resolution from ab initio, yielding a characteristic two-lobe structure that could be unambiguously docked with crystal structure. Our findings revealed conformation diversity of IgG and demonstrated phase plate was viable for cryo-EM analysis of small proteins without symmetry. This work helps extend cryo-EM boundaries, providing a valuable imaging and structural analysis framework for macromolecules with similar challenging features.

An immobilized antibody-based affinity grid strategy for on-grid purification of target proteins enables high-resolution cryo-EM.

In cryo-electron microscopy (cryo-EM), sample preparation poses a critical bottleneck, particularly for rare or fragile macromolecular assemblies and those suffering from denaturation and particle orientation distribution issues related to air-water interface. In this study, we develop and characterize an immobilized antibody-based affinity grid (IAAG) strategy based on the high-affinity PA tag/NZ-1 antibody epitope tag system. We employ Pyr-NHS as a linker to immobilize NZ-1 Fab on the graphene oxide or carbon-covered grid surface. Our results demonstrate that the IAAG grid effectively enriches PA-tagged target proteins and overcomes preferred orientation issues. Furthermore, we demonstrate the utility of our IAAG strategy for on-grid purification of low-abundance target complexes from cell lysates, enabling atomic resolution cryo-EM. This approach greatly streamlines the purification process, reduces the need for large quantities of biological samples, and addresses common challenges encountered in cryo-EM sample preparation. Collectively, our IAAG strategy provides an efficient and robust means for combined sample purification and vitrification, feasible for high-resolution cryo-EM. This approach holds potential for broader applicability in both cryo-EM and cryo-electron tomography (cryo-ET).

CLEAPA: a framework for exploring the conformational landscape of cryo-EM using energy-aware pathfinding algorithm.

MOTIVATION: Cryo-electron microscopy (cryo-EM) is a powerful technique for studying macromolecules and holds the potential for identifying kinetically preferred transition sequences between conformational states. Typically, these sequences are explored within two-dimensional energy landscapes. However, due to the complexity of biomolecules, representing conformational changes in two dimensions can be challenging. Recent advancements in reconstruction models have successfully extracted structural heterogeneity from cryo-EM images using higher-dimension latent space. Nonetheless, creating high-dimensional conformational landscapes in the latent space and then searching for preferred paths continues to be a formidable task. RESULTS: This study introduces an innovative framework for identifying preferred trajectories within high-dimensional conformational landscapes. Our method encompasses the search for the minimum energy path in the graph, where edge weights are determined based on the energy estimation at each node using local density. The effectiveness of this approach is demonstrated by identifying accurate transition states in both synthetic and real-world datasets featuring continuous conformational changes. AVAILABILITY AND IMPLEMENTATION: The CLEAPA package is available at https://github.com/tengyulin/energy_aware_pathfinding/.

Automated multiconformer model building for X-ray crystallography and cryo-EM.

In their folded state, biomolecules exchange between multiple conformational states that are crucial for their function. Traditional structural biology methods, such as X-ray crystallography and cryogenic electron microscopy (cryo-EM), produce density maps that are ensemble averages, reflecting molecules in various conformations. Yet, most models derived from these maps explicitly represent only a single conformation, overlooking the complexity of biomolecular structures. To accurately reflect the diversity of biomolecular forms, there is a pressing need to shift toward modeling structural ensembles that mirror the experimental data. However, the challenge of distinguishing signal from noise complicates manual efforts to create these models. In response, we introduce the latest enhancements to qFit, an automated computational strategy designed to incorporate protein conformational heterogeneity into models built into density maps. These algorithmic improvements in qFit are substantiated by superior Rfree and geometry metrics across a wide range of proteins. Importantly, unlike more complex multicopy ensemble models, the multiconformer models produced by qFit can be manually modified in most major model building software (e.g., Coot) and fit can be further improved by refinement using standard pipelines (e.g., Phenix, Refmac, Buster). By reducing the barrier of creating multiconformer models, qFit can foster the development of new hypotheses about the relationship between macromolecular conformational dynamics and function.