Low-molecular-weight (LMW) thiols are involved in many processes in all organisms, playing a protective role against reactive species, heavy metals, toxins and antibiotics. Actinobacteria, such as Mycobacterium tuberculosis, use the LMW thiol mycothiol (MSH) to buffer the intracellular redox environment. The NADPH-dependent FAD-containing oxidoreductase mycothiol disulfide reductase (Mtr) is known to reduce oxidized mycothiol disulfide (MSSM) to MSH, which is crucial to maintain the cellular redox balance. In this work, the first crystal structures of Mtr are presented, expanding the structural knowledge and understanding of LMW thiol reductases. The structural analyses and docking calculations provide insight into the nature of Mtrs, with regard to the binding and reduction of the MSSM substrate, in the context of related oxidoreductases. The putative binding site for MSSM suggests a similar binding to that described for the homologous glutathione reductase and its respective substrate glutathione disulfide, but with distinct structural differences shaped to fit the bulkier MSSM substrate, assigning Mtrs as uniquely functioning reductases. As MSH has been acknowledged as an attractive antitubercular target, the structural findings presented in this work may contribute towards future antituberculosis drug development.

Mevalonate kinase is central to the isoprenoid biosynthesis pathway. Here, high-resolution X-ray crystal structures of two mevalonate kinases are presented: a eukaryotic protein from Ramazzottius varieornatus and an archaeal protein from Methanococcoides burtonii. Both enzymes possess the highly conserved motifs of the GHMP enzyme superfamily, with notable differences between the two enzymes in the N-terminal part of the structures. Biochemical characterization of the two enzymes revealed major differences in their sensitivity to geranyl pyrophosphate and farnesyl pyrophosphate, and in their thermal stabilities. This work adds to the understanding of the structural basis of enzyme inhibition and thermostability in mevalonate kinases.

The validation of structural models obtained by macromolecular X-ray crystallography against experimental diffraction data, whether before deposition into the PDB or after, is typically carried out exclusively against the merged data that are eventually archived along with the atomic coordinates. It is shown here that the availability of unmerged reflection data enables valuable additional analyses to be performed that yield improvements in the final models, and tools are presented to implement them, together with examples of the results to which they give access. The first example is the automatic identification and removal of image ranges affected by loss of crystal centering or by excessive decay of the diffraction pattern as a result of radiation damage. The second example is the `reflection-auditing' process, whereby individual merged data items showing especially poor agreement with model predictions during refinement are investigated thanks to the specific metadata (such as image number and detector position) that are available for the corresponding unmerged data, potentially revealing previously undiagnosed instrumental, experimental or processing problems. The third example is the calculation of so-called F(early) − F(late) maps from carefully selected subsets of unmerged amplitude data, which can not only highlight the location and extent of radiation damage but can also provide guidance towards suitable fine-grained parametrizations to model the localized effects of such damage.

The widespread adoption of cryoEM technologies for structural biology has pushed the discipline to new frontiers. A significant worldwide effort has refined the single-particle analysis (SPA) workflow into a reasonably standardized procedure. Significant investments of development time have been made, particularly in sample preparation, microscope data-collection efficiency, pipeline analyses and data archiving. The widespread adoption of specific commercial microscopes, software for controlling them and best practices developed at facilities worldwide has also begun to establish a degree of standardization to data structures coming from the SPA workflow. There is opportunity to capitalize on this moment in the maturation of the field, to capture metadata from SPA experiments and correlate the metadata with experimental outcomes, which is presented here in a set of programs called EMinsight. This tool aims to prototype the framework and types of analyses that could lead to new insights into optimal microscope configurations as well as to define methods for metadata capture to assist with the archiving of cryoEM SPA data. It is also envisaged that this tool will be useful to microscope operators and facilities looking to rapidly generate reports on SPA data-collection and screening sessions.

