cryo-EM Map Research Articles

Ligand Identification in CryoEM and X-ray Maps Using Deep Learning.

Accurately identifying ligands plays a crucial role in the process of structure-guided drug design. Based on density maps from X-ray diffraction or cryogenic-sample electron microscopy (cryoEM), scientists verify whether small-molecule ligands bind to active sites of interest. However, the interpretation of density maps is challenging, and cognitive bias can sometimes mislead investigators into modeling fictitious compounds. Ligand identification can be aided by automatic methods, but existing approaches are available only for X-ray diffraction and are based on iterative fitting or feature-engineered machine learning rather than end-to-end deep learning. Here, we propose to identify ligands using a deep learning approach that treats density maps as 3D point clouds. We show that the proposed model is on par with existing machine learning methods for X-ray crystallography while also being applicable to cryoEM density maps. Our study demonstrates that electron density map fragments can aid the training of models that can later be applied to cryoEM structures but also highlights challenges associated with the standardization of electron microscopy maps and the quality assessment of cryoEM ligands.

Automated detection and de novo structure modeling of nucleic acids from cryo-EM maps.

Cryo-electron microscopy (cryo-EM) is one of the most powerful experimental methods for macromolecular structure determination. However, accurate DNA/RNA structure modeling from cryo-EM maps is still challenging especially for protein-DNA/RNA or multi-chain DNA/RNA complexes. Here we propose a deep learning-based method for accurate de novo structure determination of DNA/RNA from cryo-EM maps at <5 Å resolutions, which is referred to as EM2NA. EM2NA is extensively evaluated on a diverse test set of 50 experimental maps at 2.0-5.0 Å resolutions, and compared with state-of-the-art methods including CryoREAD, ModelAngelo, and phenix.map_to_model. On average, EM2NA achieves a residue coverage of 83.15%, C4' RMSD of 1.06 Å, and sequence recall of 46.86%, which outperforms the existing methods. Moreover, EM2NA is applied to build the DNA/RNA structures with 10 to 5347 nt from an EMDB-wide data set of 263 unmodeled raw maps, demonstrating its ability in the blind model building of DNA/RNA from cryo-EM maps. EM2NA is fast and can normally build a DNA/RNA structure of <500 nt within 10 minutes.

DiffModeler: large macromolecular structure modeling for cryo-EM maps using a diffusion model.

Cryogenic electron microscopy (cryo-EM) has now been widely used for determining multichain protein complexes. However, modeling a large complex structure, such as those with more than ten chains, is challenging, particularly when the map resolution decreases. Here we present DiffModeler, a fully automated method for modeling large protein complex structures. DiffModeler employs a diffusion model for backbone tracing and integrates AlphaFold2-predicted single-chain structures for structure fitting. DiffModeler showed an average template modeling score of 0.88 and 0.91 for two datasets of cryo-EM maps of 0-5 Å resolution and 0.92 for intermediate resolution maps (5-10 Å), substantially outperforming existing methodologies. Further benchmarking at low resolutions (10-20 Å) confirms its versatility, demonstrating plausible performance.

Cryo-electron microscopy structure of the di-domain core of Mycobacterium tuberculosis polyketide synthase 13, essential for mycobacterial mycolic acid synthesis.

