IUCrJ

Capturing meta-stable conformations of enzymes and ligand complexes demands structural snapshots beyond static crystal structures. While time-resolved serial crystallography at room temperature, offers a time-resolution down to the femto-second domain it requires large amounts of micro crystals, specialized beamlines and considerable experience. Moreover, as the majority of enzymes displays turnover-times in the millisecond domain or slower, simpler methods can provide meaningful structural insight into enzyme catalysis. Vitrification of protein crystals can trap reaction intermediates by rapid cooling to {inverted exclamation} 100 K, and has traditionally been used to gain insight into long lived reaction intermediates such as product complexes. However, manual vitrification procedures are limited to long delay times of at least several seconds and heavily suffer from operator variability. A solution to this problem is provided by automatic crystal plunging devices, such as the Spitrobot, that plunge loop-mounted protein crystals into liquid nitrogen within millisecond time-scales. Versatile means of reaction initiation can be achieved either by micro dispensing a ligand droplet, or via optical excitation of light-sensitive proteins, or via the photoactivation of caged compounds. In addition to the conceptual simplicity, another benefit of cryo-trapping is that data can be collected at conventional synchrotron beamlines, exploiting their robust high-throughput capabilities. Thus, compared to room-temperature time-resolved crystallography, users not only benefit from uncoupling sample-preparation and data-collection, but also from a reduction in the required technical expertise and ready access to radiation sources. However, as cryo-trapping crystallography explores dynamic structural changes that become only visible by the comparison of several samples, experiments have to be carefully planned to carry out the necessary controls and to avoid mis- or over-interpretation of the results. Here we describe a detailed protocol for cryo-trapping time-resolved crystallography using automated crystal-plungers that enables researchers to map enzymatic reaction coordinate pathways within the millisecond domain.

Accurate determination of state occupancies is essential for interpreting the structural heterogeneity inherent in time-resolved crystallography. However, in cases of high spatial overlap between states, as commonly observed in time-resolved crystallography data, the strong correlation between occupancy and atomic displacement parameters (ADPs) can render single point estimates from standard refinement protocols unreliable. We introduce MEROS (Multi-state Ensemble Refinement for Occupancy Statistics), a pipeline that implements an ensemble refinement approach to assess the post-refinement occupancy-ADP statistics of multiple overlapping states. MEROS utilizes a Monte Carlo sampling of the parameter space, performing independent refinements from randomized starting occupancies and ADP values to empirically characterize the convergence and uncertainty of the solution. The method is implemented as a modular Python pipeline that wraps established refinement programs, ensuring compatibility with existing workflows. We demonstrate its applicability in two case studies: a two-state ligand binding model in T4 lysozyme L99A and a four-state covalent catalysis mechanism in {beta}-lactamase CTX-M-14. MEROS provides occupancy and ADP mean values with standard deviations that directly quantify the informational content of the experimental diffraction data.

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.

Powder X-ray diffraction (PXRD) measurements performed on platforms originally designed for single-crystal diffraction are strongly affected by how the powder sample is presented to the X-ray beam, including the delivery configuration and support geometry. Here, we developed a modified Terasaki-plate-based sample-delivery method for PXRD using a laboratory single-crystal diffractometer implemented with the XtalCheck-S plate-reader operational mode at Turkish Light Source. The method was regarded under comparable measurement conditions relative to a standard loop/pin-based and a grease-based Terasaki setup using 5-{[4-(2-Methoxyphenyl)piperazin-1-yl]methyl}-4-ethyl-4H-1,2,4-triazole-3-thiol as a model analyte. The loop-based method allowed only limited powder sampling, whereas the grease-based Terasaki setup enabled multi-well sample delivery but produced higher background and weaker diffraction profiles. Conversely, Kapton-sealed Terasaki ensured secure retention of small amounts powder while providing lower background and clearer diffraction patterns. Within short total data collection times of only 1-2 min, the Kapton-Terasaki method delivered the best overall PXRD performance among the tested methods. Search-match and profile-fitting analyses showed that all three approaches sampled the same crystalline material, while the Kapton-based method gave the lowest profile residual (Rp = 9.6%) and the most reliable whole-pattern profile. These results demonstrate that optimizing sample delivery, rather than modifying the core instrument hardware, can substantially extend PXRD capability on an existing in situ crystallography platform for rapid, laboratory-based screening and comparative multi-sample measurements.

