DeepTracer for fast de novo cryo-EM protein structure modeling and special studies on CoV-related complexes

Automated model building and protein identification in cryo-EM maps

Interpreting electron cryo-microscopy (cryo-EM) maps with atomic models requires high levels of expertise and labour-intensive manual intervention. We present ModelAngelo, a machine-learning approach for automated atomic model building in cryo-EM maps. By combining information from the cryo-EM map with information from protein sequence and structure in a single graph neural network, ModelAngelo builds atomic models for proteins that are of similar quality as those generated by human experts. For nucleotides, ModelAngelo builds backbones with similar accuracy as humans. By using its predicted amino acid probabilities for each residue in hidden Markov model sequence searches, ModelAngelo outperforms human experts in the identification of proteins with unknown sequences. ModelAngelo will thus remove bottlenecks and increase objectivity in cryo-EM structure determination.

Artificial intelligence (AI) in Monkeypox infection prevention

Monkeypox is a possible public health concern that requires appropriate attention in order to prevent the spread of the disease. Currently, artificial intelligence (AI) is making a significant impact on precision medicine, reshaping and integrating the large amount of data derived from multiomics analyses and revolutionizing the deep-learning strategies. There has been a significant progress in the use of AI to detect, screen, diagnose, and classify diseases, characterize virus genomes, assess biomarkers for prognostic and predictive purposes, and develop follow-up strategies. Hence, it is possible to use AI for the identification of disease clusters, cases monitoring, forecasting the future outbreak, determining mortality risk, diagnosing, managing, and identifying patterns for studying disease trends. AI may also be utilized to assist gene therapy and other therapies that we are not currently able to use in healthcare. It is possible to combine pharmacology and gene therapy with regenerative medicine with the help of AI. It will directly benefit the public in overcoming fear and panic of health risks. Therefore, AI can be an effective weapon to fight against Monkeypox infection, and may prove to be an invaluable future tool in improving the clinical management of patients. Key Points: Emergence and spread of the Monkeypox virus is a new public health crisis; threatening the world. This opinion piece highlights the urgently required information for immediate delivery of solutions on controlling and monitoring the spread of Monkeypox infection through Artificial IntelligenceCommunicated by Ramaswamy H. Sarma.

Adaptive Ensemble Refinement of Protein Structures in High Resolution Electron Microscopy Density Maps with Radical Augmented Molecular Dynamics Flexible Fitting

Recent advances in cryo-electron microscopy (cryo-EM) have enabled modeling macromolecular complexes that are essential components of the cellular machinery. The density maps derived from cryo-EM experiments are often integrated with manual, knowledge-driven or artificial intelligence-driven and physics-guided computational methods to build, fit, and refine molecular structures. Going beyond a single stationary-structure determination scheme, it is becoming more common to interpret the experimental data with an ensemble of models that contributes to an average observation. Hence, there is a need to decide on the quality of an ensemble of protein structures on-the-fly while refining them against the density maps. We introduce such an adaptive decision-making scheme during the molecular dynamics flexible fitting (MDFF) of biomolecules. Using RADICAL-Cybertools, the new RADICAL augmented MDFF implementation (R-MDFF) is examined in high-performance computing environments for refinement of two prototypical protein systems, adenylate kinase and carbon monoxide dehydrogenase. For these test cases, use of multiple replicas in flexible fitting with adaptive decision making in R-MDFF improves the overall correlation to the density by 40% relative to the refinements of the brute-force MDFF. The improvements are particularly significant at high, 2-3 Å map resolutions. More importantly, the ensemble model captures key features of biologically relevant molecular dynamics that are inaccessible to a single-model interpretation. Finally, the pipeline is applicable to systems of growing sizes, which is demonstrated using ensemble refinement of capsid proteins from the chimpanzee adenovirus. The overhead for decision making remains low and robust to computing environments. The software is publicly available on GitHub and includes a short user guide to install R-MDFF on different computing environments, from local Linux-based workstations to high-performance computing environments.

ComplexQA: a deep graph learning approach for protein complex structure assessment

MOTIVATION

In recent years, the end-to-end deep learning method for single-chain protein structure prediction has achieved high accuracy. For example, the state-of-the-art method AlphaFold, developed by Google, has largely increased the accuracy of protein structure predictions to near experimental accuracy in some of the cases. At the same time, there are few methods that can evaluate the quality of protein complexes at the residue level. In particular, evaluating the quality of residues at the interface of protein complexes can lead to a wide range of applications, such as protein function analysis and drug design. In this paper, we introduce a new deep graph neural network-based method ComplexQA, to evaluate the local quality of interfaces for protein complexes by utilizing the residue-level structural information in 3D space and the sequence-level constraints.

RESULTS

We benchmark our method to other state-of-the-art quality assessment approaches on the HAF2 and DBM55-AF2 datasets (high-quality structural models predicted by AlphaFold-Multimer), and the BM5 docking dataset. The experimental results show that our proposed method achieves better or similar performance compared with other state-of-the-art methods, especially on difficult targets which only contain a few acceptable models. Our method is able to suggest a score for each interfac e residue, which demonstrates a powerful assessment tool for the ever-increasing number of protein complexes.

AVAILABILITY

https://github.com/Cao-Labs/ComplexQA.git. Contact: caora@plu.edu.

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.

Novel Artificial Intelligence-Based Approaches for Ab Initio Structure Determination and Atomic Model Building for Cryo-Electron Microscopy

Single particle cryo-electron microscopy (cryo-EM) has emerged as the prevailing method for near-atomic structure determination, shedding light on the important molecular mechanisms of biological macromolecules. However, the inherent dynamics and structural variability of biological complexes coupled with the large number of experimental images generated by a cryo-EM experiment make data processing nontrivial. In particular, ab initio reconstruction and atomic model building remain major bottlenecks that demand substantial computational resources and manual intervention. Approaches utilizing recent innovations in artificial intelligence (AI) technology, particularly deep learning, have the potential to overcome the limitations that cannot be adequately addressed by traditional image processing approaches. Here, we review newly proposed AI-based methods for ab initio volume generation, heterogeneous 3D reconstruction, and atomic model building. We highlight the advancements made by the implementation of AI methods, as well as discuss remaining limitations and areas for future development.