Mycobacteria are known for their complex cell wall, which comprises layers of peptidoglycan, polysaccharides and unusual fatty acids known as mycolic acids that form their unique outer membrane. Polyketide synthase 13 (Pks13) of Mycobacterium tuberculosis, the bacterial organism causing tuberculosis, catalyses the last step of mycolic acid synthesis prior to export to and assembly in the cell wall. Due to its essentiality, Pks13 is a target for several novel anti-tubercular inhibitors, but its 3D structure and catalytic reaction mechanism remain to be fully elucidated. Here, we report the molecular structure of the catalytic core domains of M. tuberculosis Pks13 (Mt-Pks13), determined by transmission cryo-electron microscopy (cryoEM) to a resolution of 3.4 Å. We observed a homodimeric assembly comprising the ketoacyl synthase (KS) domain at the centre, mediating dimerization, and the acyltransferase (AT) domains protruding in opposite directions from the central KS domain dimer. In addition to the KS-AT di-domains, the cryoEM map includes features not covered by the di-domain structural model that we predicted to contain a dimeric domain similar to dehydratases, yet likely lacking catalytic function. Analytical ultracentrifugation data indicate a pH-dependent equilibrium between monomeric and dimeric assembly states, while comparison with the previously determined structures of M. smegmatis Pks13 indicates architectural flexibility. Combining the experimentally determined structure with modelling in AlphaFold2 suggests a structural scaffold with a relatively stable dimeric core, which combines with considerable conformational flexibility to facilitate the successive steps of the Claisen-type condensation reaction catalysed by Pks13.

Protein complex structure modeling by cross-modal alignment between cryo-EM maps and protein sequences

Cryo-electron microscopy (cryo-EM) technique is widely used for protein structure determination. Current automatic cryo-EM protein complex modeling methods mostly rely on prior chain separation. However, chain separation without sequence guidance often suffers from errors caused by cross-chain interaction or noise densities, which would accumulate and mislead the subsequent steps. Here, we present EModelX, a fully automated cryo-EM protein complex structure modeling method, which achieves sequence-guiding modeling through cross-modal alignments between cryo-EM maps and protein sequences. EModelX first employs multi-task deep learning to predict Cα atoms, backbone atoms, and amino acid types from cryo-EM maps, which is subsequently used to sample Cα traces with amino acid profiles. The profiles are then aligned with protein sequences to obtain initial structural models, which yielded an average RMSD of 1.17 Å in our test set, approaching atomic-level precision in recovering PDB-deposited structures. After filling unmodeled gaps through sequence-guiding Cα threading, the final models achieved an average TM-score of 0.808, outperforming the state-of-the-art method. The further combination with AlphaFold can improve the average TM-score to 0.911. Analyzes conducted by comparing some EModelX-built models and PDB structures highlight its potential to improve PDB structures. EModelX is accessible at https://bio-web1.nscc-gz.cn/app/EModelX.

High-Resolution Cryo-EM Structure Determination of α-synuclein - A Prototypical Amyloid Fibril.

The physiological role of α-synuclein (α-syn), an intrinsically disordered presynaptic neuronal protein, is believed to impact the release of neurotransmitters through interactions with the SNARE complex. However, under certain cellular conditions that are not well understood, α-syn will self-assemble into β-sheet rich fibrils that accumulate and form insoluble neuronal inclusions. Studies of patient derived brain tissues have concluded that these inclusions are associated with Parkinson's disease, the second most common neurodegenerative disorder, and other synuclein related diseases called synucleinopathies. In addition, repetitions of and specific mutations to the SNCA gene, the gene that encodes α-syn, results in an increased disposition for synucleinopathies. The latest advances in cryo-EM structure determination and real-space helical reconstruction methods have resulted in over 60 in vitro structures of α-syn fibrils solved to date, with a handful of these reaching a resolution below 2.5 Å. Here, we provide a protocol for α-syn protein expression, purification, and fibrilization. We detail how sample quality is assessed by negative stain transmission electron microscopy (NS-TEM) analysis and followed by sample vitrification using the Vitrobot Mark IV vitrification robot. We provide a detailed step by step protocol for high resolution cryo-EM structure determination of α-syn fibrils using RELION and a series of specialized helical reconstruction tools that can be run within RELION. Finally, we detail how ChimeraX, Coot, and Phenix are used to build and refine a molecular model into the high resolution cryo-EM map. This workflow resulted in a 2.04 Å structure of α-syn fibrils with excellent resolution of residues 36 to 97 and an additional island of density for residues 15 to 22 that had not been previously reported. This workflow should serve as a starting point for individuals new to the neurodegeneration and structural biology fields. Together, this procedure lays the foundation for advanced structural studies of α-synuclein and other amyloid fibrils.

