Journal of Structural Biology

Bacteria microcompartments (BMCs) are pseudo-organelles comprised of a self-assembling, semi-permeable protein shell, most commonly enclosing components of enzymatic pathways. -Carboxysomes (-CBs) are anabolic BMCs known for their role in sequestering Rubisco, the enzyme responsible for carbon fixation in plants, algae and bacteria, along with an upstream enzyme and an assembly protein. Rubisco has low selectivity for its substrate, CO2, and has a slow enzymatic turnover rate, resulting in an inefficient metabolic pathway. Within the -CB, Rubisco has been observed at a range of concentrations and with either a liquid-like assembly or a pseudo-lattice of polymerized fibrils. The biophysical origins of the fibril ultrastructure organization are unclear; however, it is only observed inside -CBs. Quantitative knowledge of the binding constants and energies for assembly and maintenance of these fibrils is critical for understanding this organization and Rubisco regulation, but quantitative methods for in situ analysis of Rubisco polymerization have been lacking. Here, we present an approach to convert tomography-derived -CB volumes and Rubisco particle positions into polymerization binding curves. We used this procedure to determine the Rubisco polymerization constants, including the nucleus size (n) and equilibrium polymerization constant (Kpol). The adopted modeling approach is consistent with in situ constraints, such as concentration-dependent binding interactions and confinement. This approach offers a powerful tool to evaluate both in vitro and potentially in vivo biomolecular interactions, both of Rubisco and of other proteins and polymers suitable for analysis by cryo-electron tomography. Significance StatementCryogenic electron tomography (cryoET) is a powerful method to resolve structures of proteins in their native environment at subnanometer-level resolution. Because tomography data retains spatial relationships of all particles, it intrinsically contains information about component (e.g., protein) binding interactions. Here, we use Rubisco polymerization in -carboxysomes as a model system to demonstrate that quantitative, biochemical binding analysis is possible with cryoET.

Affinity chromatography is a powerful and therefore popular method for the purification of proteins for structural studies. The success of the technique relies on the specificity of the interaction between the target protein and the affinity resin. Here, we present the identification of two protein contaminants isolated from HEK293 cell lysate following affinity purification of twin Strep-tagged or FLAG-tagged proteins. The contaminants were identified as human propionyl-coenzyme A carboxylase (hPCC) and protein arginine methyltransferase 5 in complex with methylosome protein 50 (PRMT5:MEP50) via a combination of cryo-EM data processing and proteomic analyses. This report serves to illustrate how these contaminants may appear in cryo-EM datasets and to highlight the paramount importance of affinity chromatography resin specificity for efficient protein purification.

Calculation of density maps from atomic models is essential for structural studies using crystallography and electron cryo-microscopy (cryoEM). These maps serve various purposes, including atomic model building, refinement, visualization, and validation. However, accurately comparing model-calculated maps to experimental data poses challenges, particularly because the resolution of cryoEM experimental maps varies across the map. Traditional crystallography methods generate finite-resolution maps with uniform resolution throughout the unit cell volume, while most modern software in cryoEM employ Gaussian-like functions to generate these maps, which does not adequately account for atomic model parameters and resolution. Recent work by Urzhumtsev & Lunin (2022, IUCr Journal, 9, 728-734) introduces a novel method for computing atomic model maps that incorporate local resolution and can be expressed as analytically differentiable functions of all atomic parameters. This approach enhances the accuracy of matching atomic models to experimental maps. In this paper, we detail the implementation of this method in CCTBX and Phenix. SynopsisNew tools implemented in CCTBX and Phenix allow the calculation of variable-resolution maps through a sum of atomic images expressed as analytic functions of all atomic parameters, along with their associated local resolution.