Two dramatically distinct archaeal type IV pili structures formed by the same pilin

Type IV pili (T4P) represent one of the most common varieties of surface appendages in archaea. These filaments, assembled from relatively small pilin proteins, can be many microns long and serve diverse functions, including adhesion, biofilm formation, motility, and intercellular communication. Using cryo-electron microscopy (cryo-EM), we determined atomic structures of two dramatically different T4P from Saccharolobus islandicus REY15A. Unexpectedly, both pili were assembled from the same pilin protein but under different growth conditions. One filament, denoted mono-pilus, conforms to canonical archaeal T4P structures where all subunits are equivalent, whereas in the other filament, the tri-pilus, the same protein exists in three different conformations. The three conformations involve different orientations of the outer immunoglobulin (Ig)-like domains, mediated by a very flexible linker, and all three of these conformations are very different from the single conformation found in the mono-pilus. Remarkably, the outer domains rotate nearly 180° between the mono- and tri-pilus conformations, formally similar to what has been shown for outer domains in bacterial flagellar filaments, despite lack of homology between bacterial flagella and archaeal T4P. Interestingly, both forms of pili require the same ATPase and TadC-like membrane pore for assembly, indicating that the same secretion system can produce structurally very different filaments. However, the expression of the ATPase and TadC genes was significantly different under the conditions yielding mono- and tri-pili. While archaeal T4P are homologs of archaeal flagellar filaments, our results show that in contrast to the rigid supercoil that the flagellar filaments must adopt to serve as helical propellers, archaeal T4P are likely to have fewer constraints on their structure and enjoy more internal degrees of freedom.

Enhancing cryo-EM maps with 3D deep generative networks for assisting protein structure modeling

MOTIVATION

The tertiary structures of an increasing number of biological macromolecules have been determined using cryo-electron microscopy (cryo-EM). However, there are still many cases where the resolution is not high enough to model the molecular structures with standard computational tools. If the resolution obtained is near the empirical borderline (3-4.5 Å), improvement in the map quality facilitates structure modeling.

RESULTS

We report EM-GAN, a novel approach that modifies an input cryo-EM map to assist protein structure modeling. The method uses a 3D generative adversarial network (GAN) that has been trained on high- and low-resolution density maps to learn the density patterns, and modifies the input map to enhance its suitability for modeling. The method was tested extensively on a dataset of 65 EM maps in the resolution range of 3-6 Å and showed substantial improvements in structure modeling using popular protein structure modeling tools.

AVAILABILITY AND IMPLEMENTATION

https://github.com/kiharalab/EM-GAN, Google Colab: https://tinyurl.com/3ccxpttx.

Machine Learning Methods for Small Data Challenges in Molecular Science

Small data are often used in scientific and engineering research due to the presence of various constraints, such as time, cost, ethics, privacy, security, and technical limitations in data acquisition. However, big data have been the focus for the past decade, small data and their challenges have received little attention, even though they are technically more severe in machine learning (ML) and deep learning (DL) studies. Overall, the small data challenge is often compounded by issues, such as data diversity, imputation, noise, imbalance, and high-dimensionality. Fortunately, the current big data era is characterized by technological breakthroughs in ML, DL, and artificial intelligence (AI), which enable data-driven scientific discovery, and many advanced ML and DL technologies developed for big data have inadvertently provided solutions for small data problems. As a result, significant progress has been made in ML and DL for small data challenges in the past decade. In this review, we summarize and analyze several emerging potential solutions to small data challenges in molecular science, including chemical and biological sciences. We review both basic machine learning algorithms, such as linear regression, logistic regression (LR), k-nearest neighbor (KNN), support vector machine (SVM), kernel learning (KL), random forest (RF), and gradient boosting trees (GBT), and more advanced techniques, including artificial neural network (ANN), convolutional neural network (CNN), U-Net, graph neural network (GNN), Generative Adversarial Network (GAN), long short-term memory (LSTM), autoencoder, transformer, transfer learning, active learning, graph-based semi-supervised learning, combining deep learning with traditional machine learning, and physical model-based data augmentation. We also briefly discuss the latest advances in these methods. Finally, we conclude the survey with a discussion of promising trends in small data challenges in molecular science.

Automated simulation-based membrane protein refinement into cryo-EM data

The resolution revolution has increasingly enabled single-particle cryogenic electron microscopy (cryo-EM) reconstructions of previously inaccessible systems, including membrane proteins-a category that constitutes a disproportionate share of drug targets. We present a protocol for using density-guided molecular dynamics simulations to automatically refine atomistic models into membrane protein cryo-EM maps. Using adaptive force density-guided simulations as implemented in the GROMACS molecular dynamics package, we show how automated model refinement of a membrane protein is achieved without the need to manually tune the fitting force ad hoc. We also present selection criteria to choose the best-fit model that balances stereochemistry and goodness of fit. The proposed protocol was used to refine models into a new cryo-EM density of the membrane protein maltoporin, either in a lipid bilayer or detergent micelle, and we found that results do not substantially differ from fitting in solution. Fitted structures satisfied classical model-quality metrics and improved the quality and the model-to-map correlation of the x-ray starting structure. Additionally, the density-guided fitting in combination with generalized orientation-dependent all-atom potential was used to correct the pixel-size estimation of the experimental cryo-EM density map. This work demonstrates the applicability of a straightforward automated approach to fitting membrane protein cryo-EM densities. Such computational approaches promise to facilitate rapid refinement of proteins under different conditions or with various ligands present, including targets in the highly relevant superfamily of membrane proteins.

Chromogranin B (CHGB) is dimorphic and responsible for dominant anion channels delivered to cell surface via regulated secretion

Regulated secretion is conserved in all eukaryotes. In vertebrates granin family proteins function in all key steps of regulated secretion. Phase separation and amyloid-based storage of proteins and small molecules in secretory granules require ion homeostasis to maintain their steady states, and thus need ion conductances in granule membranes. But granular ion channels are still elusive. Here we show that granule exocytosis in neuroendocrine cells delivers to cell surface dominant anion channels, to which chromogranin B (CHGB) is critical. Biochemical fractionation shows that native CHGB distributes nearly equally in soluble and membrane-bound forms, and both reconstitute highly selective anion channels in membrane. Confocal imaging resolves granular membrane components including proton pumps and CHGB in puncta on the cell surface after stimulated exocytosis. High pressure freezing immuno-EM reveals a major fraction of CHGB at granule membranes in rat pancreatic β-cells. A cryo-EM structure of bCHGB dimer of a nominal 3.5 Å resolution delineates a central pore with end openings, physically sufficient for membrane-spanning and large single channel conductance. Together our data support that CHGB-containing (CHGB+) channels are characteristic of regulated secretion, and function in granule ion homeostasis near the plasma membrane or possibly in other intracellular processes.

