Acta Crystallographica Section D Structural Biology

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.

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.

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.

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.

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.

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.

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.

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.

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.

Macromolecular crystallographic refinement underpins structural biology, yet existing software packages often lack accessible, modular codebases amenable to rapid method development. Here, we introduce TorchRef, a PyTorch-based crystallographic refinement framework that exposes all refinable parameters, atomic coordinates, displacement parameters, occupancies, and scale factors to automatic differentiation. The framework implements FFT-based structure-factor calculations, the French-Wilson treatment of intensities, bulk-solvent modeling with established mask parameters, and stereochemical restraints from the CCP4 Monomer Library. A modular target architecture allows loss functions to be combined, weighted, and extended independently of the core refinement machinery. Validation against 1,000 PDB structures demonstrates that TorchRef-based refinement reproduces a median R-free within 1% of Phenix while maintaining comparable model quality. Structure factor calculation in TorchRef scales readily across multiple CPU cores and is over 100 times faster on modern GPUs than CCTBX. To showcase how modern methods like time-resolved crystallography can benefit from the flexibility that TorchRef provides, we implemented direct refinement of a typical time-resolved model against amplitude differences, a use case currently not explored by classic refinement programs. TorchRef is released under the MIT license with full API documentation and tutorials, providing an accessible platform for developing and testing new crystallographic refinement protocols. SynopsisTorchRef is an open-source PyTorch-based crystallographic refinement framework that exposes all refinable parameters to automatic differentiation, delivers GPU-accelerated structure-factor evaluation more than 100x faster than CCTBX, and enables new workflows, such as direct refinement against amplitude differences in time-resolved crystallography.

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.

In an infection, Hepatitis B Virus (HBV) core protein (HBc) normally assembles into icosahedral capsids. Capsid Assembly Modulators (CAMs) are direct acting antivirals that induce HBc mis-assembly and are the subject of active research and development. Two versions of HBc are used in structural studies of CAM-HBc complexes: Cp150 and Cp149-Y132A. Cp150 forms empty icosahedral capsids that are structurally indistinguishable from those found in virions. The Y132A mutation of Cp149 leads to an assembly defective soluble protein that crystalizes as flat hexagonal sheets, where the hexagons resemble icosahedral quasi-sixfold vertices. In this study, we compare structures of CAM-bound Cp150 to CAM-bound Cp149-Y132A. In capsids, the residues forming the CAM site shift to match the structure of bound CAMs, an induced fit. In Cp149-Y132A crystals, CAM sites show little structural adjustment in response to different CAMs binding. In turn, the array of residues that interact with CAMs varies from CAM to CAM in capsid structures but remains nearly constant in Cp149-Y132A crystals. These results illustrate important differences between CAM binding in Cp149-Y132A and Cp150 structures that will contribute to future CAM design.

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.

Virtual reality (VR) offers immersive and interactive learning environments that can improve student engagement and 3D visualization. However, its application in medical education is mostly limited to clinical settings and its potential for better understanding complex concepts, or empathy with the patients, remains underexplored. Here, we describe SIGHT (Simulated Immersive Guidance for Human Training), an immersive VR application, designed to teach core concepts in the physics and functional neuroanatomy, or neurophysiology of human vision. Its two integrated learning modules allow first-person experience of normal and pathological conditions: the optics module enables users to manipulate lenses, experience refractive errors such as myopia, presbyopia, and astigmatism and correct them through appropriate lens selection; the neurophysiology module allows learners to navigate the visual pathways from the retina to the visual cortex and to simulate lesions, experiencing the corresponding visual field deficits. User authentication and interactive evaluation steps provide analytical feedback of the experience and learning process. A pilot group of medical students reported high usability, engagement and deeper understanding of the vision-related concepts, showing how the approach of SIGHT can support experiential learning in medical education.

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.

Mycobacterium tuberculosis (Mtb), the causative agent of tuberculosis, can use host derived lipids as carbon and energy source for survival. Mammalian cell entry (Mce) associated membrane (Mam) proteins are important for the stability of lipid importing Mce complexes. Mtb has five homologs of Mam proteins referred as orphaned Mam (OmamA-E) proteins. A recent study suggested that OmamC (Rv1363c) is essential for the storage and utilization of lipids under starvation in Mtb. To understand the structure and interactions of OmamC, we generated a truncated soluble variant of OmamC (OmamC129-261). Here, we report on the challenges encountered during the crystallization and structure determination of OmamC129-261 and the strategies applied to overcome them. Despite the AlphaFold2 predicted model proving an initial molecular replacement solution, experimental phasing was necessary to determine the structure of OmamC129-261. Heat treatment of protein prior to crystallization setup removed partially unfolded protein present and played a critical role in enhancing the reproducibility and diffraction quality of OmamC129-261 crystals. Although reported earlier, it is not a widely used method. It is worth to try this method, especially, when faced with poor reproducibility and diffraction of crystals.