Cryo-Focused Ion Beam Scanning Electron Microscopy (cryoFIB-SEM) using samples fixed by high-pressure freezing uniquely enables high resolution cryo-volume Electron Microscope (cvEM) images of cell ultrastructure to be obtained from whole cells and complex tissues in their near native state. As the freezing process also preserves fluorescence, the link between three-dimensional (3D) ultrastructure and biological process is also enabled by targeted cryo-Correlative Light and Electron Microscopy (CLEM). However, the overall viability of cvEM is challenged by sample preparation, charge balance during imaging, sample sensitivity to beam damage, contamination, and very long acquisition times. Here we detail new experimental workflows to significantly reduce each of these effects and demonstrate the improvement in resolution possible with results from the nematode Caenorhabditis elegans and the ciliated protozoon Paramecium bursaria containing many endosymbiotic algae. These results demonstrate the versatility and potential wide-ranging utility of cvEM for 3D ultrastructural imaging of whole multicellular and unicellular organisms.

The core biopolymers (DNA, RNA and proteins) are assembled from their monomers under conditions that avoid water. RNA is crucial for the Origin of Life. When cleaved from its polymerized state, RNA first transitions to nucleoside 2,3-cyclic phosphates. In the reverse direction, RNA polymerizes from 2,3-cyclic monomers in dry states, creating short oligomers that then can ligate on a template under aqueous, alkaline conditions. We studied the role of the counterions in polymerization of 2,3-cyclic nucleotides under geologically plausible settings. Through experiments and simulations, we demonstrate that the presence of ammonium and alkylammonium counterions greatly improves RNA polymerization. The otherwise less reactive cytidine containing monomers formed polyC sequences of up to heptamers; copolymers of AU, GC, or GCAU were detected up to hexamers. Our findings suggest three reasons for this: (1) (Alkyl)ammonium cations form hydrogen bonds with phosphates, (2) their alkaline pKa value can trigger general base catalysis, and (3) (alkyl)ammonium salts naturally form dry, anhydrous materials. The findings indicate that pyrolyzed organic tars creating ammonia-rich gas pockets in subsurface rocks could have enhanced the early evolution of RNA. TOC image O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC="FIGDIR/small/713775v1_ufig1.gif" ALT="Figure 1"> View larger version (112K): org.highwire.dtl.DTLVardef@1adc431org.highwire.dtl.DTLVardef@12b8da0org.highwire.dtl.DTLVardef@5f187dorg.highwire.dtl.DTLVardef@140ed1a_HPS_FORMAT_FIGEXP M_FIG C_FIG

Automating cellular organelle segmentation is key to increasing the throughput in electron microscopy (EM) and volume EM (vEM) workflows. Deep learning (DL) has accelerated this process, but model development has predominately centered on mitochondria, partly because of a scarcity of suitable training datasets for other features. Here, we crowdsourced the manual step of labeling nuclei and lipid droplets (LDs) from complex cellular EM images and trained Panoptic DeepLab (PDL) models on these large, heterogenous annotated datasets as well as on publicly available vEM datasets. NucleoNet and DropNet, the resulting instance segmentation models for nuclei and LDs, respectively, yield high-quality results on varied benchmarks. We applied these models to quantify differences between 2D and 3D in vitro cancer models versus in vivo tumors, highlighting a path toward robust quantitation in EM. NucleoNet and DropNet are publicly available on our napari plugin, empanada v1.2, for easy point-and-click segmentation of 2D and 3D cellular EM images.

Ligand binding has been shown to induce significant alterations in the conformational landscape of proteins. Traditional crystallography approaches have provided valuable input about the end states in ligand-binding reactions. However, dynamical relationships between ligand binding and backbone rearrangement often remain obscured by crystallographic structures. In the present study, we use time-resolved serial synchrotron crystallography (TR-SSX) to directly visualize indole binding in the cavity of T4 lysozyme L99A in microcrystals under controlled environmental conditions. By integrating fixed target crystallography with LAMA-based ligand delivery, we have been able to track the progression of ligand binding and backbone rearrangement. By utilizing an occupancy refinement protocol, we have been able to quantify structural populations. Our studies reveal that ligand binding for this protein cavity follows a diffusion-limited process that progressively rearranges the F -helix of the protein towards a dominant conformational state. These findings establish an observable link between ligand diffusion, occupancy evolution and conformational adaptation within a crystalline environment. More broadly, our work shows how TR-SSX can quantify ligand and conformational populations during binding, providing a framework to interpret structural adaptation in real time.