Structural specializations of the sperm tail

Sperm motility is crucial to reproductive success in sexually reproducing organisms. Impaired sperm movement causes male infertility, which is increasing globally. Sperm are powered by a microtubule-based molecular machine-the axoneme-but it is unclear how axonemal microtubules are ornamented to support motility in diverse fertilization environments. Here, we present high-resolution structures of native axonemal doublet microtubules (DMTs) from sea urchin and bovine sperm, representing external and internal fertilizers. We identify >60 proteins decorating sperm DMTs; at least 15 are sperm associated and 16 are linked to infertility. By comparing DMTs across species and cell types, we define core microtubule inner proteins (MIPs) and analyze evolution of the tektin bundle. We identify conserved axonemal microtubule-associated proteins (MAPs) with unique tubulin-binding modes. Additionally, we identify a testis-specific serine/threonine kinase that links DMTs to outer dense fibers in mammalian sperm. Our study provides structural foundations for understanding sperm evolution, motility, and dysfunction at a molecular level.

Extracellular cytochrome nanowires appear to be ubiquitous in prokaryotes

Electrically conductive appendages from the anaerobic bacterium Geobacter sulfurreducens, recently identified as extracellular cytochrome nanowires (ECNs), have received wide attention due to numerous potential applications. However, whether other organisms employ similar ECNs for electron transfer remains unknown. Here, using cryoelectron microscopy, we describe the atomic structures of two ECNs from two major orders of hyperthermophilic archaea present in deep-sea hydrothermal vents and terrestrial hot springs. Homologs of Archaeoglobus veneficus ECN are widespread among mesophilic methane-oxidizing Methanoperedenaceae, alkane-degrading Syntrophoarchaeales archaea, and in the recently described megaplasmids called Borgs. The ECN protein subunits lack similarities in their folds; however, they share a common heme arrangement, suggesting an evolutionarily optimized heme packing for efficient electron transfer. The detection of ECNs in archaea suggests that filaments containing closely stacked hemes may be a common and widespread mechanism for long-range electron transfer in both prokaryotic domains of life.

Structures of sperm flagellar doublet microtubules expand the genetic spectrum of male infertility

Sperm motility is crucial for successful fertilization. Highly decorated doublet microtubules (DMTs) form the sperm tail skeleton, which propels the movement of spermatozoa. Using cryo-electron microscopy (cryo-EM) and artificial intelligence (AI)-based modeling, we determined the structures of mouse and human sperm DMTs and built an atomic model of the 48-nm repeat of the mouse sperm DMT. Our analysis revealed 47 DMT-associated proteins, including 45 microtubule inner proteins (MIPs). We identified 10 sperm-specific MIPs, including seven classes of Tektin5 in the lumen of the A tubule and FAM166 family members that bind the intra-tubulin interfaces. Interestingly, the human sperm DMT lacks some MIPs compared with the mouse sperm DMT. We also discovered variants in 10 distinct MIPs associated with a subtype of asthenozoospermia characterized by impaired sperm motility without evident morphological abnormalities. Our study highlights the conservation and tissue/species specificity of DMTs and expands the genetic spectrum of male infertility.

Improvement of cryo-EM maps by simultaneous local and non-local deep learning

Cryo-EM has emerged as the most important technique for structure determination of macromolecular complexes. However, raw cryo-EM maps often exhibit loss of contrast at high resolution and heterogeneity over the entire map. As such, various post-processing methods have been proposed to improve cryo-EM maps. Nevertheless, it is still challenging to improve both the quality and interpretability of EM maps. Addressing the challenge, we present a three-dimensional Swin-Conv-UNet-based deep learning framework to improve cryo-EM maps, named EMReady, by not only implementing both local and non-local modeling modules in a multiscale UNet architecture but also simultaneously minimizing the local smooth L1 distance and maximizing the non-local structural similarity between processed experimental and simulated target maps in the loss function. EMReady was extensively evaluated on diverse test sets of 110 primary cryo-EM maps and 25 pairs of half-maps at 3.0-6.0 Å resolutions, and compared with five state-of-the-art map post-processing methods. It is shown that EMReady can not only robustly enhance the quality of cryo-EM maps in terms of map-model correlations, but also improve the interpretability of the maps in automatic de novo model building.

CryoFold 2.0: Cryo-EM Structure Determination with MELD

Cryo-electron microscopy data are becoming more prevalent and accessible at higher resolution levels, leading to the development of new computational tools to determine the atomic structure of macromolecules. However, while existing tools adapted from X-ray crystallography are suitable for the highest-resolution maps, new tools are needed for lower-resolution levels and to account for map heterogeneity. In this article, we introduce CryoFold 2.0, an integrative physics-based approach that combines Bayesian inference and the ability to handle multiple data sources with the molecular dynamics flexible fitting (MDFF) approach to determine the structures of macromolecules by using cryo-EM data. CryoFold 2.0 is incorporated into the MELD (modeling employing limited data) plugin, resulting in a pipeline that is more computationally efficient and accurate than running MELD or MDFF alone. The approach requires fewer computational resources and shorter simulation times than the original CryoFold, and it minimizes manual intervention. We demonstrate the effectiveness of the approach on eight different systems, highlighting its various benefits.

Native doublet microtubules from Tetrahymena thermophila reveal the importance of outer junction proteins

Cilia are ubiquitous eukaryotic organelles responsible for cellular motility and sensory functions. The ciliary axoneme is a microtubule-based cytoskeleton consisting of two central singlets and nine outer doublet microtubules. Cryo-electron microscopy-based studies have revealed a complex network inside the lumen of both tubules composed of microtubule-inner proteins (MIPs). However, the functions of most MIPs remain unknown. Here, we present single-particle cryo-EM-based analyses of the Tetrahymena thermophila native doublet microtubule and identify 42 MIPs. These data shed light on the evolutionarily conserved and diversified roles of MIPs. In addition, we identified MIPs potentially responsible for the assembly and stability of the doublet outer junction. Knockout of the evolutionarily conserved outer junction component CFAP77 moderately diminishes Tetrahymena swimming speed and beat frequency, indicating the important role of CFAP77 and outer junction stability in cilia beating generation and/or regulation.