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.

Perturbative X-ray crystallography can visualize functional dynamics and conformational changes in proteins at atomic resolution. During a typical perturbative crystallography experiment, only a fraction of protein molecules in a crystal will be perturbed, or "excited". As a result, the observed data represent a mixture of excited and ground states. The conventional approach to estimating the excited-state structure factor amplitudes is to linearly extrapolate the difference between the structure factor amplitudes of the perturbed and unperturbed data. This approach often fails to yield well-refined structural models because it amplifies experimental errors and neglects phase differences between the ground and excited states. Here, we introduce an approach to estimating excited-state structure factor amplitudes that starts from a statistical prior for the correlations between excited and ground states. Using benchmarks from time-resolved crystallography and a drug-fragment screen, we illustrate how this approach effectively addresses the limitations of traditional extrapolation.

Macromolecular crystallography is limited by the phase problem: diffraction experiments measure amplitudes but not the phases required to reconstruct electron density. Existing phasing routes usually seek enough continuous phase information for density modification and model building to converge. Here, we ask how much phase information can be discarded while preserving convergence. We analyzed 14,148 diffraction datasets from chiral crystals to characterize centric reflections in reciprocal-space asymmetric units. After conditioning by centric trace and, where required, index parity, the two theoretical symmetry-allowed phase values were populated near equally, close to 50:50, independent of space group, defining a compact symmetry scaffold. We then retained this exact scaffold while compressing reference acentric phases to a one-bit alphabet {0, {pi}}; as expected from their diffuse parent distribution, the assignments were also near-balanced. Although this binary representation, with fixed attenuation 2/{pi}, introduces large angular errors (mean of 52{degrees}), it frequently supported automated structure solution: in paired Phenix AutoBuild tests, 705 of 894 binary initializers met a conservative joint criterion of final Free R [≤] 30% and relative chain recovery [≥] 70%, within a 20.0-2.5 [A] resolution window. To rank candidate seeds without rebuilding, we developed a branch-balanced Basin Score from inexpensive density-modification and map-connectivity observables computed at 20.0-3.5 [A]. The empirical score quickly separates productive from unproductive initializers before AutoBuild. Controlled phase inversion shows that basin compatibility decays gradually and can reappear in an anti-phase-related branch, indicating that buildability is not confined to a single neighborhood around the reference phase set but extends to a much broader field. These results recast phase initialization as basin entry and support future symmetry-aware, binary phase-search strategies.

Fourier-space projection operations are central to electron microscopy single-particle analysis and electron tomography algorithms. Machine learning methods require differentiable implementations for end-to-end model training, but PyTorchs built-in operations are too slow for practical use. This paper introduces torch-projectors: a high-performance library for differentiable Fourier-space projections in PyTorch. The library provides 2D and 3D forward and backward projection operators with linear and cubic interpolation, supporting gradient calculation for all inputs. Optimized for CPU, Apple Silicon (MPS), and CUDA devices, torch-projectors outperforms torch-fourier-slice by 1-2 orders of magnitude.

Cryo-electron tomography (cryo-ET) enables in situ structural analysis of macromolecular complexes within native cellular environments. However, the limited field of view required for high-resolution structure determination necessarily restricts a wider assessment of the broader cellular context. We present a multi-magnification cryo-ET acquisition strategy that integrates low- and high-magnification information from coincident sample regions during the same tilt-series. By interleaving acquisition of the magnifications at each tilt angle, this strategy enables simultaneous collection of large field-of-view, low-resolution tomograms and high-resolution, small field-of-view tomograms while minimising the impact of the increased electron dose. We demonstrate that we can capture cellular organisation across tens of microns, while still enabling subtomogram averaging to resolutions below 4 [A]. This integrated acquisition framework establishes a practical route to multi-scale cryo-ET, bridging molecular and cellular scales for more comprehensive biological insight.