Plant-specific membrane receptor kinases with structurally diverse extracellular domains regulate key processes in plant growth, development, immunity and symbiosis. Structural studies of these glycoproteins are often hampered by the limited quantities in which they can be obtained. Here, we describe the LRR crystallization screen, which has enabled the successful crystallization and structure determination of multiple receptor kinase ectodomains, including ligand-and co-receptor-bound complexes. As an example, we report the 1.5 [A] resolution crystal structure of the leucine-rich repeat (LRR) domain of STRUBBELIG-RECEPTOR FAMILY 6 (SRF6) from Arabidopsis thaliana. The SRF6 ectodomain contains seven LRRs and a disulfide-bond-stabilised N-terminal capping domain but lacks the canonical C-terminal cap and the N-glycosylation pattern typically observed in other family members. Previously reported protein-protein interactions between the SRF6 and SRF7 ectodomains and the receptor kinases BRI1, BRL1, BRL3, SERK3 and BIR1-3 could not be confirmed by quantitative isothermal titration calorimetry and grating-coupled interferometry assays, suggesting that these structurally conserved LRR receptor kinases may have signalling functions outside the brassinosteroid pathway. SynopsisA crystallisation screen that has enabled the structural analysis of various extracellular domains of plant membrane receptor kinases is described together.

AbstractThe development of machine learning models for protein-ligand interactions is fundamentally constrained by the quality and diversity of available structural data. Existing databases of protein-ligand complexes present researchers with an unsatisfying trade-off: carefully curated collections such as PDBBind and HiQBind offer high structural reliability but cover only a narrow slice of the Protein Data Bank (PDB), while large-scale resources like PLInder provide broad coverage at the expense of rigorous quality control. Here, we introduce CROWN (Curated Repository Of Well-resolved Non-covalent interactions), a machine learning-ready dataset that reconciles this tension by applying a comprehensive, fully automated preprocessing pipeline to the PLInder database. Starting from 649,915 protein-ligand interaction systems, CROWN applies a series of interleaved quality filters and processing stages addressing crystallographic resolution, ligand identity, pocket completeness, structural repair, interaction quality, and protonation at physiological pH. A distinguishing feature of the pipeline is a final constrained energy minimisation step using custom flat-bottomed restraints, which balances crystallographic evidence with relaxation of intramolecular strain. This step -- absent from existing protein-ligand datasets -- produces structurally uniform complexes by reconciling the heterogeneous refinement practices of different crystallographers and structure determination protocols, without distorting the experimentally observed binding geometry. The resulting dataset of 153,005 complexes represents a roughly four-fold increase in protein and species diversity over PDBBind and HiQBind, while maintaining rigorous structural standards. Importantly, CROWN adopts a geometry-centric design philosophy that treats the 3D arrangement of atoms at the binding interface as a self-consistent source of information, rather than relying on externally measured binding affinities that cover only a fraction of known structures and introduce well-documented biases. We anticipate that CROWN will serve as a broadly useful resource for training generative models of protein-ligand binding poses, developing scoring functions, and benchmarking interaction prediction methods.

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.

Optical microscopy is fundamental to modern life-science research, yet interpreting its results requires precise modelling of point spread functions (PSFs) within complex environments. This manuscript introduces a versatile and efficient approach to wave-optical PSF calculations that extends existing frameworks by incorporating detection PSF modelling through the principle of reciprocity. Accompanying this work is a free MATLAB software package centred on a single, minimalistic core function, PlaneWaveExc.m, which utilizes a plane-wave superposition based on the Richards-Wolf model. Despite its simplicity, the framework accounts for "real-life" complexities such as systemic aberrations, arbitrary amplitude and phase modulations, and stratified media with complex-valued refractive indices. We demonstrate the softwares broad applicability through diverse case studies, including single-molecule imaging, STED microscopy, the segmented aperture of the James Webb Space Telescope, and coherent wide-field iSCAT microscopy. Each example is supported by dedicated scripts to facilitate adaptation for specific research needs.