Deep learning for reconstructing protein structures from cryo-EM density maps: Recent advances and future directions

Cryo-Electron Microscopy (cryo-EM) has emerged as a key technology to determine the structure of proteins, particularly large protein complexes and assemblies in recent years. A key challenge in cryo-EM data analysis is to automatically reconstruct accurate protein structures from cryo-EM density maps. In this review, we briefly overview various deep learning methods for building protein structures from cryo-EM density maps, analyze their impact, and discuss the challenges of preparing high-quality data sets for training deep learning models. Looking into the future, more advanced deep learning models of effectively integrating cryo-EM data with other sources of complementary data such as protein sequences and AlphaFold-predicted structures need to be developed to further advance the field.

Three structural solutions for bacterial adhesion pilus stability and superelasticity

Bacterial adhesion pili are key virulence factors that mediate host-pathogen interactions in diverse epithelial environments. Deploying a multimodal approach, we probed the structural basis underpinning the biophysical properties of pili originating from enterotoxigenic (ETEC) and uropathogenic bacteria. Using cryo-electron microscopy we solved the structures of three vaccine target pili from ETEC bacteria, CFA/I, CS17, and CS20. Pairing these and previous pilus structures with force spectroscopy and steered molecular dynamics simulations, we find a strong correlation between subunit-subunit interaction energies and the force required for pilus unwinding, irrespective of genetic similarity. Pili integrate three structural solutions for stabilizing their assemblies: layer-to-layer interactions, N-terminal interactions to distant subunits, and extended loop interactions from adjacent subunits. Tuning of these structural solutions alters the biophysical properties of pili and promotes the superelastic behavior that is essential for sustained bacterial attachment.

Fast and automated protein-DNA/RNA macromolecular complex modeling from cryo-EM maps

Cryo-electron microscopy (cryo-EM) allows a macromolecular structure such as protein-DNA/RNA complexes to be reconstructed in a three-dimensional coulomb potential map. The structural information of these macromolecular complexes forms the foundation for understanding the molecular mechanism including many human diseases. However, the model building of large macromolecular complexes is often difficult and time-consuming. We recently developed DeepTracer-2.0, an artificial-intelligence-based pipeline that can build amino acid and nucleic acid backbones from a single cryo-EM map, and even predict the best-fitting residues according to the density of side chains. The experiments showed improved accuracy and efficiency when benchmarking the performance on independent experimental maps of protein-DNA/RNA complexes and demonstrated the promising future of macromolecular modeling from cryo-EM maps. Our method and pipeline could benefit researchers worldwide who work in molecular biomedicine and drug discovery, and substantially increase the throughput of the cryo-EM model building. The pipeline has been integrated into the web portal https://deeptracer.uw.edu/.

Structural consequences of turnover-induced homocitrate loss in nitrogenase

Nitrogenase catalyzes the ATP-dependent reduction of dinitrogen to ammonia during the process of biological nitrogen fixation that is essential for sustaining life. The active site FeMo-cofactor contains a [7Fe:1Mo:9S:1C] metallocluster coordinated with an R-homocitrate (HCA) molecule. Here, we establish through single particle cryoEM and chemical analysis of two forms of the Azotobacter vinelandii MoFe-protein - a high pH turnover inactivated species and a ∆NifV variant that cannot synthesize HCA - that loss of HCA is coupled to α-subunit domain and FeMo-cofactor disordering, and formation of a histidine coordination site. We further find a population of the ∆NifV variant complexed to an endogenous protein identified through structural and proteomic approaches as the uncharacterized protein NafT. Recognition by endogenous NafT demonstrates the physiological relevance of the HCA-compromised form, perhaps for cofactor insertion or repair. Our results point towards a dynamic active site in which HCA plays a role in enabling nitrogenase catalysis by facilitating activation of the FeMo-cofactor from a relatively stable form to a state capable of reducing dinitrogen under ambient conditions.

Ring-stacked capsids of white spot syndrome virus and structural transitions with genome ejection

White spot syndrome virus (WSSV) is one of the largest DNA viruses and the major pathogen responsible for white spot syndrome in crustaceans. The WSSV capsid is critical for genome encapsulation and ejection and exhibits the rod-shaped and oval-shaped structures during the viral life cycle. However, the detailed architecture of the capsid and the structural transition mechanism remain unclear. Here, using cryo-electron microscopy (cryo-EM), we obtained a cryo-EM model of the rod-shaped WSSV capsid and were able to characterize its ring-stacked assembly mechanism. Furthermore, we identified an oval-shaped WSSV capsid from intact WSSV virions and analyzed the structural transition mechanism from the oval-shaped to rod-shaped capsids induced by high salinity. These transitions, which decrease internal capsid pressure, always accompany DNA release and mostly eliminate the infection of the host cells. Our results demonstrate an unusual assembly mechanism of the WSSV capsid and offer structural insights into the pressure-driven genome release.

The cytoplasmic synthesis and coupled membrane translocation of eukaryotic polyphosphate by signal-activated VTC complex

Inorganic polyphosphate (polyP) is an ancient energy metabolite and phosphate store that occurs ubiquitously in all organisms. The vacuolar transporter chaperone (VTC) complex integrates cytosolic polyP synthesis from ATP and polyP membrane translocation into the vacuolar lumen. In yeast and in other eukaryotes, polyP synthesis is regulated by inositol pyrophosphate (PP-InsP) nutrient messengers, directly sensed by the VTC complex. Here, we report the cryo-electron microscopy structure of signal-activated VTC complex at 3.0 Å resolution. Baker's yeast VTC subunits Vtc1, Vtc3, and Vtc4 assemble into a 3:1:1 complex. Fifteen trans-membrane helices form a novel membrane channel enabling the transport of newly synthesized polyP into the vacuolar lumen. PP-InsP binding orients the catalytic polymerase domain at the entrance of the trans-membrane channel, both activating the enzyme and coupling polyP synthesis and membrane translocation. Together with biochemical and cellular studies, our work provides mechanistic insights into the biogenesis of an ancient energy metabolite.

Simultaneous solving high-resolution structures of various enzymes from human kidney microsomes

The ability to investigate tissues and organs through an integrated systems biology approach has been thought to be unobtainable in the field of structural biology, where the techniques mainly focus on a particular biomacromolecule of interest. Here we report the use of cryo-electron microscopy (cryo-EM) to define the composition of a raw human kidney microsomal lysate. We simultaneously identify and solve cryo-EM structures of four distinct kidney enzymes whose functions have been linked to protein biosynthesis and quality control, biosynthesis of retinoic acid, gluconeogenesis and glycolysis, and the regulation and metabolism of amino acids. Interestingly, all four of these enzymes are directly linked to cellular processes that, when disrupted, can contribute to the onset and progression of diabetes. This work underscores the potential of cryo-EM to facilitate tissue and organ proteomics at the atomic level.