Long-term live imaging of growing samples with light-sheet fluorescence microscopy provides unique insights into development, but morphogenesis often displaces features of interest outside the microscopes field of view (FOV), calling for automated methods to track these features and update the microscopes FOV in real time. Existing solutions, which typically rely on local or global intensity distributions, struggle to follow specific features robustly throughout morphogenesis, leading to truncated or incomplete datasets. Here, we present a light-sheet live tracking tool (LiLiTTool) that maintains user-defined regions of interest (ROI) within the FOV throughout extended imaging sessions. LiLiTTool uses Cotracker3, a state-of-the art deep learning-based motion predictor, augmented by sensor fusion with a trained object-detector. This enables robust compensation for drift, rotation, and deformation during morphogenesis, while meeting the timing constraints of live acquisition. We validated LiLiTool by integrating with the Viventis LS1 microscope, achieving sub-second processing and stable tracking of growing zebrafish embryos over many hours. LiLiTTool supports multi-ROI tracking in 3D, enabling simultaneous monitoring of multiple features within the same embryo and in multiple embryos during a single acquisition. LiLiTTool is modular and openly available on GitHub and as a napari plugin for post-acquisition tracking. By enabling precise, adaptive, and scalable real-time imaging, LiLiTTool advances smart microscopy approaches and provides the developmental biology community with a practical tool for capturing reliable spatio-temporal information in growing embryos or other morphogenetic systems.

CO dehydrogenases (CODH) are metalloenzymes that reversibly oxidize CO to CO2, at a buried NiFe4S4 active site. The substrates, CO and CO2, need therefore to be transported through the protein matrix to reach the active site. The most likely pathway for intra-protein diffusion is the hydrophobic channel identified in the crystal structures. Here, we use site-directed mutagenesis to study the highly conserved isoleucine 563 of Thermococcus sp. AM4 CODH2. Mutations at this position change the biochemical properties (KM for CO, product inhibition constant, catalytic bias...), and increase the resistance of the enzyme to the inhibitor O2, showing that isoleucine 563 indeed lines the gas channel. The I563F mutation decreases the bimolecular rate constant of inhibition by O2 15-fold, and increases the IC50 20-fold, which is the strongest improvement in O2 resistance reported so far. We show that the size of the introduced amino acids is less important than their flexibility - along with the size of the cavity formed near the active site in the channel. We also conclude that O2 access to the active site cannot be slowed down without also affecting CO diffusion. This tradeoff will have to be considered in further attempts to use site-directed mutagenesis to make CODHs more O2 tolerant.

Monte Carlo neutron ray-tracing simulations of time-of-flight (TOF)-Laue neutron macromolecular crystal diffraction (n-MX) using the McStas software package were done for the upcoming NMX Macromolecular Diffractometer at the European Spallation Source. Splitting neutron rays that arrive at the crystal lead to dramatic improvements in event formation with minimal computational overhead. The simulated event probability data was sampled using a new single-pass weighted reservoir sampling method, and processed like real n-MX data using DIALS. The effects of air and beamstop scatter on simulated data was investigated. SynopsisMonte Carlo simulations of neutron protein diffraction experiments provide useful data that models instrumental components that interact with neutrons, as well as the crystal diffraction itself. These data can be applied to instrument development, such as the commissioning of the NMX Macromolecular Diffractometer at ESS.

Understanding the structural dynamics of biomolecules is crucial for uncovering biological functions. As molecular dynamics (MD) simulation data becomes more available, deep generative models have been developed to synthesize realistic MD trajectories. However, existing methods produce fixed-length trajectories by jointly denoising high-dimensional spatiotemporal representations, which conflicts with MDs frame-by-frame integration process and fails to capture time-dependent conformational diversity. Inspired by MDs sequential nature, we introduce a new probabilistic autoregressive (ProAR) framework for trajectory generation. ProAR uses a dual-network system that models each frame as a multivariate Gaussian distribution and employs an anti-drifting sampling strategy to reduce cumulative errors. This approach captures conformational uncertainty and time-coupled structural changes while allowing flexible generation of trajectories of arbitrary length. Experiments on ATLAS, a large-scale protein MD dataset, demonstrate that for long trajectory generation, our model achieves a 7.5% reduction in reconstruction RMSE and an average 25.8% improvement in conformation change accuracy compared to previous state-of-the-art methods. For conformation sampling task, it performs comparably to specialized time-independent models, providing a flexible and dependable alternative to standard MD simulations.

