Refinement of protein structures into low-resolution density maps using rosetta

Elucidating structural variability in p53 conformers using combinatorial refinement strategies and molecular dynamics

Low molecular weight proteins and protein assemblies can now be investigated using cryo-electron microscopy (EM) as a complement to traditional structural biology techniques. It is important, however, to not lose sight of the dynamic information inherent in macromolecules that give rise to their exquisite functionality. As computational methods continue to advance the field of biomedical imaging, so must strategies to resolve the minute details of disease-related entities. Here, we employed combinatorial modeling approaches to assess flexible properties among low molecular weight proteins (~100 kDa or less). Through a blend of rigid body refinement and simulated annealing, we determined new hidden conformations for wild type p53 monomer and dimer forms. Structures for both states converged to yield new conformers, each revealing good stereochemistry and dynamic information about the protein. Based on these insights, we identified fluid parts of p53 that complement the stable central core of the protein responsible for engaging DNA. Molecular dynamics simulations corroborated the modeling results and helped pinpoint the more flexible residues in wild type p53. Overall, the new computational methods may be used to shed light on other small protein features in a vast ensemble of structural data that cannot be easily delineated by other algorithms.

CryoTEN: Efficiently Enhancing Cryo-EM Density Maps Using Transformers

Motivation

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.

Results

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.

Availability and implementation

The source code and data is freely available at https://github.com/jianlin-cheng/cryoten.

Capsid structure of bacteriophage ΦKZ provides insights into assembly and stabilization of jumbo phages

Jumbo phages are a group of tailed bacteriophages with large genomes and capsids. As a prototype of jumbo phage, ΦKZ infects Pseudomonas aeruginosa, a multi-drug-resistant (MDR) opportunistic pathogen leading to acute or chronic infection in immunocompromised individuals. It holds potential to be used as an antimicrobial agent and as a model for uncovering basic phage biology. Although previous low-resolution structural studies have indicated that jumbo phages may have more complicated capsid structures than smaller phages such as HK97, the detailed structures and the assembly mechanism of their capsids remain largely unknown. Here, we report a 3.5-Å-resolution cryo-EM structure of the ΦKZ capsid. The structure unveiled ten minor capsid proteins, with some decorating the outer surface of the capsid and the others forming a complex network attached to the capsid's inner surface. This network seems to play roles in driving capsid assembly and capsid stabilization. Similar mechanisms of capsid assembly and stabilization are probably employed by many other jumbo viruses.

Cryo-EM unveils kinesin KIF1A's processivity mechanism and the impact of its pathogenic variant P305L

Mutations in the microtubule-associated motor protein KIF1A lead to severe neurological conditions known as KIF1A-associated neurological disorders (KAND). Despite insights into its molecular mechanism, high-resolution structures of KIF1A-microtubule complexes remain undefined. Here, we present 2.7-3.5 Å resolution structures of dimeric microtubule-bound KIF1A, including the pathogenic P305L mutant, across various nucleotide states. Our structures reveal that KIF1A binds microtubules in one- and two-heads-bound configurations, with both heads exhibiting distinct conformations with tight inter-head connection. Notably, KIF1A's class-specific loop 12 (K-loop) forms electrostatic interactions with the C-terminal tails of both α- and β-tubulin. The P305L mutation does not disrupt these interactions but alters loop-12's conformation, impairing strong microtubule-binding. Structure-function analysis reveals the K-loop and head-head coordination as major determinants of KIF1A's superprocessive motility. Our findings advance the understanding of KIF1A's molecular mechanism and provide a basis for developing structure-guided therapeutics against KAND.

Outcomes of the EMDataResource cryo-EM Ligand Modeling Challenge

The EMDataResource Ligand Model Challenge aimed to assess the reliability and reproducibility of modeling ligands bound to protein and protein-nucleic acid complexes in cryogenic electron microscopy (cryo-EM) maps determined at near-atomic (1.9-2.5 Å) resolution. Three published maps were selected as targets: Escherichia coli beta-galactosidase with inhibitor, SARS-CoV-2 virus RNA-dependent RNA polymerase with covalently bound nucleotide analog and SARS-CoV-2 virus ion channel ORF3a with bound lipid. Sixty-one models were submitted from 17 independent research groups, each with supporting workflow details. The quality of submitted ligand models and surrounding atoms were analyzed by visual inspection and quantification of local map quality, model-to-map fit, geometry, energetics and contact scores. A composite rather than a single score was needed to assess macromolecule+ligand model quality. These observations lead us to recommend best practices for assessing cryo-EM structures of liganded macromolecules reported at near-atomic resolution.

Structure and topography of the synaptic V-ATPase-synaptophysin complex

Synaptic vesicles are organelles with a precisely defined protein and lipid composition1,2, yet the molecular mechanisms for the biogenesis of synaptic vesicles are mainly unknown. Here we discovered a well-defined interface between the synaptic vesicle V-ATPase and synaptophysin by in situ cryo-electron tomography and single-particle cryo-electron microscopy of functional synaptic vesicles isolated from mouse brains3. The synaptic vesicle V-ATPase is an ATP-dependent proton pump that establishes the proton gradient across the synaptic vesicle, which in turn drives the uptake of neurotransmitters4,5. Synaptophysin6 and its paralogues synaptoporin7 and synaptogyrin8 belong to a family of abundant synaptic vesicle proteins whose function is still unclear. We performed structural and functional studies of synaptophysin-knockout mice, confirming the identity of synaptophysin as an interaction partner with the V-ATPase. Although there is little change in the conformation of the V-ATPase upon interaction with synaptophysin, the presence of synaptophysin in synaptic vesicles profoundly affects the copy number of V-ATPases. This effect on the topography of synaptic vesicles suggests that synaptophysin assists in their biogenesis. In support of this model, we observed that synaptophysin-knockout mice exhibit severe seizure susceptibility, suggesting an imbalance of neurotransmitter release as a physiological consequence of the absence of synaptophysin.

State-of-the-RNArt: benchmarking current methods for RNA 3D structure prediction

RNAs are essential molecules involved in numerous biological functions. Understanding RNA functions requires the knowledge of their 3D structures. Computational methods have been developed for over two decades to predict the 3D conformations from RNA sequences. These computational methods have been widely used and are usually categorised as either ab initio or template-based. The performances remain to be improved. Recently, the rise of deep learning has changed the sight of novel approaches. Deep learning methods are promising, but their adaptation to RNA 3D structure prediction remains difficult. In this paper, we give a brief review of the ab initio, template-based and novel deep learning approaches. We highlight the different available tools and provide a benchmark on nine methods using the RNA-Puzzles dataset. We provide an online dashboard that shows the predictions made by benchmarked methods, freely available on the EvryRNA platform: https://evryrna.ibisc.univ-evry.fr/evryrna/state_of_the_rnart/.