Smart de novo Macromolecular Structure Modeling from Cryo-EM Maps

The study of macromolecular structures has expanded our understanding of the amazing cell machinery and such knowledge has changed how the pharmaceutical industry develops new vaccines in recent years. Traditionally, X-ray crystallography has been the main method for structure determination, however, cryogenic electron microscopy (cryo-EM) has increasingly become more popular due to recent advancements in hardware and software. The number of cryo-EM maps deposited in the EMDataResource (formerly EMDatabase) since 2002 has been dramatically increasing and it continues to do so. De novo macromolecular complex modeling is a labor-intensive process, therefore, it is highly desirable to develop software that can automate this process. Here we discuss our automated, data-driven, and artificial intelligence approaches including map processing, feature extraction, modeling building, and target identification. Recently, we have enabled DNA/RNA modeling in our deep learning-based prediction tool, DeepTracer. We have also developed DeepTracer-ID, a tool that can identify proteins solely based on the cryo-EM map. In this paper, we will present our accumulated experiences in developing deep learning-based methods surrounding macromolecule modeling applications.

Improving Protein-Ligand Interaction Modeling with cryo-EM Data, Templates, and Deep Learning in 2021 Ligand Model Challenge

Elucidating protein-ligand interaction is crucial for studying the function of proteins and compounds in an organism and critical for drug discovery and design. The problem of protein-ligand interaction is traditionally tackled by molecular docking and simulation, which is based on physical forces and statistical potentials and cannot effectively leverage cryo-EM data and existing protein structural information in the protein-ligand modeling process. In this work, we developed a deep learning bioinformatics pipeline (DeepProLigand) to predict protein-ligand interactions from cryo-EM density maps of proteins and ligands. DeepProLigand first uses a deep learning method to predict the structure of proteins from cryo-EM maps, which is averaged with a reference (template) structure of the proteins to produce a combined structure to add ligands. The ligands are then identified and added into the structure to generate a protein-ligand complex structure, which is further refined. The method based on the deep learning prediction and template-based modeling was blindly tested in the 2021 EMDataResource Ligand Challenge and was ranked first in fitting ligands to cryo-EM density maps. These results demonstrate that the deep learning bioinformatics approach is a promising direction for modeling protein-ligand interactions on cryo-EM data using prior structural information.

Discovery, structure, and function of filamentous 3-methylcrotonyl-CoA carboxylase

3-methylcrotonyl-CoA carboxylase (MCC) is a biotin-dependent mitochondrial enzyme necessary for leucine catabolism in most organisms. While the crystal structure of recombinant bacterial MCC has been characterized, the structure and potential polymerization of native MCC remain elusive. Here, we discovered that native MCC from Leishmania tarentolae (LtMCC) forms filaments, and determined the structures of different filament regions at 3.4, 3.9, and 7.3 Å resolution using cryoEM. α6β6 LtMCCs assemble in a twisted-stacks architecture, manifesting as supramolecular rods up to 400 nm. Filamentous LtMCCs bind biotin non-covalently and lack coenzyme A. Filaments elongate by stacking α6β6 LtMCCs onto the exterior α-trimer of the terminal LtMCC. This stacking immobilizes the biotin carboxylase domains, sequestering the enzyme in an inactive state. Our results support a new model for LtMCC catalysis, termed the dual-swinging-domains model, and cast new light on the function of polymerization in the carboxylase superfamily and beyond.

Beyond sequence: Structure-based machine learning

Recent breakthroughs in protein structure prediction demarcate the start of a new era in structural bioinformatics. Combined with various advances in experimental structure determination and the uninterrupted pace at which new structures are published, this promises an age in which protein structure information is as prevalent and ubiquitous as sequence. Machine learning in protein bioinformatics has been dominated by sequence-based methods, but this is now changing to make use of the deluge of rich structural information as input. Machine learning methods making use of structures are scattered across literature and cover a number of different applications and scopes; while some try to address questions and tasks within a single protein family, others aim to capture characteristics across all available proteins. In this review, we look at the variety of structure-based machine learning approaches, how structures can be used as input, and typical applications of these approaches in protein biology. We also discuss current challenges and opportunities in this all-important and increasingly popular field.

Toward structural-omics of the bovine retinal pigment epithelium

The use of an integrated systems biology approach to investigate tissues and organs has been thought to be impracticable in the field of structural biology, where the techniques mainly focus on determining the structure of a particular biomacromolecule of interest. Here, we report the use of cryoelectron microscopy (cryo-EM) to define the composition of a raw bovine retinal pigment epithelium (RPE) lysate. From this sample, we simultaneously identify and solve cryo-EM structures of seven different RPE enzymes whose functions affect neurotransmitter recycling, iron metabolism, gluconeogenesis, glycolysis, axonal development, and energy homeostasis. Interestingly, dysfunction of these important proteins has been directly linked to several neurodegenerative disorders, including Huntington's disease, amyotrophic lateral sclerosis (ALS), Parkinson's disease, Alzheimer's disease, and schizophrenia. Our work underscores the importance of cryo-EM in facilitating tissue and organ proteomics at the atomic level.

Molecular architecture of the Chikungunya virus replication complex

To better understand how positive-strand (+) RNA viruses assemble membrane-associated replication complexes (RCs) to synthesize, process, and transport viral RNA in virus-infected cells, we determined both the high-resolution structure of the core RNA replicase of chikungunya virus and the native RC architecture in its cellular context at subnanometer resolution, using in vitro reconstitution and in situ electron cryotomography, respectively. Within the core RNA replicase, the viral polymerase nsP4, which is in complex with nsP2 helicase-protease, sits in the central pore of the membrane-anchored nsP1 RNA-capping ring. The addition of a large cytoplasmic ring next to the C terminus of nsP1 forms the holo-RNA-RC as observed at the neck of spherules formed in virus-infected cells. These results represent a major conceptual advance in elucidating the molecular mechanisms of RNA virus replication and the principles underlying the molecular architecture of RCs, likely to be shared with many pathogenic (+) RNA viruses.

Improved AlphaFold modeling with implicit experimental information

Machine-learning prediction algorithms such as AlphaFold and RoseTTAFold can create remarkably accurate protein models, but these models usually have some regions that are predicted with low confidence or poor accuracy. We hypothesized that by implicitly including new experimental information such as a density map, a greater portion of a model could be predicted accurately, and that this might synergistically improve parts of the model that were not fully addressed by either machine learning or experiment alone. An iterative procedure was developed in which AlphaFold models are automatically rebuilt on the basis of experimental density maps and the rebuilt models are used as templates in new AlphaFold predictions. We show that including experimental information improves prediction beyond the improvement obtained with simple rebuilding guided by the experimental data. This procedure for AlphaFold modeling with density has been incorporated into an automated procedure for interpretation of crystallographic and electron cryo-microscopy maps.