Molecular architecture of the assembly of Bacillus spore coat protein GerQ revealed by cryo-EM

Protein filaments are ubiquitous in nature and have diverse biological functions. Cryo-electron microscopy (cryo-EM) enables the determination of atomic structures, even from native samples, and is capable of identifying previously unknown filament species through high-resolution cryo-EM maps. In this study, we determine the structure of an unreported filament species from a cryo-EM dataset collected from Bacillus amyloiquefaciens biofilms. These filaments are composed of GerQ, a spore coat protein known to be involved in Bacillus spore germination. GerQ assembles into a structurally stable architecture consisting of rings containing nine subunits, which stacks to form filaments. Molecular dockings and model predictions suggest that this nine-subunit structure is suitable for binding CwlJ, a protein recruited by GerQ and essential for Ca2+-DPA induced spore germination. While the assembly state of GerQ within the spores and the direct interaction between GerQ and CwlJ have yet to be validated through further experiments, our findings provide valuable insights into the self-assembly of GerQ and enhance our understanding of its role in spore germination.

CryoTEN: Efficiently Enhancing Cryo-EM Density Maps Using Transformers.

Cryogenic Electron Microscopy (cryo-EM) is a core experimental technique used to determine the structure of macromolecules such as proteins. However, the effectiveness of cryo-EM is often hindered by the noise and missing density values in cryo-EM density maps caused by experimental conditions such as low contrast and conformational heterogeneity. Although various global and local map sharpening techniques are widely employed to improve cryo-EM density maps, it is still challenging to efficiently improve their quality for building better protein structures from them. In this study, we introduce CryoTEN - a three-dimensional U-Net style transformer to improve cryo-EM maps effectively. CryoTEN is trained using a diverse set of 1,295 cryo-EM maps as inputs and their corresponding simulated maps generated from known protein structures as targets. An independent test set containing 150 maps is used to evaluate CryoTEN, and the results demonstrate that it can robustly enhance the quality of cryo-EM density maps. In addition, the automatic de novo protein structure modeling shows that the protein structures built from the density maps processed by CryoTEN have substantially better quality than those built from the original maps. Compared to the existing state-of-the-art deep learning methods for enhancing cryo-EM density maps, CryoTEN ranks second in improving the quality of density maps, while running > 10 times faster and requiring much less GPU memory than them. The source code and data is freely available at https://github.com/jianlin-cheng/cryoten.

3D Variability Analysis Reveals a Hidden Conformational Change Controlling Ammonia Transport in Human Asparagine Synthetase.

How motions in enzymes might be linked to catalytic function is of considerable general interest. Advances in X-ray crystallography and cryogenic electron microscopy (cryo-EM) offer the promise of elucidating functionally relevant conformational changes that are not easily studied by other biophysical methods. Here we use 3D variability analysis (3DVA) of the cryo-EM map for wild-type (WT) human asparagine synthetase (ASNS) to identify a functional role for the Arg-142 side chain as a gate that mediates ammonia access to a catalytically relevant intramolecular tunnel. Our 3DVA-derived hypothesis is assessed experimentally, using the R142I variant in which Arg-142 is replaced by isoleucine, and by molecular dynamics (MD) simulations on independent, computational models of the WT human ASNS monomer and its catalytically relevant, ternary complex with β-aspartyl-AMP and MgPPi. Residue fluctuations in the MD trajectories for the human ASNS monomer are consistent with those determined for 3DVA-derived structures. These MD simulations also indicate that the gating function of Arg-142 is separate from the molecular events that form a continuous tunnel linking the two active sites. Experimental support for Arg-142 playing a role in intramolecular ammonia translocation is provided by the glutamine-dependent synthetase activity of the R142 variant relative to WT ASNS. MD simulations of computational models for the R142I variant and the R142I/β-aspartyl-AMP/MgPPi ternary complex provide a possible molecular basis for this observation. Overall, the combination of 3DVA with MD simulations is a generally applicable approach to generate testable hypotheses of how conformational changes in buried side chains might regulate function in enzymes.