Growing Glycans in Rosetta: Accurate de novo glycan modeling, density fitting, and rational sequon design

Carbohydrates and glycoproteins modulate key biological functions. However, experimental structure determination of sugar polymers is notoriously difficult. Computational approaches can aid in carbohydrate structure prediction, structure determination, and design. In this work, we developed a glycan-modeling algorithm, GlycanTreeModeler, that computationally builds glycans layer-by-layer, using adaptive kernel density estimates (KDE) of common glycan conformations derived from data in the Protein Data Bank (PDB) and from quantum mechanics (QM) calculations. GlycanTreeModeler was benchmarked on a test set of glycan structures of varying lengths, or "trees". Structures predicted by GlycanTreeModeler agreed with native structures at high accuracy for both de novo modeling and experimental density-guided building. We employed these tools to design de novo glycan trees into a protein nanoparticle vaccine to shield regions of the scaffold from antibody recognition, and experimentally verified shielding. This work will inform glycoprotein model prediction, glycan masking, and further aid computational methods in experimental structure determination and refinement.

Predictive modeling and cryo-EM: A synergistic approach to modeling macromolecular structure

Over the last 15 years, structural biology has seen unprecedented development and improvement in two areas: electron cryo-microscopy (cryo-EM) and predictive modeling. Once relegated to low resolutions, single-particle cryo-EM is now capable of achieving near-atomic resolutions of a wide variety of macromolecular complexes. Ushered in by AlphaFold, machine learning has powered the current generation of predictive modeling tools, which can accurately and reliably predict models for proteins and some complexes directly from the sequence alone. Although they offer new opportunities individually, there is an inherent synergy between these techniques, allowing for the construction of large, complex macromolecular models. Here, we give a brief overview of these approaches in addition to illustrating works that combine these techniques for model building. These examples provide insight into model building, assessment, and limitations when integrating predictive modeling with cryo-EM density maps. Together, these approaches offer the potential to greatly accelerate the generation of macromolecular structural insights, particularly when coupled with experimental data.

Outcomes of the EMDataResource Cryo-EM Ligand Modeling Challenge

The EMDataResource Ligand Model Challenge aimed to assess the reliability and reproducibility of modeling ligands bound to protein and protein/nucleic-acid complexes in cryogenic electron microscopy (cryo-EM) maps determined at near-atomic (1.9-2.5 Å) resolution. Three published maps were selected as targets: E. coli beta-galactosidase with inhibitor, SARS-CoV-2 RNA-dependent RNA polymerase with covalently bound nucleotide analog, and SARS-CoV-2 ion channel ORF3a with bound lipid. Sixty-one models were submitted from 17 independent research groups, each with supporting workflow details. We found that (1) the quality of submitted ligand models and surrounding atoms varied, as judged by visual inspection and quantification of local map quality, model-to-map fit, geometry, energetics, and contact scores, and (2) a composite rather than a single score was needed to assess macromolecule+ligand model quality. These observations lead us to recommend best practices for assessing cryo-EM structures of liganded macromolecules reported at near-atomic resolution.

Observation of Bacteriophage Ultrastructure by Cryo-Electron Microscopy

Transmission electron microscopy (TEM) is an ideal method to observe and determine the structure of bacteriophages. From early studies by negative staining to the present atomic structure models derived from cryo-TEM, bacteriophage detection, classification, and structure determination have been mostly done by electron microscopy. Although embedding in metal salts has been a routine method for virus observation for many years, the preservation of bacteriophages in a thin layer of fast frozen buffer has proven to be the most convenient preparation method for obtaining images using cryo-electron microscopy (cryo-EM). In this technique, frozen samples are observed at liquid nitrogen temperature, and the images are acquired using different recording media. The incorporation of direct electron detectors has been a fundamental step in achieving atomic resolution images of a number of viruses. These projection images can be numerically combined using different approaches to render a three-dimensional model of the virus. For those viral components exhibiting any symmetry, averaging can nowadays achieve atomic structures in most cases. Image processing methods have also evolved to improve the resolution in asymmetric viral components or regions showing different types of symmetries (symmetry mismatch).

Cryo-EM Unveils the Processivity Mechanism of Kinesin KIF1A and the Impact of its Pathogenic Variant P305L

Mutations in the microtubule-associated motor protein KIF1A lead to severe neurological conditions known as KIF1A-associated neurological disorders (KAND). Despite insights into its molecular mechanism, high-resolution structures of KIF1A-microtubule complexes remain undefined. Here, we present 2.7-3.4 Å resolution structures of dimeric microtubule-bound KIF1A, including the pathogenic P305L mutant, across various nucleotide states. Our structures reveal that KIF1A binds microtubules in one- and two-heads-bound configurations, with both heads exhibiting distinct conformations with tight inter-head connection. Notably, KIF1A's class-specific loop 12 (K-loop) forms electrostatic interactions with the C-terminal tails of both α- and β-tubulin. The P305L mutation does not disrupt these interactions but alters loop-12's conformation, impairing strong microtubule-binding. Structure-function analysis reveals the K-loop and head-head coordination as major determinants of KIF1A's superprocessive motility. Our findings advance the understanding of KIF1A's molecular mechanism and provide a basis for developing structure-guided therapeutics against KAND.

Structure and dynamics of differential ligand binding in the human ρ-type GABAA receptor

The neurotransmitter γ-aminobutyric acid (GABA) drives critical inhibitory processes in and beyond the nervous system, partly via ionotropic type-A receptors (GABAARs). Pharmacological properties of ρ-type GABAARs are particularly distinctive, yet the structural basis for their specialization remains unclear. Here, we present cryo-EM structures of a lipid-embedded human ρ1 GABAAR, including a partial intracellular domain, under apo, inhibited, and desensitized conditions. An apparent resting state, determined first in the absence of modulators, was recapitulated with the specific inhibitor (1,2,5,6-tetrahydropyridin-4-yl)methylphosphinic acid and blocker picrotoxin and provided a rationale for bicuculline insensitivity. Comparative structures, mutant recordings, and molecular simulations with and without GABA further explained the sensitized but slower activation of ρ1 relative to canonical subtypes. Combining GABA with picrotoxin also captured an apparent uncoupled intermediate state. This work reveals structural mechanisms of gating and modulation with applications to ρ-specific pharmaceutical design and to our biophysical understanding of ligand-gated ion channels.