Single-particle cryogenic electron microscopy (cryo-EM) has enabled near-atomic resolution structure determination of diverse biomolecules. Because the high vacuum required for electron microscopy prevents the imaging of liquid-phase samples, cryo-EM samples are prepared by plunging the sample into a cryogen, rapidly cooling the sample and suspending the ensemble of biomolecules in a matrix of water glass. However, the effects of this vitrification on the biomolecular ensemble are unknown, complicating efforts to use cryo-EM to derive conformational ensembles of biomolecules. To study these effects, we carried out extensive molecular dynamics simulations (over 50 milliseconds) of the Trp-cage miniprotein at equilibrium and undergoing rapid cooling. We simulated seven cooling rates spanning three orders of magnitude, with the slowest coolings matching experimental rates. By inspecting molecular mobility and density-temperature equations of state for water with and without protein, we found that water vitrification is unaltered by the protein. To track protein conformation changes, and to relate them to conformational kinetics, we made a Markov State Model (MSM) of Trp-cage from 5.4 milliseconds of equilibrium sampling at 277 K. We observed that MSM states with a characteristic time longer than the duration of the non-equilibrium cooling, tend to be more robust to artefacts induced by such cooling. Critically, although we observe that some states vanish in the equilibrium ensemble at 230 K, none do in our nonequilibrium cooled ensembles. However, to account for perturbations induced by nonequilibrium cooling for more labile states, we developed a thermodynamic inference framework for recovering equilibrium populations from the measured vitrified ensembles. These results indicate that cryo-EM has the capacity to be a reliable and accurate biophysical technique for the study of biomolecular ensembles. SignificanceCryogenic electron microscopy images biomolecules trapped in vitreous ice. To vitrify the sample, it must be cooled over the course of 22 microseconds. However, the degree to which this cooling causes the ensemble of the molecules to be perturbed from equilibrium is unknown. Here we present extensive molecular dynamics simulations to quantify the equilibrium dynamics of the Trp-cage miniprotein and the effects of cooling on its conformational ensemble. By simulating cooling at seven different rates, including the slowest experimental rates that still result in vitrification, we connect the kinetic properties of a proteins conformational state to the change in state population from cooling. We show that cooling-induced population shifts are small but observable. We further introduce a thermodynamic-inference method to recover equilibrium populations from the cooled ensembles.

Deep learning has revolutionized structural biology by prediction with near experimental accuracy static protein folds from amino acid sequence alone. However, proteins function as dynamic ensembles of protein conformation states, and current sequence-only models often fail to capture the specific conformational states and heterogeneity dictated by cellular environments or ligand binding. While recent generative models can sample broad conformational landscapes, they remain unconstrained by physical reality, often hallucinating plausible but experimentally invalid states. Here, we present AlphaSAXS, an end-to-end framework that constrains artificial intelligence (AI) inference using Small Angle X-ray Scattering (SAXS) experimental solution scattering data. By integrating real-space pair distance distributions (P(r)) directly into the AlphaFold architecture, AlphaSAXS effectively steers the structural hypothesis toward the experimentally observed structures. We demonstrate that AlphaSAXS resolves documented failure modes of sequence-only models in Apo-Holo transitions, successfully distinguishing between states with identical sequences but distinct scattering profiles. Furthermore, we introduce a hybrid inference protocol that couples deep learning with biophysical hydration modeling, enabling the reconstruction of solution state protein ensembles compatible with experimental data. This work establishes a paradigm for experimentally guided AI, bridging the gap between probabilistic sampling and biophysical measurement.

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.