Neural representations of cryo-EM maps and a graph-based interpretation

Background

Advances in imagery at atomic and near-atomic resolution, such as cryogenic electron microscopy (cryo-EM), have led to an influx of high resolution images of proteins and other macromolecular structures to data banks worldwide. Producing a protein structure from the discrete voxel grid data of cryo-EM maps involves interpolation into the continuous spatial domain. We present a novel data format called the neural cryo-EM map, which is formed from a set of neural networks that accurately parameterize cryo-EM maps and provide native, spatially continuous data for density and gradient. As a case study of this data format, we create graph-based interpretations of high resolution experimental cryo-EM maps.

Results

Normalized cryo-EM map values interpolated using the non-linear neural cryo-EM format are more accurate, consistently scoring less than 0.01 mean absolute error, than a conventional tri-linear interpolation, which scores up to 0.12 mean absolute error. Our graph-based interpretations of 115 experimental cryo-EM maps from 1.15 to 4.0 Å resolution provide high coverage of the underlying amino acid residue locations, while accuracy of nodes is correlated with resolution. The nodes of graphs created from atomic resolution maps (higher than 1.6 Å) provide greater than 99% residue coverage as well as 85% full atomic coverage with a mean of 0.19 Å root mean squared deviation. Other graphs have a mean 84% residue coverage with less specificity of the nodes due to experimental noise and differences of density context at lower resolutions.

Conclusions

The fully continuous and differentiable nature of the neural cryo-EM map enables the adaptation of the voxel data to alternative data formats, such as a graph that characterizes the atomic locations of the underlying protein or macromolecular structure. Graphs created from atomic resolution maps are superior in finding atom locations and may serve as input to predictive residue classification and structure segmentation methods. This work may be generalized to transform any 3D grid-based data format into non-linear, continuous, and differentiable format for downstream geometric deep learning applications.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-022-04942-1.

Tail proteins of phage SU10 reorganize into the nozzle for genome delivery

Escherichia coli phage SU10 belongs to the genus Kuravirus from the class Caudoviricetes of phages with short non-contractile tails. In contrast to other short-tailed phages, the tails of Kuraviruses elongate upon cell attachment. Here we show that the virion of SU10 has a prolate head, containing genome and ejection proteins, and a tail, which is formed of portal, adaptor, nozzle, and tail needle proteins and decorated with long and short fibers. The binding of the long tail fibers to the receptors in the outer bacterial membrane induces the straightening of nozzle proteins and rotation of short tail fibers. After the re-arrangement, the nozzle proteins and short tail fibers alternate to form a nozzle that extends the tail by 28 nm. Subsequently, the tail needle detaches from the nozzle proteins and five types of ejection proteins are released from the SU10 head. The nozzle with the putative extension formed by the ejection proteins enables the delivery of the SU10 genome into the bacterial cytoplasm. It is likely that this mechanism of genome delivery, involving the formation of the tail nozzle, is employed by all Kuraviruses.

E. coli phage SU10 has a short non-contractile tail. Here, the authors show that after cell binding, nozzle proteins and tail fibers of SU10 change conformation to form a nozzle that enables the delivery of the phage DNA into the bacterial cytoplasm.

Building Protein Atomic Models from Cryo-EM Density Maps and Residue Co-Evolution

Electron cryo-microscopy (cryo-EM) has emerged as a powerful method by which to obtain three-dimensional (3D) structures of macromolecular complexes at atomic or near-atomic resolution. However, de novo building of atomic models from near-atomic resolution (3–5 Å) cryo-EM density maps is a challenging task, in particular because poorly resolved side-chain densities hamper sequence assignment by automatic procedures at a lower resolution. Furthermore, segmentation of EM density maps into individual subunits remains a difficult problem when the structure of the subunits is not known, or when significant conformational rearrangement occurs between the isolated and associated form of the subunits. To tackle these issues, we have developed a graph-based method to thread most of the C-α trace of the protein backbone into the EM density map. The EM density is described as a weighted graph such that the resulting minimum spanning tree encompasses the high-density regions of the map. A pruning algorithm cleans the tree and finds the most probable positions of the C-α atoms, by using side-chain density when available, as a collection of C-α trace fragments. By complementing experimental EM maps with contact predictions from sequence co-evolutionary information, we demonstrate that this approach can correctly segment EM maps into individual subunits and assign amino acid sequences to backbone traces to generate atomic models.

Residue-wise local quality estimation for protein models from cryo-EM maps

An increasing number of protein structures are being determined by cryogenic electron microscopy (cryo-EM). Although the resolution of determined cryo-EM density maps is improving in general, there are still many cases where amino acids of a protein are assigned with different levels of confidence. Here we developed a method that identifies potential misassignment of residues in the map, including residue shifts along an otherwise correct main-chain trace. The score, named DAQ, computes the likelihood that the local density corresponds to different amino acids, atoms, and secondary structures, estimated via deep learning, and assesses the consistency of the amino acid assignment in the protein structure model with that likelihood. When DAQ was applied to different versions of model structures in the Protein Data Bank that were derived from the same density maps, a clear improvement in the DAQ score was observed in the newer versions of the models. DAQ also found potential misassignment errors in a substantial number of deposited protein structure models built into cryo-EM maps.

Cryo-EM structures of Escherichia coli Ec86 retron complexes reveal architecture and defence mechanism

First discovered in the 1980s, retrons are bacterial genetic elements consisting of a reverse transcriptase and a non-coding RNA (ncRNA). Retrons mediate antiphage defence in bacteria but their structure and defence mechanisms are unknown. Here, we investigate the Escherichia coli Ec86 retron and use cryo-electron microscopy to determine the structures of the Ec86 (3.1 Å) and cognate effector-bound Ec86 (2.5 Å) complexes. The Ec86 reverse transcriptase exhibits a characteristic right-hand-like fold consisting of finger, palm and thumb subdomains. Ec86 reverse transcriptase reverse-transcribes part of the ncRNA into satellite, multicopy single-stranded DNA (msDNA, a DNA-RNA hybrid) that we show wraps around the reverse transcriptase electropositive surface. In msDNA, both inverted repeats are present and the 3' sides of the DNA/RNA chains are close to the reverse transcriptase active site. The Ec86 effector adopts a two-lobe fold and directly binds reverse transcriptase and msDNA. These findings offer insights into the structure-function relationship of the retron-effector unit and provide a structural basis for the optimization of retron-based genome editing systems.