Understanding how viral genomes are organized under extreme spatial confinement remains a fundamental challenge in structural virology. Icosahedral viruses, despite offering high-resolution capsid structures through cryo-electron microscopy and X-ray crystallography, present a major obstacle: their genomes do not obey icosahedral symmetry and are thus averaged out during standard reconstruction procedures, leaving genome topology largely unresolved. Computational modeling offers a complementary avenue, but existing approaches often rely on simplified polymer representations that fail to capture sequence-specific features and the extreme compaction observed in small DNA viruses. Here, we focus on Porcine Circovirus type 2 (PCV2), a member of the Circoviridae family and one of the smallest autonomous mammalian viruses, which packages a circular ~1.7 kb single-stranded DNA genome into a ~20 nm T=1 icosahedral capsid at one of the highest DNA packing densities found in nature. We introduce an integrative methodology combining AI-based structural prediction, lattice Monte Carlo simulations, and multiscale molecular dynamics to generate and simulate three-dimensional topological models of the complete PCV2 virion. Our results demonstrate that multiple distinct genome arrangements can produce virions with indistinguishable external morphology, yet differ substantially in their internal stress distributions and predicted particle stability. These findings suggest that PCV2 populations comprise energetically heterogeneous assemblies with implications for infectivity, uncoating, and environmental persistence, while providing a generalizable framework for modeling genome topology in other confined viral systems.

Investigating single-cell dynamics and morphology in tissues and embryos requires highly accurate quantitative analysis of microscopy images. Despite significant advances in the field of bioimage analysis, even the most sophisticated segmentation and tracking algorithms inevitably produce errors (e.g. : over segmentation, missing objects, miss-connected objects). Although error rate may be small, their propagation throughout a time-lapse sequence has catastrophic effects on the accuracy of tracking and extraction of single cell parameters. Extracting single cell temporal information in the context of tissue/embryo requires thus expert curation to identify and correct segmentation errors. In the movies commonly used in developmental biology and stem cell research, both the number of imaged cells and the duration of recording are large, making this manual correction task extremely time-consuming. This has now become a major bottleneck in the fields of development, stem cell biology and bioimage analysis. We present here EpiCure (Epithelial Curation), a versatile tool designed to streamline and accelerate manual curation of segmentation and tracking in 2D movies of large epithelial tissues. EpiCure uses temporal information and morphometric parameters to automatically identify segmentation and tracking errors and provides user-friendly tools to correct them. It focuses on ergonomics and offers several visualization options to help navigating in movies of tissue covering a large number of cells, speeding up the detection of errors and their curation. EpiCure is highly interoperable and supports input from a wide range of segmentation tools. It also includes multiple export filters, enabling seamless integration with downstream analysis pipelines. In this paper, using movies from several animal models, we highlight the importance of curating cell segmentation and tracking for accurate downstream analysis, and demonstrate how EpiCure helps the curation process for extracting accurate single cell dynamics and cellular events detection, making it faster and amenable on large dataset.