Cryo-electron tomography (cryo-ET) enables in situ three-dimensional visualization of many protein complexes and other macromolecular assemblies such as ribosomes in cells, yet automated macromolecule particle identification in 3D cryo-ET tomograms remains a major bottleneck due to dose-limited low signal-to-noise ratios, missing-wedge artifacts, and densely crowded cellular backgrounds. We present TomoSwin3D, an end-to-end three-dimensional (3D) macromolecule particle identification and classification pipeline centered on a Swin Transformer-based U-Net that performs particle identification and classification and outputs particle centroid coordinates. TomoSwin3D leverages a multi-channel input representation that augments raw tomogram densities with complementary 3D feature maps capturing edge strength (Sobel gradients), local contrast enhancement (morphological top-hat), and multiscale blob responses (Difference-of-Gaussians), improving detectability of small and low-contrast targets. To better preserve particle geometry and avoid hand-crafted shape assumptions, it adopts occupancy-preserving supervision that directly uses available 3D instance masks rather than heuristic Gaussian/spherical labels and applies scalable patch-wise inference followed by lightweight post-processing (connected-component analysis, size filtering, centroid extraction) for robust centroid coordinate extraction. Across diverse simulated and experimental cryo-ET tomogram benchmarks including SHREC 2021 and 2020 test datasets, EMPIAR dataset, and Cryo-ET data portal dataset, TomoSwin3D achieves strong and consistent performance in detecting proteins and other particles, outperforming existing methods, with a pronounced advantage in picking hard, small protein particles. These results establish TomoSwin3D as a scalable and accurate solution for high-throughput cryo-ET macromolecule particle picking and downstream subtomogram averaging.

Structure elucidation of biological macromolecules by single particle cryogenic electron microscopy (SPA cryo-EM) or cryogenic electron tomography (cryo-ET) relies on low-dose imaging on cryogenic transmission electron microscopes (cryo-TEMs). Routine microscope setup remains technically demanding and can be time-consuming, particularly for inexperienced or infrequent users. We present LowDoseWizard, a guided workflow implemented in SerialEM that enables rapid and standardised setup of cryo-TEM imaging conditions. From minimal user input, the workflow configures microscope optics, camera parameters and image shift settings for all low-dose imaging states, and guides the user through key daily alignment procedures including beam shift offset calibration, objective lens astigmatism correction and coma-free alignment. The workflow is organised into modular routines that can be executed sequentially or independently, while microscope-specific acquisition parameters are defined in editable configuration files, allowing flexible adaptation to different instruments without modification of the core scripts. Across user sessions on three microscopes at EMBL Heidelberg, the complete setup required on average less than 15 minutes. To assess whether predefined imaging conditions generated by the workflow are compatible with high-resolution data collection, we acquired apoferritin data on a 200 kV Glacios and a 300 kV Titan Krios. These datasets yielded reconstructions at 1.62 [A] and 1.09 [A] resolution, respectively, demonstrating that rapid, guided setup can support near-atomic and atomic-resolution single particle cryo-EM. LowDoseWizard lowers the barrier to robust cryo-TEM setup, reduces the time spent on routine parameter selection and alignment, and helps users focus on sample-specific aspects of data acquisition such as target selection. The workflow should be particularly valuable in shared instrumentation environments, where accessibility, reproducibility and efficient microscope use are critical.

We present new tools for the structure determination of amyloid filaments from electron cryo-microscopy (cryo-EM) images. We introduce a new algorithm for automated filament picking, based on their characteristic 4.75 [A] repeat signal; we implement the new auto-picker in a fully automated procedure for on-the-fly pre-processing of cryo-EM data sets of amyloid filaments; we present a graphical tool to select filament types based on bi-hierarchical clustering of filaments and 2D class assignments; and we introduce a denoising neural network for Blush regularisation that is re-trained on amyloid reconstructions. The implementation of these tools in release 5.1 of our open-software package RELION ensures broad applicability. We demonstrate their usefulness on two experimental data sets, including a previously described data set on recombinant human islet amyloid protein (hIAPP) with the S20G mutation for which we identify two new filament types.