Small LEA proteins mitigate air-water interface damage to fragile cryo-EM samples during plunge freezing

Air-water interface (AWI) interactions during cryo-electron microscopy (cryo-EM) sample preparation cause significant sample loss, hindering structural biology research. Organisms like nematodes and tardigrades produce Late Embryogenesis Abundant (LEA) proteins to withstand desiccation stress. Here we show that these LEA proteins, when used as additives during plunge freezing, effectively mitigate AWI damage to fragile multi-subunit molecular samples. The resulting high-resolution cryo-EM maps are comparable to or better than those obtained using existing AWI damage mitigation methods. Cryogenic electron tomography reveals that particles are localized at specific interfaces, suggesting LEA proteins form a barrier at the AWI. This interaction may explain the observed sample-dependent preferred orientation of particles. LEA proteins offer a simple, cost-effective, and adaptable approach for cryo-EM structural biologists to overcome AWI-related sample damage, potentially revitalizing challenging projects and advancing the field of structural biology.

CryoTRANS: predicting high-resolution maps of rare conformations from self-supervised trajectories in cryo-EM

Cryogenic electron microscopy (cryo-EM) has revolutionized structural biology, enabling efficient determination of structures at near-atomic resolutions. However, a common challenge arises from the severe imbalance among various conformations of vitrified particles, leading to low-resolution reconstructions in rare conformations due to a lack of particle images in these quasi-stable states. We introduce CryoTRANS, a method that predicts high-resolution maps of rare conformations by constructing a self-supervised pseudo-trajectory between density maps of varying resolutions. This trajectory is represented by an ordinary differential equation parameterized by a deep neural network, ensuring retention of detailed structures from high-resolution density maps. By leveraging a single high-resolution density map, CryoTRANS significantly improves the reconstruction of rare conformations and has been validated on four real-world datasets: alpha-2-macroglobulin, actin-binding protein complexes, SARS-CoV-2 spike glycoprotein, and the 70S ribosome. CryoTRANS can also predict high-resolution structures in cryogenic electron tomography maps using a high-resolution cryo-EM map.

Structures of influenza A and B replication complexes give insight into avian to human host adaptation and reveal a role of ANP32 as an electrostatic chaperone for the apo-polymerase

Replication of influenza viral RNA depends on at least two viral polymerases, a parental replicase and an encapsidase, and cellular factor ANP32. ANP32 comprises an LRR domain and a long C-terminal low complexity acidic region (LCAR). Here we present evidence suggesting that ANP32 is recruited to the replication complex as an electrostatic chaperone that stabilises the encapsidase moiety within apo-polymerase symmetric dimers that are distinct for influenza A and B polymerases. The ANP32 bound encapsidase, then forms the asymmetric replication complex with the replicase, which is embedded in a parental ribonucleoprotein particle (RNP). Cryo-EM structures reveal the architecture of the influenza A and B replication complexes and the likely trajectory of the nascent RNA product into the encapsidase. The cryo-EM map of the FluB replication complex shows extra density attributable to the ANP32 LCAR wrapping around and stabilising the apo-encapsidase conformation. These structures give new insight into the various mutations that adapt avian strain polymerases to use the distinct ANP32 in mammalian cells.