Delineating Conformational Variability in Small Protein Structures Using Combinatorial Refinement Strategies

As small protein assemblies and even small proteins are becoming more amenable to cryo-Electron Microscopy (EM) structural studies, it is important to consider the complementary dynamic information present in the data. Current computational strategies are limited in their ability to resolve minute differences among low molecular weight entities. Here, we demonstrate a new combinatorial approach to delineate flexible conformations among small proteins using real-space refinement applications. We performed a meta-analysis of structural data for the SARS CoV-2 Nucleocapsid (N) protein using a combination of rigid-body refinement and simulated annealing methods. For the N protein monomer, we determined three new flexible conformers with good stereochemistry and quantitative comparisons provided new evidence of their dynamic properties. A similar analysis performed for the N protein dimer showed only minor structural differences among the flexible models. These results suggested a more stable view of the N protein dimer than the monomer structure. Taken together, the new computational strategies can delineate conformational changes in low molecular weight proteins that may go unnoticed by conventional assessments. The results also suggest that small proteins may be further stabilized for structural studies through the use of solution components that limit the movement of external flexible regions.

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.

Residue-level error detection in cryoelectron microscopy models

Building accurate protein models into moderate resolution (3-5 Å) cryoelectron microscopy (cryo-EM) maps is challenging and error prone. We have developed MEDIC (Model Error Detection in Cryo-EM), a robust statistical model that identifies local backbone errors in protein structures built into cryo-EM maps by combining local fit-to-density with deep-learning-derived structural information. MEDIC is validated on a set of 28 structures that were subsequently solved to higher resolutions, where we identify the differences between low- and high-resolution structures with 68% precision and 60% recall. We additionally use this model to fix over 100 errors in 12 deposited structures and to identify errors in 4 refined AlphaFold predictions with 80% precision and 60% recall. As modelers more frequently use deep learning predictions as a starting point for refinement and rebuilding, MEDIC's ability to handle errors in structures derived from hand-building and machine learning methods makes it a powerful tool for structural biologists.

End-to-end protein-ligand complex structure generation with diffusion-based generative models

BACKGROUND

Three-dimensional structures of protein-ligand complexes provide valuable insights into their interactions and are crucial for molecular biological studies and drug design. However, their high-dimensional and multimodal nature hinders end-to-end modeling, and earlier approaches depend inherently on existing protein structures. To overcome these limitations and expand the range of complexes that can be accurately modeled, it is necessary to develop efficient end-to-end methods.

RESULTS

We introduce an equivariant diffusion-based generative model that learns the joint distribution of ligand and protein conformations conditioned on the molecular graph of a ligand and the sequence representation of a protein extracted from a pre-trained protein language model. Benchmark results show that this protein structure-free model is capable of generating diverse structures of protein-ligand complexes, including those with correct binding poses. Further analyses indicate that the proposed end-to-end approach is particularly effective when the ligand-bound protein structure is not available.

CONCLUSION

The present results demonstrate the effectiveness and generative capability of our end-to-end complex structure modeling framework with diffusion-based generative models. We suppose that this framework will lead to better modeling of protein-ligand complexes, and we expect further improvements and wide applications.

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.

C-N bond formation by a polyketide synthase

Assembly-line polyketide synthases (PKSs) are molecular factories that produce diverse metabolites with wide-ranging biological activities. PKSs usually work by constructing and modifying the polyketide backbone successively. Here, we present the cryo-EM structure of CalA3, a chain release PKS module without an ACP domain, and its structures with amidation or hydrolysis products. The domain organization reveals a unique "∞"-shaped dimeric architecture with five connected domains. The catalytic region tightly contacts the structural region, resulting in two stabilized chambers with nearly perfect symmetry while the N-terminal docking domain is flexible. The structures of the ketosynthase (KS) domain illustrate how the conserved key residues that canonically catalyze C-C bond formation can be tweaked to mediate C-N bond formation, revealing the engineering adaptability of assembly-line polyketide synthases for the production of novel pharmaceutical agents.

Insights into autoregulation of a membrane protein complex by its cytoplasmic domains

Membrane transporters mediate the passage of molecules across membranes and are essential for cellular function. While the transmembrane region of these proteins is responsible for substrate transport, often the cytoplasmic regions are required for modulating their activity. However, it can be difficult to obtain atomic-resolution descriptions of these autoregulatory domains by classical structural biology techniques, especially if they lack a single, defined structure. The betaine permease, BetP, a homotrimer, is a prominent and well-studied example of a membrane protein whose autoregulation depends on cytoplasmic N- and C-terminal segments. These domains sense and transduce changes in K+ concentration and in lipid bilayer properties caused by osmotic stress. However, structural data for these terminal domains is incomplete, which hinders a clear description of the molecular mechanism of autoregulation. Here we used microsecond-scale molecular simulations of the BetP trimer to compare reported conformations of the 45-amino-acid long C-terminal tails. The simulations provide support for the idea that the conformation derived from electron microscopy (EM) data represents a more stable global orientation of the C-terminal segment under downregulating conditions while also providing a detailed molecular description of its dynamics and highlighting specific interactions with lipids, ions, and neighboring transporter subunits. A missing piece of the molecular puzzle is the N-terminal segment, whose dynamic nature has prevented structural characterization. Using Rosetta to generate ensembles of de novo conformations in the context of the EM-derived structure robustly identifies two features of the N-terminal tail, namely 1) short helical elements and 2) an orientation that would confine potential interactions to the protomer in the counterclockwise direction (viewed from the cytoplasm). Since each C-terminal tail only contacts the protomer in the clockwise direction, these results indicate an intricate interplay between the three protomers of BetP in the downregulated protein and a multidirectionality that may facilitate autoregulation of transport.

Assembly and Capsid Expansion Mechanism of Bacteriophage P22 Revealed by High-Resolution Cryo-EM Structures

The formation of many double-stranded DNA viruses, such as herpesviruses and bacteriophages, begins with the scaffolding-protein-mediated assembly of the procapsid. Subsequently, the procapsid undergoes extensive structural rearrangement and expansion to become the mature capsid. Bacteriophage P22 is an established model system used to study virus maturation. Here, we report the cryo-electron microscopy structures of procapsid, empty procapsid, empty mature capsid, and mature capsid of phage P22 at resolutions of 2.6 Å, 3.9 Å, 2.8 Å, and 3.0 Å, respectively. The structure of the procapsid allowed us to build an accurate model of the coat protein gp5 and the C-terminal region of the scaffolding protein gp8. In addition, interactions among the gp5 subunits responsible for procapsid assembly and stabilization were identified. Two C-terminal α-helices of gp8 were observed to interact with the coat protein in the procapsid. The amino acid interactions between gp5 and gp8 in the procapsid were consistent with the results of previous biochemical studies involving mutant proteins. Our structures reveal hydrogen bonds and salt bridges between the gp5 subunits in the procapsid and the conformational changes of the gp5 domains involved in the closure of the local sixfold opening and a thinner capsid shell during capsid maturation.

