Journal of Structural Biology

Serial-sectioning electron microscopy allows the exploration of the 3D ultrastructure of cells, tissues or whole organisms. Sample preparation is a critical step for this technique, which determines the quality of downstream imaging and analysis. Here we optimize a high-pressure freezing sample-preparation workflow for larvae of the marine annelid Platynereis dumerilii. We tested several fixation and staining methods and screened a large number of specimens by synchrotron X-ray imaging to evaluate specimen integrity and contrast. We further evaluated selected blocks by array tomography SEM to select the best performing protocols. Our protocols and robust workflow could be adapted to similar marine planktonic organisms including ciliated invertebrate larvae.

Cryogenic electron microscopy (cryo-EM) has enabled rapid progress in structural biology throughout the last decade. However, cryo-EM sample preparation still represents a major bottleneck in high-resolution structure determination. Many proteins and macromolecular complexes interact with the air-water interface during sample preparation, which can cause protein denaturation, complex disassembly, and preferred particle orientation. Development of methods to alleviate these issues is an active field of investigation. However, a universally applicable solution to this problem has not been found so far. Here, we describe the simple and cost-effective usage of synthetic peptides with defined amino acid sequence to protect cryo-EM samples from complex disassembly and reduce preferred orientation. We show benefits during cryo-EM grid preparation of a large E3 ubiquitin ligase termed the anaphase-promoting complex (APC/C), and the small CDK11:cyclin L:SAP30BP complex.

Preferred particle orientation, which results from the adsorption of proteins to the air-water interface, is a frequently encountered issue in single-particle cryo-electron microscopy (cryo-EM). Particles then present only a limited number of views, which are oversampled during data acquisition at the expense of throughput, while other views may be missing altogether. In severe cases, a high-resolution reconstruction becomes unattainable. We have recently established that ultrasonic excitation of the sample during vitrification improves the angular distribution by detaching particles from the air-water interface and reshuffling their orientations. Here, we describe an implementation of this principle for a plunge freezing apparatus, in which the ultrasonic transducer is integrated into the head of the pair of tweezers holding the sample, and show that this design is able to reduce preferred orientation. We demonstrate this for hemagglutinin, the canonical example of preferred orientation, and show that the fraction of side and tilted views increases with the excitation amplitude. This allows us to obtain a complete reconstruction of the trimer at 2.5 [A].

The visualisation of large biological samples in cryo-electron tomography requires both few-nanometre resolution and a wide, ideally >10 micrometrre, field of view. For a single image, increasing magnification trades increased resolution for a smaller field of view. Montaging, where high-magnification images are stitched together to increase field of view, addresses the trade-off but is dose-inefficient with round beams that are standard in transmission electron microscopy (TEM), since this illumination shape does not tessellate. Our workflow greatly simplifies montaging schemes for dose-sensitive specimens, allowing large montages of any shape - from individual cells or other features of interest up to whole lamellae - to be collected at high resolution without compromising electron dose efficiency. We demonstrate the workflow by acquiring montage tomograms of whole yeast (Saccharomyces cerevisiae) cells in cryo-FIB lamellae and of human neurons.

Cryo-Electron Microscopy (CryoEM) Single Particle Analysis (SPA) is pivotal for elucidating protein structures and their conformational dynamics. Several critical steps are required to obtain accurate maps. First, the \textit{ab initio} relative orientations and shifts between images must be determined. Second, the common issue of specimens presenting a preferred orientation should be adequately addressed. Third, all of the above must account for particle flexibility and sample heterogeneity. Additionally, these steps are interrelated, making the search for all these parameters a real challenge when considering convergence robustness. In this context, we present ReconSIREN, a novel deep learning framework that addresses the key steps outlined before in an \textit{ab initio} manner while greatly minimizing one of the most important practical burdens in SPA, namely the errors introduced in the CryoEM maps by the existence of preferred orientations; the latter is achieved by a change of the optimization approach that avoids the so-called ``attraction problem'', and by the introduction of a new type of geometrical priors. It does so fully \textit{ab initio}, using deep learning training/inference steps without resorting to more traditional optimization approaches, and generating highly accurate initial reconstructions. We present maps as both homogeneous (consensus) maps and a full conformational landscape for analyzing flexibility and heterogeneity. Thanks to its Mixture-of-Experts (MoE) architecture, ReconSIREN distributes the estimation workload across independent experts, improving optimization efficiency and simplifying the process.