Signaling by a bacterial phytochrome histidine kinase involves a conformational cascade reorganizing the dimeric photoreceptor

Phytochromes (Phys) are a divergent cohort of bili-proteins that detect light through reversible interconversion between dark-adapted Pr and photoactivated Pfr states. While our understandings of downstream events are emerging, it remains unclear how Phys translate light into an interpretable conformational signal. Here, we present models of both states for a dimeric Phy with histidine kinase (HK) activity from the proteobacterium Pseudomonas syringae, which were built from high-resolution cryo-EM maps (2.8–3.4-Å) of the photosensory module (PSM) and its following signaling (S) helix together with lower resolution maps for the downstream output region augmented by RoseTTAFold and AlphaFold structural predictions. The head-to-head models reveal the PSM and its photointerconversion mechanism with strong clarity, while the HK region is interpretable but relatively mobile. Pr/Pfr comparisons show that bilin phototransformation alters PSM architecture culminating in a scissoring motion of the paired S-helices linking the PSMs to the HK bidomains that ends in reorientation of the paired catalytic ATPase modules relative to the phosphoacceptor histidines. This action apparently primes autophosphorylation enroute to phosphotransfer to the cognate DNA-binding response regulator AlgB which drives quorum-sensing behavior through transient association with the photoreceptor. Collectively, these models illustrate how light absorption conformationally translates into accelerated signaling by Phy-type kinases.

Bayesian reweighting of biomolecular structural ensembles using heterogeneous cryo-EM maps with the cryoENsemble method

Cryogenic electron microscopy (cryo-EM) has emerged as a powerful method for the determination of structures of complex biological molecules. The accurate characterisation of the dynamics of such systems, however, remains a challenge. To address this problem, we introduce cryoENsemble, a method that applies Bayesian reweighting to conformational ensembles derived from molecular dynamics simulations to improve their agreement with cryo-EM data, thus enabling the extraction of dynamics information. We illustrate the use of cryoENsemble to determine the dynamics of the ribosome-bound state of the co-translational chaperone trigger factor (TF). We also show that cryoENsemble can assist with the interpretation of low-resolution, noisy or unaccounted regions of cryo-EM maps. Notably, we are able to link an unaccounted part of the cryo-EM map to the presence of another protein (methionine aminopeptidase, or MetAP), rather than to the dynamics of TF, and model its TF-bound state. Based on these results, we anticipate that cryoENsemble will find use for challenging heterogeneous cryo-EM maps for biomolecular systems encompassing dynamic components.

Correction of preferred orientation-induced distortion in cryo-electron microscopy maps.

Reconstruction maps of cryo-electron microscopy (cryo-EM) exhibit distortion when the cryo-EM dataset is incomplete, usually caused by unevenly distributed orientations. Prior efforts had been attempted to address this preferred orientation problem using tilt-collection strategy and modifications to grids or to air-water interfaces. However, these approaches often require time-consuming experiments, and the effect was always protein dependent. Here, we developed a procedure containing removing misaligned particles and an iterative reconstruction method based on signal-to-noise ratio of Fourier component to correct this distortion by recovering missing data using a purely computational algorithm. This procedure called signal-to-noise ratio iterative reconstruction method (SIRM) was applied on incomplete datasets of various proteins to fix distortion in cryo-EM maps and to a more isotropic resolution. In addition, SIRM provides a better reference map for further reconstruction refinements, resulting in an improved alignment, which ultimately improves map quality and benefits model building.

HIV-1 Intasomes Assembled with Excess Integrase C-Terminal Domain Protein Facilitate Structural Studies by Cryo-EM and Reveal the Role of the Integrase C-Terminal Tail in HIV-1 Integration.

Retroviral integration is mediated by intasome nucleoprotein complexes wherein a pair of viral DNA ends are bridged together by a multimer of integrase (IN). Atomic-resolution structures of HIV-1 intasomes provide detailed insights into the mechanism of integration and inhibition by clinical IN inhibitors. However, previously described HIV-1 intasomes are highly heterogeneous and have the tendency to form stacks, which is a limiting factor in determining high-resolution cryo-EM maps. We have assembled HIV-1 intasomes in the presence of excess IN C-terminal domain protein, which was readily incorporated into the intasomes. The purified intasomes were largely homogeneous and exhibited minimal stacking tendencies. The cryo-EM map resolution was further improved to 2.01 Å, which will greatly facilitate structural studies of IN inhibitor action and drug resistance mechanisms. The C-terminal 18 residues of HIV-1 IN, which are critical for virus replication and integration in vitro, have not been well resolved in previous intasome structures, and its function remains unclear. We show that the C-terminal tail participates in intasome assembly, resides within the intasome core, and forms a small alpha helix (residues 271-276). Mutations that disrupt alpha helix integrity impede IN activity in vitro and disrupt HIV-1 infection at the step of viral DNA integration.