StarMap: a user-friendly workflow for Rosetta-driven molecular structure refinement

Cryogenic electron microscopy (cryo-EM) data represent density maps of macromolecular systems at atomic or near-atomic resolution. However, building and refining 3D atomic models by using data from cryo-EM maps is not straightforward and requires significant hands-on experience and manual intervention. We recently developed StarMap, an easy-to-use interface between the popular structural display program ChimeraX and Rosetta, a powerful molecular modeling engine. StarMap offers a general approach for refining structural models of biological macromolecules into cryo-EM density maps by combining Monte Carlo sampling with local density-guided optimization, Rosetta-based all-atom refinement and real-space B-factor calculations in a straightforward workflow. StarMap includes options for structural symmetry, local refinements and independent model validation. The overall quality of the refinement and the structure resolution is then assessed via analytical outputs, such as magnification calibration (pixel size calibration) and Fourier shell correlations. Z-scores reported by StarMap provide an easily interpretable indicator of the goodness of fit for each residue and can be plotted to evaluate structural models and improve local residue refinements, as well as to identify flexible regions and potentially functional sites in large macromolecular complexes. The protocol requires general computer skills, without the need for coding expertise, because most parts of the workflow can be operated by clicking tabs within the ChimeraX graphical user interface. Time requirements for the model refinement depend on the size and quality of the input data; however, this step can typically be completed within 1 d. The analytical parts of the workflow are completed within minutes.

Improved interface packing and design opportunities revealed by CryoEM analysis of a designed protein nanocage

Symmetric protein assemblies play important roles in nature which makes them an attractive target for engineering. De novo symmetric protein complexes can be created through computational protein design to tailor their properties from first principles, and recently several protein nanocages have been created by bringing together protein components through hydrophobic interactions. Accurate experimental structures of newly-developed proteins are essential to validate their design, improve assembly stability, and tailor downstream applications. We describe the CryoEM structure of the nanocage I3-01, at an overall resolution of 3.5 Å. I3-01, comprising 60 aldolase subunits arranged with icosahedral symmetry, has resisted high-resolution characterization. Some key differences between the refined structure and the original design are identified, such as improved packing of hydrophobic sidechains, providing insight to the resistance of I3-01 to high-resolution averaging. Based on our analysis, we suggest factors important in the design and structural processing of new assemblies.

Complementary antibody lineages achieve neutralization breadth in an HIV-1 infected elite neutralizer

Broadly neutralizing antibodies (bNAbs) have remarkable breadth and potency against most HIV-1 subtypes and are able to prevent HIV-1 infection in animal models. However, bNAbs are extremely difficult to induce by vaccination. Defining the developmental pathways towards neutralization breadth can assist in the design of strategies to elicit protective bNAb responses by vaccination. Here, HIV-1 envelope glycoproteins (Env)-specific IgG+ B cells were isolated at various time points post infection from an HIV-1 infected elite neutralizer to obtain monoclonal antibodies (mAbs). Multiple antibody lineages were isolated targeting distinct epitopes on Env, including the gp120-gp41 interface, CD4-binding site, silent face and V3 region. The mAbs each neutralized a diverse set of HIV-1 strains from different clades indicating that the patient's remarkable serum breadth and potency might have been the result of a polyclonal mixture rather than a single bNAb lineage. High-resolution cryo-electron microscopy structures of the neutralizing mAbs (NAbs) in complex with an Env trimer generated from the same individual revealed that the NAbs used multiple strategies to neutralize the virus; blocking the receptor binding site, binding to HIV-1 Env N-linked glycans, and disassembly of the trimer. These results show that diverse NAbs can complement each other to achieve a broad and potent neutralizing serum response in HIV-1 infected individuals. Hence, the induction of combinations of moderately broad NAbs might be a viable vaccine strategy to protect against a wide range of circulating HIV-1 viruses.

Near-atomic, non-icosahedrally averaged structure of giant virus Paramecium bursaria chlorella virus 1

Giant viruses are a large group of viruses that infect many eukaryotes. Although components that do not obey the overall icosahedral symmetry of their capsids have been observed and found to play critical roles in the viral life cycles, identities and high-resolution structures of these components remain unknown. Here, by determining a near-atomic-resolution, five-fold averaged structure of Paramecium bursaria chlorella virus 1, we unexpectedly found the viral capsid possesses up to five major capsid protein variants and a penton protein variant. These variants create varied capsid microenvironments for the associations of fibers, a vesicle, and previously unresolved minor capsid proteins. Our structure reveals the identities and atomic models of the capsid components that do not obey the overall icosahedral symmetry and leads to a model for how these components are assembled and initiate capsid assembly, and this model might be applicable to many other giant viruses.

Identification of IOMA-class neutralizing antibodies targeting the CD4-binding site on the HIV-1 envelope glycoprotein

A major goal of current HIV-1 vaccine design efforts is to induce broadly neutralizing antibodies (bNAbs). The VH1-2-derived bNAb IOMA directed to the CD4-binding site of the HIV-1 envelope glycoprotein is of interest because, unlike the better-known VH1-2-derived VRC01-class bNAbs, it does not require a rare short light chain complementarity-determining region 3 (CDRL3). Here, we describe three IOMA-class NAbs, ACS101-103, with up to 37% breadth, that share many characteristics with IOMA, including an average-length CDRL3. Cryo-electron microscopy revealed that ACS101 shares interactions with those observed with other VH1-2 and VH1-46-class bNAbs, but exhibits a unique binding mode to residues in loop D. Analysis of longitudinal sequences from the patient suggests that a transmitter/founder-virus lacking the N276 glycan might have initiated the development of these NAbs. Together these data strengthen the rationale for germline-targeting vaccination strategies to induce IOMA-class bNAbs and provide a wealth of sequence and structural information to support such strategies.