Continuous protein conformations are essential for understanding fundamental biological processes and supporting drug discovery. Although cryo-EM can resolve individual states at high resolution, recovering continuous heterogeneity from 2D particle images remains challenging. High noise, motion blur, and limited structural priors make it difficult to accurately generate and validate high-resolution continuous conformations using raw particle data. Here, we introduce cryoMV, a framework that integrates structure-prior-guided modeling with real-particle validation for continuous conformational transitions. CryoMV uses reference density maps to establish structural anchors and motion priors, models candidate transition paths between selected conformations, and transfers the learned representation to raw 2D cryo-EM particle images. Each candidate conformation is subsequently evaluated using the estimated particle poses and contrast transfer functions. Supported conformations are reconstructed through raw particle back-projection and assessed using canonical half-maps and Fourier shell correlation. On EMPIAR-10516 and EMPIAR-10345, cryoMV achieves excellent performance in terms of robustness, verifiability, and reconstruction resolution. By incorporating structure-prior modeling and evidence from the raw particles, cryoMV offers an explicit mechanism for assessing whether generated conformations are supported by experimental data and provides a practical approach to reducing model-induced artifacts in continuous cryo-EM heterogeneity analysis.

Structural model building and refinement need many decisions between different programs. These decisions often depend on an experienced researcher who knows how the programs work together and how to read experimental data. We used Claude Code with Claude Opus 5.5 to supervise this work for 24 cryo-EM and X-ray cases. The work finished in about 36 hours on one workstation. The agent prepared the inputs, ran established programs, read the validation results, compared candidate models and chose the next step. For 15 deposited cryo-EM models with weak starting geometry, the median MolProbity score improved from 2.62 to 1.76. For three crystal structures, the agent models came close to the published Rfree values. Chemical information such as ligands, ions and modifications must be provided by the researcher, in the same way as the sequence. With this information, the agent can place and check these components. These results show that agent supervision is acceptable when every decision can be inspected. The researcher makes the final scientific judgment. We suggest practical requirements for this kind of use. Future AI agents may help researchers obtain high quality and reliable structures of biological macromolecules.

Cryo-EM maps frequently suffer from weak structural signals, spatially variable quality, and noise, complicating interpretation and downstream modeling. Existing post-processing approaches treat density enhancement, quality assessment, and masking as separate tasks, often amplifying residual noise or introducing local artifacts. Here, we present CryoCodex, a unified framework that jointly performs density enhancement, local quality estimation, and molecular masking. CryoCodex introduces a multi-head, multi-scale vector-quantization module that projects encoder-derived latent features onto a shared codebook of structural prototypes. By imposing discrete constraints across feature subspaces and spatial scales, the codebook suppresses stochastic noise while preserving recurrent structural patterns. On a non-redundant test set of 126 experimental maps spanning 3-8 [A], CryoCodex outperformed four representative methods, achieving a mean map-model FSC-0.5 of 3.93 [A] versus 4.62 [A] for EMReady2 and 6.87 [A] for the deposited maps. Downstream automated de novo model building with Phenix further confirmed the quality of the CryoCodex-enhanced maps. These gains extended to maps containing nucleic acids, where CryoCodex produced the best map-model agreement and improved model building. Half-map cross-validation indicated that these improvements are anchored to experimental data rather than driven by half-map-specific noise or structural hallucination. CryoCodex also delivered reliable local quality estimates and molecular masks within the same framework. Together, these results establish CryoCodex as a practical tool for cryo-EM map post-processing and structural determination in challenging cases.