Preparing electron-transparent cryo-lamellae is inherently a serial and low-throughput process. Once the lamellae are milled, these thin structures endure both mechanical and thermal stress, and as a result many valuable lamellae crack or even disintegrate entirely. This loss is often regarded as a "lamella tax", i.e. an unavoidable cost of working with such fragile specimens. In this work, we introduce two modifications to the standard lamella-preparation workflow aimed at improving lamella mechanical resistance to crack formation and external stress. The first modification involves milling arrays of perforations directly within the lamella body. These perforations are designed to function as crack-arrest holes, intercepting cracks as they appear and preventing, or at least delaying their further propagation. By slowing crack growth, these features increase the likelihood that the lamella remains intact long enough to complete cryo-TEM imaging. The second modification replaces the conventional rigid attachment of the lamella to the surrounding cellular bulk material with a softer suspension using ring-shaped springs formed by ion beam milling. Mounting the lamella on smooth annular springs provides mechanical compliance both across and along the lamella axis, as well as at intermediate angles and in the out-of-plane direction. This flexibility allows the lamella to accommodate larger stresses and deformations without reaching its mechanical failure threshold. We fabricated a series of test lamellae incorporating different crack-arrest hole geometries, as well as lamellae suspended on soft annular springs. We performed high-resolution cryo-TEM imaging to characterise the perforations themselves and characterised the captured crack geometry within the lamellae at the highest level of detail achieved to date. TEM imaging shows crack interception and guided, non-catastrophic failure paths, while simulations confirm lowered stress in suspended lamellae.

Serial femtosecond crystallography (SFX) has revolutionised structural biology by enabling direct visualization of conformational dynamics of biomacromolecules at near-physiological temperatures. However, conventional SFX sample delivery at X-ray free-electron laser (XFEL) facilities introduces a significant X-ray scattering background, which may limit the data quality and resolution. Here, we introduce an aerosol delivery-based method that drastically reduces the back-ground scattering by minimising the liquid environment surrounding the nanocrystals. We validate our method by solving the structure of Cydia pomonella granulovirus nanocrystals at 1.9 [A] resolution, achieving orders-of-magnitude lower background scattering compared to a liquid jet-based method. Structural comparison with the liquid jet-based model revealed similar overall structure, suggesting that the native structure is largely preserved despite dehydration during aerosolisation. Our method enables efficient SFX studies, particularly pump-probe time-resolved SFX on protein nanocrystals with enhanced signal-to-noise ratios, as well as high-throughput small molecule SFX (smSFX) applications for pharmaceuticals and functional materials.

Single-particle cryogenic electron microscopy (cryoEM) is a widely used technique for structure determination of biomacromolecules to near-atomic resolution. Random distributions of these molecules in vitrified ice are necessary to accumulate enough two-dimensional views to generate a complete three-dimensional (3-D) reconstruction. However, interactions between the sample and the air-water interface (AWI) that occur during vitrification often bias the views of the sample, a phenomenon termed preferred orientation, limiting our ability to obtain 3-D reconstructions. Surfactants are often used as sample additives to prevent AWI-induced deterioration, but no general strategy exists for surfactant choice, requiring laborious screening for each sample. To circumvent these issues, we developed SurfACT, a cocktail of diverse surfactants with distinct physicochemical properties that limits AWI-dependent sample denaturation and orientation bias, while mitigating individual surfactant-specific drawbacks. Here we demonstrate SurfACTs effectiveness with four proteins plagued by AWI-induced issues, including two species of hemagglutinin (HA), molybdenum-iron protein (MoFeP) from the nitrogenase enzyme, and aldolase. All four samples show drastically improved viewing distribution and map completeness when SurfACT is applied. Cryogenic electron tomography demonstrates that SurfACT redistributes particles from the AWI into the bulk ice, driving signal recovery and inhibiting denaturation. This versatile sample additive minimizes sample-specific screening and expands the capabilities and range of suitable samples for cryoEM.

Due to recent technological advances, in situ structural cell biology is becoming a high throughput microscopy technique as all the steps of the workflow, from sample preparation to data analysis, are executed faster, more reliable and more reproducible. Sample thinning by cryoFIB-SEM is an essential tool in preparing electron transparent lamellae of biological specimens suitable for further characterization by cryoET. Modern cryoFIB-SEM instruments can be operated remotely and are capable of automated and unsupervised lamellae preparation. To take full advantage of these developments they need a constant supply of LN2 to maintain cryogenic conditions inside the microscope chamber. Here, we introduce a custom automated LN2 refill system that is compatible with gas cooled cryostages, supports long-term cryoFIB-SEM operations and liberates the user from highly repetitive and manual work. We believe this solution can be utilized with other cryoSEM or cryoFIB-SEM devices requiring N2 gas-flow cooling.

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.