Kinesin-8-specific loop-2 controls the dual activities of the motor domain according to tubulin protofilament shape

Kinesin-8s are dual-activity motor proteins that can move processively on microtubules and depolymerize microtubule plus-ends, but their mechanism of combining these distinct activities remains unclear. We addressed this by obtaining cryo-EM structures (2.6-3.9 Å) of Candida albicans Kip3 in different catalytic states on the microtubule lattice and on a curved microtubule end mimic. We also determined a crystal structure of microtubule-unbound CaKip3-ADP (2.0 Å) and analyzed the biochemical activity of CaKip3 and kinesin-1 mutants. These data reveal that the microtubule depolymerization activity of kinesin-8 originates from conformational changes of its motor core that are amplified by dynamic contacts between its extended loop-2 and tubulin. On curved microtubule ends, loop-1 inserts into preceding motor domains, forming head-to-tail arrays of kinesin-8s that complement loop-2 contacts with curved tubulin and assist depolymerization. On straight tubulin protofilaments in the microtubule lattice, loop-2-tubulin contacts inhibit conformational changes in the motor core, but in the ADP-Pi state these contacts are relaxed, allowing neck-linker docking for motility. We propose that these tubulin shape-induced alternations between pro-microtubule-depolymerization and pro-motility kinesin states, regulated by loop-2, are the key to the dual activity of kinesin-8 motors.

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.

AI-Based Protein Structure Prediction in Drug Discovery: Impacts and Challenges

Proteins are the molecular machinery of the human body, and their malfunctioning is often responsible for diseases, making them crucial targets for drug discovery. The three-dimensional structure of a protein determines its biological function, its conformational state determines substrates, cofactors, and protein binding. Rational drug discovery employs engineered small molecules to selectively interact with proteins to modulate their function. To selectively target a protein and to design small molecules, knowing the protein structure with all its specific conformation is critical. Unfortunately, for a large number of proteins relevant for drug discovery, the three-dimensional structure has not yet been experimentally solved. Therefore, accurately predicting their structure based on their amino acid sequence is one of the grant challenges in biology. Recently, AlphaFold2, a machine learning application based on a deep neural network, was able to predict unknown structures of proteins with an unprecedented accuracy. Despite the impressive progress made by AlphaFold2, nature still challenges the field of structure prediction. In this Perspective, we explore how AlphaFold2 and related methods help make drug design more efficient. Furthermore, we discuss the roles of predicting domain-domain orientations, all relevant conformational states, the influence of posttranslational modifications, and conformational changes due to protein binding partners. We highlight where further improvements are needed for advanced machine learning methods to be successfully and frequently used in the pharmaceutical industry.

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.

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.

Progressive assembly of multi-domain protein structures from cryo-EM density maps

Progress in cryo-electron microscopy has provided the potential for large-size protein structure determination. However, the success rate for solving multi-domain proteins remains low because of the difficulty in modelling inter-domain orientations. Here we developed domain enhanced modeling using cryo-electron microscopy (DEMO-EM), an automatic method to assemble multi-domain structures from cryo-electron microscopy maps through a progressive structural refinement procedure combining rigid-body domain fitting and flexible assembly simulations with deep-neural-network inter-domain distance profiles. The method was tested on a large-scale benchmark set of proteins containing up to 12 continuous and discontinuous domains with medium- to low-resolution density maps, where DEMO-EM produced models with correct inter-domain orientations (template modeling score (TM-score) >0.5) for 97% of cases and outperformed state-of-the-art methods. DEMO-EM was applied to the severe acute respiratory syndrome coronavirus 2 genome and generated models with average TM-score and root-mean-square deviation of 0.97 and 1.3 Å, respectively, with respect to the deposited structures. These results demonstrate an efficient pipeline that enables automated and reliable large-scale multi-domain protein structure modelling from cryo-electron microscopy maps.

Accurate flexible refinement for atomic-level protein structure using cryo-EM density maps and deep learning

With the rapid progress of deep learning in cryo-electron microscopy and protein structure prediction, improving the accuracy of the protein structure model by using a density map and predicted contact/distance map through deep learning has become an urgent need for robust methods. Thus, designing an effective protein structure optimization strategy based on the density map and predicted contact/distance map is critical to improving the accuracy of structure refinement. In this article, a protein structure optimization method based on the density map and predicted contact/distance map by deep-learning technology was proposed in accordance with the result of matching between the density map and the initial model. Physics- and knowledge-based energy functions, integrated with Cryo-EM density map data and deep-learning data, were used to optimize the protein structure in the simulation. The dynamic confidence score was introduced to the iterative process for choosing whether it is a density map or a contact/distance map to dominate the movement in the simulation to improve the accuracy of refinement. The protocol was tested on a large set of 224 non-homologous membrane proteins and generated 214 structural models with correct folds, where 4.5% of structural models were generated from structural models with incorrect folds. Compared with other state-of-the-art methods, the major advantage of the proposed methods lies in the skills for using density map and contact/distance map in the simulation, as well as the new energy function in the re-assembly simulations. Overall, the results demonstrated that this strategy is a valuable approach and ready to use for atomic-level structure refinement using cryo-EM density map and predicted contact/distance map.

Catalytic trajectory of a dimeric nonribosomal peptide synthetase subunit with an inserted epimerase domain

Nonribosomal peptide synthetases (NRPSs) are modular assembly-line megaenzymes that synthesize diverse metabolites with wide-ranging biological activities. The structural dynamics of synthetic elongation has remained unclear. Here, we present cryo-EM structures of PchE, an NRPS elongation module, in distinct conformations. The domain organization reveals a unique “H”-shaped head-to-tail dimeric architecture. The capture of both aryl and peptidyl carrier protein-tethered substrates and intermediates inside the heterocyclization domain and l-cysteinyl adenylate in the adenylation domain illustrates the catalytic and recognition residues. The multilevel structural transitions guided by the adenylation C-terminal subdomain in combination with the inserted epimerase and the conformational changes of the heterocyclization tunnel are controlled by two residues. Moreover, we visualized the direct structural dynamics of the full catalytic cycle from thiolation to epimerization. This study establishes the catalytic trajectory of PchE and sheds light on the rational re-engineering of domain-inserted dimeric NRPSs for the production of novel pharmaceutical agents.

The catalytic domains in nonribosomal peptide synthetases (NRPSs) are responsible for a choreography of events that elongates substrates into natural products. Here, the authors present cryo-EM structures of a siderophore-producing dimeric NRPS elongation module in multiple distinct conformations, which provides insight into the mechanisms of catalytic trajectory.

Structural and functional insight into regulation of kinesin-1 by microtubule-associated protein MAP7

Microtubule (MT)-associated protein 7 (MAP7) is a required cofactor for kinesin-1-driven transport of intracellular cargoes. Using cryo-electron microscopy and single-molecule imaging, we investigated how MAP7 binds MTs and facilitates kinesin-1 motility. The MT-binding domain (MTBD) of MAP7 bound MTs as an extended α helix between the protofilament ridge and the site of lateral contact. Unexpectedly, the MTBD partially overlapped with the binding site of kinesin-1 and inhibited its motility. However, by tethering kinesin-1 to the MT, the projection domain of MAP7 prevented dissociation of the motor and facilitated its binding to available neighboring sites. The inhibitory effect of the MTBD dominated as MTs became saturated with MAP7. Our results reveal biphasic regulation of kinesin-1 by MAP7 in the context of their competitive binding to MTs.