Atomistic simulations often struggle to capture the full conformational landscape of biomolecular complexes on experimentally relevant timescales, leaving gaps in our understanding of functionally relevant thermodynamics and kinetics. Cryo-electron microscopy (cryo-EM) provides high-resolution structural snapshots spanning multiple conformational states, but translating these static images into dynamic mechanisms remains challenging. Existing approaches for integrating cryo-EM data with molecular simulations either perturb the underlying dynamics through artificial biasing potentials or remain confined to conformations already present in the prior ensemble. We introduce cryoWEight, an automated and extensible Python framework that couples iterative weighted ensemble (WE) simulations with ensemble reweighting against cryo-EM particle images to steer simulation trajectories towards conformational states consistent with cryo-EM populations. We have found that reweighting in the image space introduces a directional bias towards the target distribution even when the prior ensemble has no overlap with the target distribution in the collective variable space. The reweighting requires only the state likelihoods that set the ensemble weights, and any observable that defines these likelihoods can serve as the target distribution, therefore generalizing beyond the application of cryo-EM. cryoWEight is validated on three proteins: chignolin, the N-terminal domain of ribosomal protein L9, and adenylate kinase. The framework converges towards the target conformational states across different sampling strategies and noise levels, providing a scalable route to integrate cryo-EM data with enhanced sampling approaches.

Tardigrades are minute animals renowned for environmental stress tolerance, and their remarkable biology has motivated extensive molecular studies using biochemical and biophysical approaches. Yet the native structures of endogenous macromolecular assemblies in tardigrades remain largely inaccessible, in part because only limited biological material is available. Here we show that purification-free single-particle cryo-EM, guided by long-read cDNA sequence assignment, can recover multiple endogenous assemblies directly from crude extract of a wild-derived Macrobiotus cf. shonaicus culture. We reconstructed a dimeric vitellogenin (Vg) particle at 3.10 [A] resolution, a previously uncharacterized tardigrade ferritin at 3.09 [A] resolution, and a low-resolution ribosome map. The Vg particle is a glycosylated and metal-containing assembly with structural evidence of proteolytic processing and contains lipid-like densities occupying a broad chamber whose walls include N-linked glycans. The ferritin structure revealed a divergent C-terminal helix that lines a mineral-like density observed at a position corresponding to previously described mineralization sites in ferritin-family cages. These results establish purification-free cryo-EM as a route to structural analysis of endogenous macromolecular assemblies from minute animal samples. In tardigrades, this approach provides a foundation for future state-dependent structural studies, including desiccation, rehydration and reproduction.

Single-particle cryo-electron microscopy (cryo-EM) plays an increasingly important role in structure-based drug discovery (SBDD) by revealing target protein-ligand interactions at the molecular level, but its application has largely focused on large- and medium-sized protein complexes. Many therapeutically important targets are smaller than 50 kDa and remain highly challenging for cryo-EM analysis because of low signal-to-noise ratios and difficulties in particle alignment. Here, we describe cryoFAST, an integrated workflow that couples ligand-compatible Fab discovery and structure-guided computational maturation with biochemical triage, fiducial-complex assembly, and single-particle cryo-EM. Through phage-display screening and/or AI-guided design, we developed high-affinity Fab binders of approximately 50 kDa that stabilize the target without disrupting ligand-target interactions and then assembled Fab-target complexes for cryo-EM analysis. Using the therapeutically relevant extracellular domain (~20 kDa) of interleukin-4 receptor (IL-4R) as a proof of concept, we determined its cryo-EM structure at 2.74 Angstrom resolution and established a framework for elucidating ligand-binding modes. This workflow provides a practical, efficient, and potentially broadly applicable route to the structural characterization of small protein targets that are otherwise challenging to study. By extending the applicability of cryo-EM to high-value but technically challenging small targets, cryoFAST may open new opportunities for structure-based drug discovery.

Mitochondrial NADH-ubiquinone (UQ) oxidoreductase (respiratory complex I) reduces UQ within its long tunnel-like cavity, which is coupled to triggering proton translocation at the membrane domain. Although mammalian complex I adopts distinct "open" and "closed" conformations, the architecture of the UQ-accessing tunnel in the open conformation remains poorly defined owing to partial disorder of the UQ catalytic region near Fe-S cluster N2 in the tunnel. Here, we applied cryo-electron microscopy (EM) single particle analysis to determine the structures of complex I in bovine heart submitochondrial particles in the presence or absence of a mycothiazole-type inhibitor. Preceding photoaffinity labeling experiments using a synthetic photoreactive derivative ([125I]pMT) revealed that mycothiazole specifically labels the 49-kDa and ND1 subunits, both of which form a part of the UQ-accessing tunnel. Cryo-EM analysis demonstrated that bound mycothiazole reorganizes and stabilizes the UQ catalytic region in both open and closed conformations, enabling the first complete structural modeling of the entire UQ-accessing tunnel in the open conformation. Unexpectedly, in the presence of bound mycothiazole, loops of the PSST subunit forming the catalytic region in the tunnel adopted closed conformation-like features, whereas open conformation-like features were determined for the transmembrane helices of ND1 and ND6 subunits. The present study revealed that bound mycothiazole can reorganize the UQ catalytic region in the UQ-accessing tunnel and lead to a hybrid conformational state beyond the canonical open-versus-closed conformational model.