Modeling Ligands into Maps Derived from Electron Cryomicroscopy.

Deciphering the protein-ligand interactions in a macromolecular complex is crucial for understanding the molecular mechanism, underlying biological processes, and drug development. In recent years, cryogenic sample electron microscopy (cryoEM) has emerged as a powerful technique to determine the structures of macromolecules and to investigate the mode of ligand binding at near-atomic resolution. Identifying and modeling non-protein molecules in cryoEM maps is often challenging due to anisotropic resolution across the molecule of interest and inherent noise in the data. In this article, the readers are introduced to various software and methods currently used for ligand identification, model building, and refinement of atomic coordinates using selected macromolecules. One of the simplest ways to identify the presence of a ligand, as illustrated with the enolase enzyme, is to subtract the two maps obtained with and without the ligand. The extra density of the ligand is likely to stand out in the difference map even at a higher threshold. There are instances, as shown in the case of metabotropic Glutamate receptor mGlu5, when such simple difference maps cannot be generated. The recently introduced method of deriving the Fo-Fc omit map can serve as a tool for validating and demonstrating the presence of the ligand. Finally, using the well-studied β-galactosidase as an example, the effect of resolution on modeling the ligands and solvent molecules in cryoEM maps is analyzed, and an outlook on how cryoEM can be used in drug discovery is presented.

Accurate model and ensemble refinement using cryo-electron microscopy maps and Bayesian inference.

Converting cryo-electron microscopy (cryo-EM) data into high-quality structural models is a challenging problem of outstanding importance. Current refinement methods often generate unbalanced models in which physico-chemical quality is sacrificed for excellent fit to the data. Furthermore, these techniques struggle to represent the conformational heterogeneity averaged out in low-resolution regions of density maps. Here we introduce EMMIVox, a Bayesian inference approach to determine single-structure models as well as structural ensembles from cryo-EM maps. EMMIVox automatically balances experimental information with accurate physico-chemical models of the system and the surrounding environment, including waters, lipids, and ions. Explicit treatment of data correlation and noise as well as inference of accurate B-factors enable determination of structural models and ensembles with both excellent fit to the data and high stereochemical quality, thus outperforming state-of-the-art refinement techniques. EMMIVox represents a flexible approach to determine high-quality structural models that will contribute to advancing our understanding of the molecular mechanisms underlying biological functions.

Dual protection by Bcp1 and Rkm1 ensures incorporation of uL14 into pre-60S ribosomal subunits.

Eukaryotic ribosomal proteins contain extended regions essential for translation coordination. Dedicated chaperones stabilize the associated ribosomal proteins. We identified Bcp1 as the chaperone of uL14 in Saccharomyces cerevisiae. Rkm1, the lysine methyltransferase of uL14, forms a ternary complex with Bcp1 and uL14 to protect uL14. Rkm1 is transported with uL14 by importins to the nucleus, and Bcp1 disassembles Rkm1 and importin from uL14 simultaneously in a RanGTP-independent manner. Molecular docking, guided by crosslinking mass spectrometry and validated by a low-resolution cryo-EM map, reveals the correlation between Bcp1, Rkm1, and uL14, demonstrating the protection model. In addition, the ternary complex also serves as a surveillance point, whereas incorrect uL14 is retained on Rkm1 and prevented from loading to the pre-60S ribosomal subunits. This study reveals the molecular mechanism of how uL14 is protected and quality checked by serial steps to ensure its safe delivery from the cytoplasm until its incorporation into the 60S ribosomal subunit.

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.