The air-water interface (AWI) remains the primary barrier to routine high-resolution cryo-EM structure determination, driving protein adsorption, structural denaturation, and restricted particle orientations during vitrification. Here, we describe a simple and broadly applicable strategy to mitigate these effects using the mild non-ionic detergent n-decyl-{beta}-D-maltopyranoside (DM). Addition of DM at low millimolar concentrations immediately prior to vitrification consistently suppresses AWI-driven artifacts, resulting in improved angular sampling, reduced structural damage, and enhanced reconstruction quality across diverse macromolecular systems. Using this approach, we obtained a high-resolution reconstruction of the 65 kDa Nucleophosmin 1 pentamer, a target previously limited by severe preferred orientation issues. We further show that DM promotes isotropic particle distributions for high-resolution reconstruction of hemagglutinin, transthyretin, as well as suppressing denaturation of aldolase while stabilizing its C-terminus. Our results indicate that DM effectively passivates deleterious air-water interface interactions without compromising particle integrity. These results establish DM as an effective additive for improving the robustness of single-particle cryo-EM sample preparation. O_FIG O_LINKSMALLFIG WIDTH=174 HEIGHT=200 SRC="FIGDIR/small/716008v1_ufig1.gif" ALT="Figure 1"> View larger version (56K): org.highwire.dtl.DTLVardef@108a6edorg.highwire.dtl.DTLVardef@10728b4org.highwire.dtl.DTLVardef@1014b2eorg.highwire.dtl.DTLVardef@1eed745_HPS_FORMAT_FIGEXP M_FIG C_FIG

Coat protein (CP) tertiary structures and their capsid organization of spherical viruses are highly conserved within each virus family. While AlphaFold successfully predicts the tertiary structures of individual CPs, their association to form proper quaternary assemblies cannot be easily accomplished. Here, we report a generalized methodology and associated web-based utility (https://foldavirus.org) that combines AlphaFold predictions of CPs with the knowledge on corresponding icosahedral architectures (e.g., T=1, 3, 4...) based on the known structures from the same virus family to generate associated capsids. The resulting assemblies are subjected to Amber energy minimization to relieve any steric clashes at the inter-subunit interfaces. Significantly, the capsid models are validated by calculating robust Mahalanobis distance using the residue annotations categorized as interface, core and surface amino acids with respect to those observed in the experimentally determined analogous structures. Given the amino acid sequence of CP(s), we successfully generated capsids up to T=9 icosahedral symmetry, including those of Picornaviruses that display pseudo-T=3 symmetry comprising different CPs. As the number of currently available CP sequences are 3-4 orders of magnitude larger than the experimentally determined 3D-structures, this approach bridges the huge gap that exists between the corresponding sequence and structure space.

Particle bombardment systems are widely used for plant transformation, but commercial devices are expensive and rely on high-pressure helium gas. This study aimed to develop a cost-effective and helium gas-free alternative using an air duster gun connected to a commercial compressor. A nozzle (for DNA with transgenes), gold particles (as DNA carriers), nozzle-to-sample distance, and a method for coating gold particles with DNA were optimized to yield better transformation efficiency in targeting onion epidermal cells and rice calli. From the rice calli transformed with the newly developed system (a tool to shoot genes with massive air from a compressor: TSGMAC), stable transgenic plants could be obtained. TSGMAC offers a low-cost and helium gas-free solution for plant transformation and genome editing and can enhance accessibility to particle bombardment-based techniques.

Fourier ptychographic microscopy (FPM) uses sequential multi-angle illumination and iterative phase retrieval to recover a high-resolution complex image from a series of low-resolution brightfield and darkfield images. We present OpenFPM, an open-source FPM platform in which conventional and optomechanical hardware is replaced with compact, low-cost 3D printed components. Illumination, sample and objective positioning, and camera triggering are controlled using a Python-based interface on a Raspberry Pi microcomputer. With a 10 x /0.25 NA objective lens and 636 nm illumination, OpenFPM experimentally achieves amplitude and phase reconstructions with an effective synthetic NA of 0.90 over a 1 mm field-of-view. This platform gives researchers accessible and affordable hardware for developing and testing LED-array microscopy techniques for a range of biomedical imaging applications.