Structure of the human SAGA coactivator complex

The SAGA complex is a regulatory hub involved in gene regulation, chromatin modification, DNA damage repair and signaling. While structures of yeast SAGA (ySAGA) have been reported, there are noteworthy functional and compositional differences for this complex in metazoans. Here we present the cryogenic-electron microscopy (cryo-EM) structure of human SAGA (hSAGA) and show how the arrangement of distinct structural elements results in a globally divergent organization from that of yeast, with a different interface tethering the core module to the TRRAP subunit, resulting in a dramatically altered geometry of functional elements and with the integration of a metazoan-specific splicing module. Our hSAGA structure reveals the presence of an inositol hexakisphosphate (InsP6) binding site in TRRAP and an unusual property of its pseudo-(Ψ)PIKK. Finally, we map human disease mutations, thus providing the needed framework for structure-guided drug design of this important therapeutic target for human developmental diseases and cancer.

Integrative structural modeling of macromolecular complexes using Assembline

Integrative modeling enables structure determination of macromolecular complexes by combining data from multiple experimental sources such as X-ray crystallography, electron microscopy or cross-linking mass spectrometry. It is particularly useful for complexes not amenable to high-resolution electron microscopy-complexes that are flexible, heterogeneous or imaged in cells with cryo-electron tomography. We have recently developed an integrative modeling protocol that allowed us to model multi-megadalton complexes as large as the nuclear pore complex. Here, we describe the Assembline software package, which combines multiple programs and libraries with our own algorithms in a streamlined modeling pipeline. Assembline builds ensembles of models satisfying data from atomic structures or homology models, electron microscopy maps and other experimental data, and provides tools for their analysis. Compared with other methods, Assembline enables efficient sampling of conformational space through a multistep procedure, provides new modeling restraints and includes a unique configuration system for setting up the modeling project. Our protocol achieves exhaustive sampling in less than 100-1,000 CPU-hours even for complexes in the megadalton range. For larger complexes, resources available in institutional or public computer clusters are needed and sufficient to run the protocol. We also provide step-by-step instructions for preparing the input, running the core modeling steps and assessing modeling performance at any stage.

Structural basis of glycan276-dependent recognition by HIV-1 broadly neutralizing antibodies

Recognition of N-linked glycan at residue N276 (glycan276) at the periphery of the CD4-binding site (CD4bs) on the HIV-envelope trimer is a formidable challenge for many CD4bs-directed antibodies. To understand how this glycan can be recognized, here we isolate two lineages of glycan276-dependent CD4bs antibodies. Antibody CH540-VRC40.01 (named for donor-lineage.clone) neutralizes 81% of a panel of 208 diverse strains, while antibody CH314-VRC33.01 neutralizes 45%. Cryo-electron microscopy (cryo-EM) structures of these two antibodies and 179NC75, a previously identified glycan276-dependent CD4bs antibody, in complex with HIV-envelope trimer reveal substantially different modes of glycan276 recognition. Despite these differences, binding of glycan276-dependent antibodies maintains a glycan276 conformation similar to that observed in the absence of glycan276-binding antibodies. By contrast, glycan276-independent CD4bs antibodies, such as VRC01, displace glycan276 upon binding. These results provide a foundation for understanding antibody recognition of glycan276 and suggest its presence may be crucial for priming immunogens seeking to initiate broad CD4bs recognition.

Structural basis of mechano-chemical coupling by the mitotic kinesin KIF14

KIF14 is a mitotic kinesin whose malfunction is associated with cerebral and renal developmental defects and several cancers. Like other kinesins, KIF14 couples ATP hydrolysis and microtubule binding to the generation of mechanical work, but the coupling mechanism between these processes is still not fully clear. Here we report 20 high-resolution (2.7-3.9 Å) cryo-electron microscopy KIF14-microtubule structures with complementary functional assays. Analysis procedures were implemented to separate coexisting conformations of microtubule-bound monomeric and dimeric KIF14 constructs. The data provide a comprehensive view of the microtubule and nucleotide induced KIF14 conformational changes. It shows that: 1) microtubule binding, the nucleotide species, and the neck-linker domain govern the transition between three major conformations of the motor domain; 2) an undocked neck-linker prevents the nucleotide-binding pocket to fully close and dampens ATP hydrolysis; 3) 13 neck-linker residues are required to assume a stable docked conformation; 4) the neck-linker position controls the hydrolysis rather than the nucleotide binding step; 5) the two motor domains of KIF14 dimers adopt distinct conformations when bound to the microtubule; and 6) the formation of the two-heads-bound-state introduces structural changes in both motor domains of KIF14 dimers. These observations provide the structural basis for a coordinated chemo-mechanical kinesin translocation model.

Protein Structure Prediction from NMR Hydrogen-Deuterium Exchange Data

Amide hydrogen-deuterium exchange (HDX) has long been used to determine regional flexibility and binding sites in proteins; however, the data are too sparse for full structural characterization. Experiments that measure HDX rates, such as HDX-NMR, have far higher throughput compared to structure determination via X-ray crystallography, cryo-EM, or a full suite of NMR experiments. Data from HDX-NMR experiments encode information on the protein structure, making HDX a prime candidate to be supplemented by computational algorithms for protein structure prediction. We have developed a methodology to incorporate HDX-NMR data into ab initio protein structure prediction using the Rosetta software framework to predict structures based on experimental agreement. To demonstrate the efficacy of our algorithm, we examined 38 proteins with HDX-NMR data available, comparing the predicted model with and without the incorporation of HDX data into scoring. The root-mean-square deviation (rmsd, a measure of the average atomic distance between superimposed models) of the predicted model improved by 1.42 Å on average after incorporating the HDX-NMR data into scoring. The average rmsd improvement for the proteins where the selected model rmsd changed after incorporating HDX data was 3.63 Å, including one improvement of more than 11 Å and seven proteins improving by greater than 4 Å, with 12/15 proteins improving overall. Additionally, for independent verification, two proteins that were not part of the original benchmark were scored including HDX data, with a dramatic improvement of the selected model rmsd of nearly 9 Å for one of the proteins. Moreover, we have developed a confidence metric allowing us to successfully identify near-native models in the absence of a native structure. Improvement in model selection with a strong confidence measure demonstrates that protein structure prediction with HDX-NMR is a powerful tool which can be performed with minimal additional computational strain and expense.