DeepTracer-ID: De novo protein identification from cryo-EM maps

The recent revolution in cryo-electron microscopy (cryo-EM) has made it possible to determine macromolecular structures directly from cell extracts. However, identifying the correct protein from the cryo-EM map is still challenging and often needs additional sequence information from other techniques, such as tandem mass spectrometry and/or bioinformatics. Here, we present DeepTracer-ID, a server-based approach to identify the candidate protein in a user-provided organism de novo from a cryo-EM map, without the need for additional information. Our method first uses DeepTracer to generate a protein backbone model that best represents the cryo-EM map, and this model is then searched against the library of AlphaFold2 predictions for all proteins in the given organism. This method is highly accurate and robust for high-resolution cryo-EM maps: in all 13 experimental maps tested blindly, DeepTracer-ID identified the correct proteins as the top candidates. Eight of the maps were of known structures, while the other five unpublished maps were validated by prior protein annotation and careful inspection of the model refined into the map. The program also showed promising results for both homomeric and heteromeric protein complexes. This platform is possible because of the recent breakthroughs in large-scale three-dimensional protein structure prediction.

Architecture and self-assembly of the jumbo bacteriophage nuclear shell

Bacteria encode myriad defences that target the genomes of infecting bacteriophage, including restriction-modification and CRISPR-Cas systems1. In response, one family of large bacteriophages uses a nucleus-like compartment to protect its replicating genomes by excluding host defence factors2-4. However, the principal composition and structure of this compartment remain unknown. Here we find that the bacteriophage nuclear shell assembles primarily from one protein, which we name chimallin (ChmA). Combining cryo-electron tomography of nuclear shells in bacteriophage-infected cells and cryo-electron microscopy of a minimal chimallin compartment in vitro, we show that chimallin self-assembles as a flexible sheet into closed micrometre-scale compartments. The architecture and assembly dynamics of the chimallin shell suggest mechanisms for its nucleation and growth, and its role as a scaffold for phage-encoded factors mediating macromolecular transport, cytoskeletal interactions, and viral maturation.

Yeast PI31 inhibits the proteasome by a direct multisite mechanism

Proteasome inhibitors are widely used as therapeutics and research tools, and typically target one of the three active sites, each present twice in the proteasome complex. An endogeneous proteasome inhibitor, PI31, was identified 30 years ago, but its inhibitory mechanism has remained unclear. Here, we identify the mechanism of Saccharomyces cerevisiae PI31, also known as Fub1. Using cryo-electron microscopy (cryo-EM), we show that the conserved carboxy-terminal domain of Fub1 is present inside the proteasome's barrel-shaped core particle (CP), where it simultaneously interacts with all six active sites. Targeted mutations of Fub1 disrupt proteasome inhibition at one active site, while leaving the other sites unaffected. Fub1 itself evades degradation through distinct mechanisms at each active site. The gate that allows substrates to access the CP is constitutively closed, and Fub1 is enriched in mutant CPs with an abnormally open gate, suggesting that Fub1 may function to neutralize aberrant proteasomes, thereby ensuring the fidelity of proteasome-mediated protein degradation.

Model building of protein complexes from intermediate-resolution cryo-EM maps with deep learning-guided automatic assembly

Advances in microscopy instruments and image processing algorithms have led to an increasing number of cryo-electron microscopy (cryo-EM) maps. However, building accurate models into intermediate-resolution EM maps remains challenging and labor-intensive. Here, we propose an automatic model building method of multi-chain protein complexes from intermediate-resolution cryo-EM maps, named EMBuild, by integrating AlphaFold structure prediction, FFT-based global fitting, domain-based semi-flexible refinement, and graph-based iterative assembling on the main-chain probability map predicted by a deep convolutional network. EMBuild is extensively evaluated on diverse test sets of 47 single-particle EM maps at 4.0-8.0 Å resolution and 16 subtomogram averaging maps of cryo-ET data at 3.7-9.3 Å resolution, and compared with state-of-the-art approaches. We demonstrate that EMBuild is able to build high-quality complex structures that are comparably accurate to the manually built PDB structures from the cryo-EM maps. These results demonstrate the accuracy and reliability of EMBuild in automatic model building.

Protein Structural Modeling for Electron Microscopy Maps Using VESPER and MAINMAST

An increasing number of protein structures are determined by cryo-electron microscopy (cryo-EM) and stored in the Electron Microscopy Data Bank (EMDB). To interpret determined cryo-EM maps, several methods have been developed that model the tertiary structure of biomolecules, particularly proteins. Here we show how to use two such methods, VESPER and MAINMAST, which were developed in our group. VESPER is a method mainly for two purposes: fitting protein structure models into an EM map and aligning two EM maps locally or globally to capture their similarity. VESPER represents each EM map as a set of vectors pointing toward denser points. By considering matching the directions of vectors, in general, VESPER aligns maps better than conventional methods that only consider local densities of maps. MAINMAST is a de novo protein modeling tool designed for EM maps with resolution of 3-5 Å or better. MAINMAST builds a protein main chain directly from a density map by tracing dense points in an EM map and connecting them using a tree-graph structure. This article describes how to use these two tools using three illustrative modeling examples. © 2022 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Protein structure model fitting using VESPER Alternate Protocol: Atomic model fitting using VESPER web server Basic Protocol 2: Protein de novo modeling using MAINMAST.

Simulation and Machine Learning Methods for Ion-Channel Structure Determination, Mechanistic Studies and Drug Design

Ion channels are expressed in almost all living cells, controlling the in-and-out communications, making them ideal drug targets, especially for central nervous system diseases. However, owing to their dynamic nature and the presence of a membrane environment, ion channels remain difficult targets for the past decades. Recent advancement in cryo-electron microscopy and computational methods has shed light on this issue. An explosion in high-resolution ion channel structures paved way for structure-based rational drug design and the state-of-the-art simulation and machine learning techniques dramatically improved the efficiency and effectiveness of computer-aided drug design. Here we present an overview of how simulation and machine learning-based methods fundamentally changed the ion channel-related drug design at different levels, as well as the emerging trends in the field.