Carbohydrates are essential molecules involved in a wide range of biological processes. While their structural representation in the Protein Data Bank (PDB) has been hindered due to their highly diverse and flexible nature, advancements on X-ray crystallography and CryoEM are accelerating the obtaining of their experimental structures, which in turn supports improved analyses of their structural properties, such as the role of their non-chair conformations on biological processes. In this context, the current work provides a comprehensive and up to date overview of carbohydrate structures within the PDB through classification, quantification, and characterization of monosaccharides and glycan chains, incorporating Cremer-Pople puckering coordinates to evaluate pyranose conformations. Our findings show that, even with a steep increase in CryoEM structures including more complex oligosaccharides, carbohydrate data remains concentrate dinsmallglycanchains, with a about 4% of non-chair conformation sintopresolution structures. Free energy landscapes for the pyranose ring in the presence and absence of nine different binding proteins, including both enzymes and non-enzymes, reinforce the preferred stability of chairs and the rarity of conformational induction upon binding.

Cryo-electron microscopy (cryo-EM) has become a central method for determining structures of molecular assemblies, requiring reliable tools to interpret the density maps. Despite recent advances in AI-driven model building, at intermediate-to-low resolution (4-15 [a]) accurate rigid-body fitting of existing atomic structures into cryo-EM maps remains an essential step in the modelling process. Here we present an updated version of our software package PowerFit. It performs an exhaustive translational and rotational search against the cryo-EM density map, implementing sensitive correlation functions. The updated release preserves the fitting accuracy and sensitivity of the original method while substantially improving the computational performance through modernised CPU and GPU implementations. In addition, to facilitate the interpretation of results, it provides an HTML report with interactive 3D visualisation using MolViewSpec. The same HTML report is used to generate the result page when using PowerFit via its web service. The report enables immediate inspection and validation of the results. The software's accessibility has been enhanced by updating it from Python 2 to Python 3, enabling straightforward installation via the Python Package Index (PyPI) and simple integration into computational pipelines. PowerFit is freely available at https://github.com/haddocking/powerfit, as a web service at https://wenmr.science.uu.nl/powerfit or in JupyterLite directly in a web browser at https://www.bonvinlab.org/powerfit/jupyterlite.html.

Hetero-oligomeric protein complexes play crucial roles across biology, providing combinatorial diversity and context-specific functionality in membrane receptors, molecular chaperones and beyond. Yet, existing methods are limited in their ability to resolve and quantify the assembly energetics of complex biomolecular populations. Here, we introduce a comprehensive method combining iterative 3D classification, error-rate quantification via simulated particles, and global thermodynamic modeling to extract interaction energies from cryo-EM data. Using hexameric cruciferin (the primary rapeseed storage protein) as a model, we reconstruct a 1.83 [A] structure and distinguish three highly similar 50 kDa isoforms. We resolve the complex compositional heterogeneity of the hexameric species at the single-particle level and fit a global binding model revealing pairwise interaction energies between isoforms differing by fractions of the thermal energy (kB T ). This method offers a powerful, broadly applicable tool to characterize the compositional heterogeneity and energetics of biomolecular complexes.