Human RNase 2 (eosinophil-derived neurotoxin, EDN) is a major eosinophil granule protein of the vertebrate-specific RNase A superfamily and is involved in antiviral response and inflammation. Identifying ligand-binding pockets in EDN is thus relevant to structure-based drug design. In our laboratory we identified by protein crystallography a conserved site at the protein surface binding to carboxylic anion molecules (malonate, tartrate and citrate). Searching for potential biomolecules rich in anion groups and considering previous report of EDN binding to glycosaminoglycans, we explored the protein binding to saccharides. Next, EDN crystals were soaked with mono- and disaccharides, and the 3D structures of ten complexes were solved by X-ray crystallography at atomic resolution. We identified protein binding pockets to glucose, fucose, mannose, sucrose, galactose, trehalose, N-acetyl-D-glucosamine, N-acetylmuramic acid, and the sialic acid N-acetylneuraminic acid. A main site for glucose, fucose, and galactose was located adjacent to the spotted carboxylic anion site. Secondarily, N-acetylneuraminic acid, N-acetylmuramic acid, sucrose, galactose, and mannose shared another protein surface region. Overall, the saccharides clustered into seven defined sites, outlining a conserved recognition pattern, which was further analysed by molecular modelling. Interestingly, within the RNase A family, we find amphibian RNases that were initially isolated as carbohydrate binding proteins and named as leczymes, combining enzymatic and lectin properties. The present data is the first systematic structural characterization of a mammalian sugar-binding RNase within the family. The results highlight unique EDN residues that mediate its sugar specific interactions, of particular interest for a better understanding of the protein physiological role. HighlightsO_LIstructure of RNase 2 in complex with mono and disaccharides at atomic resolution C_LIO_LIidentification of RNase 2 unique sugar binding sites C_LIO_LIcharacterization of a mammalian RNase A family enzyme with lectin properties C_LI Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=110 SRC="FIGDIR/small/713198v1_ufig1.gif" ALT="Figure 1"> View larger version (46K): org.highwire.dtl.DTLVardef@1d805f7org.highwire.dtl.DTLVardef@16fcc49org.highwire.dtl.DTLVardef@ccfd92org.highwire.dtl.DTLVardef@1b8f1e_HPS_FORMAT_FIGEXP M_FIG C_FIG

G protein-coupled receptors (GPCRs) are critical regulators of human physiology and major drug targets. Although structural studies have provided valuable insights, determining GPCR structures remains challenging, especially for inactive state receptors. Recent advances in cryo-electron microscopy (cryo-EM) have enabled structural determination of small GPCRs by using fusion partner proteins and binders to increase molecular weight. However, current methods require extensive experimental screening of fusion constructs. Widely adopted strategies, such as BRIL-Fab complexes, also face limitations due to inherent flexibility. Here, we introduce a streamlined and universal pipeline that integrates an in silico fusion construct screening program, NOAH (NOAH: NOn-experimental, AI-assisted High-throughput construct screening), with a de novo designed fusion protein called ARK1 (ARtificially-designed fiducial marKer). We validate the efficacy of NOAH by determining the structures of the vasopressin V2 receptor (V2R) bound to the clinical antagonist tolvaptan and the partial agonist OPC51803, as well as the bradykinin B2 receptor (B2R) bound to the clinical antagonist icatibant, thereby elucidating their activation and deactivation mechanisms. Furthermore, we demonstrate the capability of NOAH-ARK1 by solving the tolvaptan-bound V2R structure at higher resolution and showcase the methods versatility by determining the structure of lysophosphatidic acid receptor 2 (LPA2) bound to the antagonist Ki16425. This approach eliminates the need for time-consuming and labor-intensive construct optimization, providing a rapid and widely applicable solution for high-resolution GPCR structure determination and drug discovery.

Glutamate racemase (MurI) catalyzes the stereochemical interconversion of L-glutamate to D-glutamate, a key element of bacterial peptidoglycan biosynthesis. In this study, we present the crystal structure of Helicobacter pylori glutamate racemase at 1.43 [A] and in monoclinic symmetry, as previously reported models, but different unit-cell parameters. The present model contains a single dimer and retains the previously described head-to-head dimer arrangement. The differences between the models arise from variations in unit-cell parameters, which lead to altered crystal packing interactions rather than changes in the quaternary assembly. The monomeric fold and active-site architecture remain conserved and are consistent with the catalytic features described for bacterial glutamate racemases. This structure provides an updated, high-resolution structural model for H. pylori glutamate racemase and highlights the variability in crystal packing within the same space group.