Archaeal bundling pili of Pyrobaculum calidifontis reveal similarities between archaeal and bacterial biofilms

While biofilms formed by bacteria have received great attention due to their importance in pathogenesis, much less research has been focused on the biofilms formed by archaea. It has been known that extracellular filaments in archaea, such as type IV pili, hami, and cannulae, play a part in the formation of archaeal biofilms. We have used cryo-electron microscopy to determine the atomic structure of a previously uncharacterized class of archaeal surface filaments from hyperthermophilic Pyrobaculum calidifontis. These filaments, which we call archaeal bundling pili (ABP), assemble into highly ordered bipolar bundles. The bipolar nature of these bundles most likely arises from the association of filaments from at least two different cells. The component protein, AbpA, shows homology, both at the sequence and structural level, to the bacterial protein TasA, a major component of the extracellular matrix in bacterial biofilms, contributing to biofilm stability. We show that AbpA forms very stable filaments in a manner similar to the donor-strand exchange of bacterial TasA fibers and chaperone-usher pathway pili where a β-strand from one subunit is incorporated into a β-sheet of the next subunit. Our results reveal likely mechanistic similarities and evolutionary connection between bacterial and archaeal biofilms, and suggest that there could be many other archaeal surface filaments that are as yet uncharacterized.

Beyond the Backbone: The Next Generation of Pathwalking Utilities for Model Building in CryoEM Density Maps

Single-particle electron cryomicroscopy (cryoEM) has become an indispensable tool for studying structure and function in macromolecular assemblies. As an integral part of the cryoEM structure determination process, computational tools have been developed to build atomic models directly from a density map without structural templates. Nearly a decade ago, we created Pathwalking, a tool for de novo modeling of protein structure in near-atomic resolution cryoEM density maps. Here, we present the latest developments in Pathwalking, including the addition of probabilistic models, as well as a companion tool for modeling waters and ligands. This software was evaluated on the 2021 CryoEM Ligand Challenge density maps, in addition to identifying ligands in three IP3R1 density maps at ~3 Å to 4.1 Å resolution. The results clearly demonstrate that the Pathwalking de novo modeling pipeline can construct accurate protein structures and reliably localize and identify ligand density directly from a near-atomic resolution map.

LPTD: a novel linear programming-based topology determination method for cryo-EM maps

SUMMARY

Topology determination is one of the most important intermediate steps toward building the atomic structure of proteins from their medium-resolution cryo-electron microscopy (cryo-EM) map. The main goal in the topology determination is to identify correct matches (i.e. assignment and direction) between secondary structure elements (SSEs) (α-helices and β-sheets) detected in a protein sequence and cryo-EM density map. Despite many recent advances in molecular biology technologies, the problem remains a challenging issue. To overcome the problem, this article proposes a linear programming-based topology determination (LPTD) method to solve the secondary structure topology problem in three-dimensional geometrical space. Through modeling of the protein's sequence with the aid of extracting highly reliable features and a distance-based scoring function, the secondary structure matching problem is transformed into a complete weighted bipartite graph matching problem. Subsequently, an algorithm based on linear programming is developed as a decision-making strategy to extract the true topology (native topology) between all possible topologies. The proposed automatic framework is verified using 12 experimental and 15 simulated α-β proteins. Results demonstrate that LPTD is highly efficient and extremely fast in such a way that for 77% of cases in the dataset, the native topology has been detected in the first rank topology in <2 s. Besides, this method is able to successfully handle large complex proteins with as many as 65 SSEs. Such a large number of SSEs have never been solved with current tools/methods.

AVAILABILITY AND IMPLEMENTATION

The LPTD package (source code and data) is publicly available at https://github.com/B-Behkamal/LPTD. Moreover, two test samples as well as the instruction of utilizing the graphical user interface have been provided in the shared readme file.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Molecular architecture of the human caveolin-1 complex

Membrane-sculpting proteins shape the morphology of cell membranes and facilitate remodeling in response to physiological and environmental cues. Complexes of the monotopic membrane protein caveolin function as essential curvature-generating components of caveolae, flask-shaped invaginations that sense and respond to plasma membrane tension. However, the structural basis for caveolin's membrane remodeling activity is currently unknown. Here, we show that, using cryo-electron microscopy, the human caveolin-1 complex is composed of 11 protomers organized into a tightly packed disc with a flat membrane-embedded surface. The structural insights suggest a previously unrecognized mechanism for how membrane-sculpting proteins interact with membranes and reveal how key regions of caveolin-1, including its scaffolding, oligomerization, and intramembrane domains, contribute to its function.

Structure and mechanism of NALCN-FAM155A-UNC79-UNC80 channel complex

NALCN channel mediates sodium leak currents and is important for maintaining proper resting membrane potential. NALCN and FAM155A form the core complex of the channel, the activity of which essentially depends on the presence of both UNC79 and UNC80, two auxiliary proteins. NALCN, FAM155A, UNC79, and UNC80 co-assemble into a large hetero-tetrameric channel complex. Genetic mutations of NALCN channel components lead to neurodevelopmental diseases. However, the structure and mechanism of the intact channel complex remain elusive. Here, we present the cryo-EM structure of the mammalian NALCN-FAM155A-UNC79-UNC80 quaternary complex. The structure shows that UNC79-UNC80 form a large piler-shaped heterodimer which was tethered to the intracellular side of the NALCN channel through tripartite interactions with the cytoplasmic loops of NALCN. Two interactions are essential for proper cell surface localization of NALCN. The other interaction relieves the self-inhibition of NALCN by pulling the auto-inhibitory CTD Interacting Helix (CIH) out of its binding site. Our work defines the structural mechanism of NALCN modulation by UNC79 and UNC80.

Ciliary central apparatus structure reveals mechanisms of microtubule patterning

A pair of extensively modified microtubules form the central apparatus (CA) of the axoneme of most motile cilia, where they regulate ciliary motility. The external surfaces of both CA microtubules are patterned asymmetrically with large protein complexes that repeat every 16 or 32 nm. The composition of these projections and the mechanisms that establish asymmetry and longitudinal periodicity are unknown. Here, by determining cryo-EM structures of the CA microtubules, we identify 48 different CA-associated proteins, which in turn reveal mechanisms for asymmetric and periodic protein binding to microtubules. We identify arc-MIPs, a novel class of microtubule inner protein, that bind laterally across protofilaments and remodel tubulin structure and lattice contacts. The binding mechanisms utilized by CA proteins may be generalizable to other microtubule-associated proteins. These structures establish a foundation to elucidate the contributions of individual CA proteins to ciliary motility and ciliopathies.