In this paper, we introduce Nagini3D, a 3D biological image segmentation method that hybridizes two high-performing yet highly complementary segmentation paradigms: deep learning-based approaches and snake-based methods. Our approach leverages both the high efficiency of data-driven techniques and the convenient shape representation provided by active surfaces, which proves highly practical for incorporating a priori knowledge as well as for the post-analysis of segmented objects. By considering a complete 3D volume, our method ensures segmentation consistency across all three dimensions, unlike many approaches that handle volumes in a slice-by-slice manner. Additionally, it theoretically enables the representation of any object with spherical topology, overcoming the limitations of common 3D methods that rely on object convexity assumptions. Furthermore, the continuous representation of objects allows for a resolution-independent description, facilitating the extraction of various geometric features of interest (e.g., local curvature, principal deformations). We demonstrate both the quality of the segmentation produced by our method and the richness of the accessible geometric features through an extensive set of experiments conducted on multiple real and simulated datasets, including objects with challenging geometries.

Nuclear segmentation in 3D microscopy remains challenging, particularly for low signal-to-noise ratio (SNR) data from timelapse imaging, in vivo experiments, or dense tissue samples. Yet accurate 3D nuclear segmentation is a prerequisite for quantitative single-cell analysis, making this a key bottleneck for high-throughput study of challenging datasets. We present NucLogic, a fast and robust 3D nuclear segmentation algorithm that combines the maturity of 2D segmentation with geometric stitching rules based on nuclear shape, orientation, and intensity, to reconstruct accurate 3D volumes. NucLogic reliably segments challenging datasets including long-term timelapse imaging of organoids, and large dense structures such as the zebrafish brain. Benchmarked against other state-of-the-art segmentation tools, NucLogic matches F1 scores at high SNR, with increasing outperformance as SNR decreases. Importantly, segmentation completes at 20-50% of the computation time, with greater speed gains on larger images. Beyond segmentation, NucLogic includes an intuitive graphical user interface allowing non-experts to segment, quantify, and visualize 3D and 4D data. This enables easy quantification of cell numbers and integration of a variety of both cell-intrinsic properties, such as phenotype, morphology, and local environment metrics, including density and spatial interactions. Together, NucLogic makes quantitative 3D nuclear analysis of previously unmanageable datasets accessible to a broad research community.

Proteins are molecular machines whose dynamics govern their function. Using time-resolved crystallography, the basis of protein mechanics can be uncovered by physical perturbation on protein crystals. Perturbations are often vector fields that, in crystals, break symmetry. Lacking existing tools for analyzing symmetry breaking, we built open-source software to do so. We applied this method to three time-resolved crystallography experiments and analyzed the effect of vectorial perturbations on macromolecular crystal structure. We demonstrate that our method can handle symmetry reduction in all 65 Sohncke space groups.

While cryogenic electron microscopy (cryo-EM) has transformed structural biology, sample preparation remains a major determinant of success. Specifically, the air-water interface (AWI) can damage complexes, impose preferred orientation, and reduce particle density. Here, we introduce cryoSHIELDs, a chemically diverse library of peptide additives designed to passivate the AWI and improve cryo-EM grid preparation. Across 24 protein systems spanning GPCRs, ABC and SLC transporters, soluble and Fab-containing complexes, and enzymes, cryoSHIELDs increased the success rate of structure determination. Individual cryoSHIELDs improved particle density, angular sampling, complex stability, and, for routine targets, map quality and resolution. This toolkit enabled the structural characterization of important targets, including the ABCC6 transporter, revealing unique TMD0-mediated stabilization, and DAMGO-bound -opioid receptor-Gi complex using substantially lower protein concentration. CryoSHIELDs provide a simple, tunable, and broadly applicable platform for more reliable cryo-EM in discovery biology and structure-based drug development.

High-speed atomic force microscopy (HS-AFM) experiments provide unique insights into nanoscale biological processes by directly visualizing the surface topography dynamics of biomolecules, such as proteins, over extended periods of time and generating large imaging data sets. However, the lack of computational methods for automated data analysis has thus far limited the full utilization of these data for the quantitative understanding of single-molecule dynamic. Here, we present the AFM Analysis Arena (A3) within BioAFMviewer as a software platform for automated, high-throughput quantitative analysis of AFM topographic imaging data within a user-friendly interactive graphical interface, implementing a convenient workflow from raw HS-AFM (AFM) imaging to statistical analysis. In several applications to HS-AFM imaging data, we demonstrate methods of automatized molecule detection and dynamic tracking to highlight the available capacities, aiming to transform HS-AFM measurements from observations towards an analytical tool in nanoscale biology.