Structural basis for the biosynthesis of lovastatin

Statins are effective cholesterol-lowering drugs. Lovastatin, one of the precursors of statins, is formed from dihydromonacolin L (DML), which is synthesized by lovastatin nonaketide synthase (LovB), with the assistance of a separate trans-acting enoyl reductase (LovC). A full DML synthesis comprises 8 polyketide synthetic cycles with about 35 steps. The assembling of the LovB-LovC complex, and the structural basis for the iterative and yet permutative functions of the megasynthase have remained a mystery. Here, we present the cryo-EM structures of the LovB-LovC complex at 3.60 Å and the core LovB at 2.91 Å resolution. The domain organization of LovB is an X-shaped face-to-face dimer containing eight connected domains. The binding of LovC laterally to the malonyl-acetyl transferase domain allows the completion of a L-shaped catalytic chamber consisting of six active domains. This architecture and the structural details of the megasynthase provide the basis for the processing of the intermediates by the individual catalytic domains. The detailed architectural model provides structural insights that may enable the re-engineering of the megasynthase for the generation of new statins.

Fitting Low-Resolution Protein Structures into Cryo-EM Density Maps by Multiobjective Optimization of Global and Local Correlations

The rigid-body fitting of predicted structural models into cryo-electron microscopy (cryo-EM) density maps is a necessary procedure for density map-guided protein structure determination and prediction. We proposed a novel multiobjective optimization protocol, MOFIT, which performs a rigid-body density-map fitting based on particle swarm optimization (PSO). MOFIT was tested on a large set of 292 nonhomologous single-domain proteins. Starting from structural models predicted by I-TASSER, MOFIT achieved an average coordinate root-mean-square deviation of 2.46 Å, which was 1.57, 2.79, and 3.95 Å lower than three leading single-objective function-based methods, where the differences were statistically significant with p-values of 1.65 × 10^-6, 6.36 × 10^-8, and 6.44 × 10^-11 calculated using two-tail Student's t tests. Detailed analyses showed that the major advantages of MOFIT lie in the multiobjective protocol and the extensive PSO search simulations guided by the composite objective functions, which integrates complementary correlation coefficients from the global structure, local fragments, and individual residues with the cryo-EM density maps.

ATP synthase hexamer assemblies shape cristae of Toxoplasma mitochondria

Mitochondrial ATP synthase plays a key role in inducing membrane curvature to establish cristae. In Apicomplexa causing diseases such as malaria and toxoplasmosis, an unusual cristae morphology has been observed, but its structural basis is unknown. Here, we report that the apicomplexan ATP synthase assembles into cyclic hexamers, essential to shape their distinct cristae. Cryo-EM was used to determine the structure of the hexamer, which is held together by interactions between parasite-specific subunits in the lumenal region. Overall, we identified 17 apicomplexan-specific subunits, and a minimal and nuclear-encoded subunit-a. The hexamer consists of three dimers with an extensive dimer interface that includes bound cardiolipins and the inhibitor IF1. Cryo-ET and subtomogram averaging revealed that hexamers arrange into ~20-megadalton pentagonal pyramids in the curved apical membrane regions. Knockout of the linker protein ATPTG11 resulted in the loss of pentagonal pyramids with concomitant aberrantly shaped cristae. Together, this demonstrates that the unique macromolecular arrangement is critical for the maintenance of cristae morphology in Apicomplexa.

Suppressor Mutations in Type II Secretion Mutants of Vibrio cholerae: Inactivation of the VesC Protease

Genome-wide transposon mutagenesis has identified the genes encoding the T2SS in Vibrio cholerae as essential for viability, but the reason for this is unclear. Mutants with deletions or insertions in these genes can be isolated, suggesting that they have acquired secondary mutations that suppress their growth defect.

The type II secretion system (T2SS) is a conserved transport pathway responsible for the secretion of a range of virulence factors by many pathogens, including Vibrio cholerae. Disruption of the T2SS genes in V. cholerae results in loss of secretion, changes in cell envelope function, and growth defects. While T2SS mutants are viable, high-throughput genomic analyses have listed these genes among essential genes. To investigate whether secondary mutations arise as a consequence of T2SS inactivation, we sequenced the genomes of six V. cholerae T2SS mutants with deletions or insertions in either the epsG, epsL, or epsM genes and identified secondary mutations in all mutants. Two of the six T2SS mutants contain distinct mutations in the gene encoding the T2SS-secreted protease VesC. Other mutations were found in genes coding for V. cholerae cell envelope proteins. Subsequent sequence analysis of the vesC gene in 92 additional T2SS mutant isolates identified another 19 unique mutations including insertions or deletions, sequence duplications, and single-nucleotide changes resulting in amino acid substitutions in the VesC protein. Analysis of VesC variants and the X-ray crystallographic structure of wild-type VesC suggested that all mutations lead to loss of VesC production and/or function. One possible mechanism by which V. cholerae T2SS mutagenesis can be tolerated is through selection of vesC-inactivating mutations, which may, in part, suppress cell envelope damage, establishing permissive conditions for the disruption of the T2SS. Other mutations may have been acquired in genes encoding essential cell envelope proteins to prevent proteolysis by VesC.

IMPORTANCE Genome-wide transposon mutagenesis has identified the genes encoding the T2SS in Vibrio cholerae as essential for viability, but the reason for this is unclear. Mutants with deletions or insertions in these genes can be isolated, suggesting that they have acquired secondary mutations that suppress their growth defect. Through whole-genome sequencing and phenotypic analysis of T2SS mutants, we show that one means by which the growth defect can be suppressed is through mutations in the gene encoding the T2SS substrate VesC. VesC homologues are present in other Vibrio species and close relatives, and this may be why inactivation of the T2SS in species such as Vibrio vulnificus, Vibrio sp. strain 60, and Aeromonas hydrophila also results in a pleiotropic effect on their outer membrane assembly and integrity.

Neutralizing Antibody Responses Induced by HIV-1 Envelope Glycoprotein SOSIP Trimers Derived from Elite Neutralizers

The induction of broadly neutralizing antibodies (bNAbs) is a major goal in vaccine research. HIV-1-infected individuals that develop exceptionally strong bNAb responses, termed elite neutralizers, can inform vaccine design by providing blueprints for the induction of similar bNAb responses. We describe a new recombinant native-like envelope glycoprotein (Env) SOSIP trimer, termed AMC009, based on the viral founder sequences of an elite neutralizer. The subtype B AMC009 SOSIP protein formed stable native-like trimers that displayed multiple bNAb epitopes. Overall, its structure at 4.3-Å resolution was similar to that of BG505 SOSIP.664. The AMC009 trimer resembled one from a second elite neutralizer, AMC011, in having a dense and complete glycan shield. When tested as immunogens in rabbits, the AMC009 trimers did not induce autologous neutralizing antibody (NAb) responses efficiently while the AMC011 trimers did so very weakly, outcomes that may reflect the completeness of their glycan shields. The AMC011 trimer induced antibodies that occasionally cross-neutralized heterologous tier 2 viruses, sometimes at high titer. Cross-neutralizing antibodies were more frequently elicited by a trivalent combination of AMC008, AMC009, and AMC011 trimers, all derived from subtype B viruses. Each of these three individual trimers could deplete the NAb activity from the rabbit sera. Mapping the polyclonal sera by electron microscopy revealed that antibodies of multiple specificities could bind to sites on both autologous and heterologous trimers. These results advance our understanding of how to use Env trimers in multivalent vaccination regimens and the immunogenicity of trimers derived from elite neutralizers.IMPORTANCE Elite neutralizers, i.e., individuals who developed unusually broad and potent neutralizing antibody responses, might serve as blueprints for HIV-1 vaccine design. Here, we studied the immunogenicity of native-like recombinant envelope glycoprotein (Env) trimers based on viral sequences from elite neutralizers. While immunization with single trimers from elite neutralization did not recapitulate the breadth and potency of neutralization observed in these infected individuals, a combination of three subtype B Env trimers from elite neutralizers resulted in some neutralization breadth within subtype B viruses. These results should guide future efforts to design vaccines to induce broadly neutralizing antibodies.

Consensus mutagenesis approach improves the thermal stability of system xc - transporter, xCT, and enables cryo-EM analyses

System x_c⁻ is an amino acid antiporter that imports L‐cystine into cells and exports intracellular L‐glutamate, at a 1:1 ratio. As L‐cystine is an essential precursor for glutathione synthesis, system x_c⁻ supports tumor cell growth through glutathione‐based oxidative stress resistance and is considered as a potential therapeutic target for cancer treatment. System x_c⁻ consists of two subunits, the light chain subunit SLC7A11 (xCT) and the heavy chain subunit SLC3A2 (also known as CD98hc or 4F2hc), which are linked by a conserved disulfide bridge. Although the recent structures of another SLC7 member, L‐type amino acid transporter 1 (LAT1) in complex with CD98hc, have provided the structural basis toward understanding the amino acid transport mechanism, the detailed molecular mechanism of xCT remains unknown. To revealthe molecular mechanism, we performed single‐particle analyses of the xCT‐CD98hc complex. As wild‐type xCT‐CD98hc displayed poor stability and could not be purified to homogeneity, we applied a consensus mutagenesis approach to xCT. The consensus mutated construct exhibited increased stability as compared to the wild‐type, and enabled the cryoelectron microscopy (cryo‐EM) map to be obtained at 6.2 Å resolution by single‐particle analysis. The cryo‐EM map revealed sufficient electron density to assign secondary structures. In the xCT structure, the hash and arm domains are well resolved, whereas the bundle domain shows some flexibility. CD98hc is positioned next to the xCT transmembrane domain. This study provides the structural basis of xCT, and our consensus‐based strategy could represent a good choice toward solving unstable protein structures.

Progressive and accurate assembly of multi-domain protein structures from cryo-EM density maps

Progress in cryo-electron microscopy (cryo-EM) has provided the potential for large-size protein structure determination. However, the solution rate for multi-domain proteins remains low due to the difficulty in modeling inter-domain orientations. We developed DEMO-EM, an automatic method to assemble multi-domain structures from cryo-EM maps through a progressive structural refinement procedure combining rigid-body domain fitting and flexible assembly simulations with deep neural network inter-domain distance profiles. The method was tested on a large-scale benchmark set of proteins containing up to twelve continuous and discontinuous domains with medium-to-low-resolution density maps, where DEMO-EM produced models with correct inter-domain orientations (TM-score >0.5) for 98% of cases and significantly outperformed the state-of-the-art methods. DEMO-EM was applied to SARS-Cov-2 coronavirus genome and generated models with average TM-score/RMSD of 0.97/1.4Å to the deposited structures. These results demonstrated an efficient pipeline that enables automated and reliable large-scale multi-domain protein structure modeling with atomic-level accuracy from cryo-EM maps.

Deep learning enables the atomic structure determination of the Fanconi Anemia core complex from cryoEM

Cryo-electron microscopy of protein complexes often leads to moderate resolution maps (4-8 Å), with visible secondary-structure elements but poorly resolved loops, making model building challenging. In the absence of high-resolution structures of homologues, only coarse-grained structural features are typically inferred from these maps, and it is often impossible to assign specific regions of density to individual protein subunits. This paper describes a new method for overcoming these difficulties that integrates predicted residue distance distributions from a deep-learned convolutional neural network, computational protein folding using Rosetta, and automated EM-map-guided complex assembly. We apply this method to a 4.6 Å resolution cryoEM map of Fanconi Anemia core complex (FAcc), an E3 ubiquitin ligase required for DNA interstrand crosslink repair, which was previously challenging to interpret as it comprises 6557 residues, only 1897 of which are covered by homology models. In the published model built from this map, only 387 residues could be assigned to the specific subunits with confidence. By building and placing into density 42 deep-learning-guided models containing 4795 residues not included in the previously published structure, we are able to determine an almost-complete atomic model of FAcc, in which 5182 of the 6557 residues were placed. The resulting model is consistent with previously published biochemical data, and facilitates interpretation of disease-related mutational data. We anticipate that our approach will be broadly useful for cryoEM structure determination of large complexes containing many subunits for which there are no homologues of known structure.

Structural insight into the Staphylococcus aureus ATP-driven exporter of virulent peptide toxins

Staphylococcus aureus is a major human pathogen that has acquired alarming broad-spectrum antibiotic resistance. One group of secreted toxins with key roles during infection is the phenol-soluble modulins (PSMs). PSMs are amphipathic, membrane-destructive cytolytic peptides that are exported to the host-cell environment by a designated adenosine 5'-triphosphate (ATP)-binding cassette (ABC) transporter, the PSM transporter (PmtABCD). Here, we demonstrate that the minimal Pmt unit necessary for PSM export is PmtCD and provide its first atomic characterization by single-particle cryo-EM and x-ray crystallography. We have captured the transporter in the ATP-bound state at near atomic resolution, revealing a type II ABC exporter fold, with an additional cytosolic domain. Comparison to a lower-resolution nucleotide-free map displaying an "open" conformation and putative hydrophobic inner chamber of a size able to accommodate the binding of two PSM peptides provides mechanistic insight and sets the foundation for therapeutic design.