Lanthanide ions have ideal chemical properties for catalysis, such as hard Lewis acidity, fast ligand-exchange kinetics, high coordination-number preferences and low geometric requirements for coordination. As a result, many small-molecule lanthanide catalysts have been described in the literature. Yet, despite the ability of enzymes to catalyse highly stereoselective reactions under gentle conditions, very few lanthanoenzymes have been investigated. In this work, the mononuclear binding of europium(III) and gadolinium(III) to the active site of a mutant of the model enzyme phosphotriesterase are described using X-ray crystallography at 1.78 and 1.61 Å resolution, respectively. It is also shown that despite coordinating a single non-natural metal cation, the PTE-R18 mutant is still able to maintain esterase activity.

Diffuse scattering is a promising method to gain additional insight into protein dynamics from macromolecular crystallography experiments. Bragg intensities yield the average electron density, while the diffuse scattering can be processed to obtain a three-dimensional reciprocal-space map that is further analyzed to determine correlated motion. To make diffuse scattering techniques more accessible, software for data processing called mdx2 has been created that is both convenient to use and simple to extend and modify. mdx2 is written in Python, and it interfaces with DIALS to implement self-contained data-reduction workflows. Data are stored in NeXus format for software interchange and convenient visualization. mdx2 can be run on the command line or imported as a package, for instance to encapsulate a complete workflow in a Jupyter notebook for reproducible computing and education. Here, mdx2 version 1.0 is described, a new release incorporating state-of-the-art techniques for data reduction. The implementation of a complete multi-crystal scaling and merging workflow is described, and the methods are tested using a high-redundancy data set from cubic insulin. It is shown that redundancy can be leveraged during scaling to correct systematic errors and obtain accurate and reproducible measurements of weak diffuse signals.

The Swiss Light Source facilitates fragment-based drug-discovery campaigns for academic and industrial users through the Fast Fragment and Compound Screening (FFCS) software suite. This framework is further enriched by the option to utilize the Smart Digital User (SDU) software for automated data collection across the PXI, PXII and PXIII beamlines. In this work, the newly developed HEIDI webpage (https://heidi.psi.ch) is introduced: a platform crafted using state-of-the-art software architecture and web technologies for sample management of rotational data experiments. The HEIDI webpage features a data-review tab for enhanced result visualization and provides programmatic access through a representational state transfer application programming interface (REST API). The migration of the local FFCS MongoDB instance to the cloud is highlighted and detailed. This transition ensures secure, encrypted and consistently accessible data through a robust and reliable REST API tailored for the FFCS software suite. Collectively, these advancements not only significantly elevate the user experience, but also pave the way for future expansions and improvements in the capabilities of the system.

Cryo-electron tomography (cryo-ET) enables molecular-resolution 3D imaging of complex biological specimens such as viral particles, cellular sections and, in some cases, whole cells. This enables the structural characterization of molecules in their near-native environments, without the need for purification or separation, thereby preserving biological information such as conformational states and spatial relationships between different molecular species. Subtomogram averaging is an image-processing workflow that allows users to leverage cryo-ET data to identify and localize target molecules, determine high-resolution structures of repeating molecular species and classify different conformational states. Here, STOPGAP, an open-source package for subtomogram averaging that is designed to provide users with fine control over each of these steps, is described. In providing detailed descriptions of the image-processing algorithms that STOPGAP uses, this manuscript is also intended to serve as a technical resource to users as well as for further community-driven software development.

Metalloproteins are ubiquitous in all living organisms and take part in a very wide range of biological processes. For this reason, their experimental characterization is crucial to obtain improved knowledge of their structure and biological functions. The three-dimensional structure represents highly relevant information since it provides insight into the interaction between the metal ion(s) and the protein fold. Such interactions determine the chemical reactivity of the bound metal. The available PDB structures can contain errors due to experimental factors such as poor resolution and radiation damage. A lack of use of distance restraints during the refinement and validation process also impacts the structure quality. Here, the aim was to obtain a thorough overview of the distribution of the distances between metal ions and their donor atoms through the statistical analysis of a data set based on more than 115 000 metal-binding sites in proteins. This analysis not only produced reference data that can be used by experimentalists to support the structure-determination process, for example as refinement restraints, but also resulted in an improved insight into how protein coordination occurs for different metals and the nature of their binding interactions. In particular, the features of carboxylate coordination were inspected, which is the only type of interaction that is commonly present for nearly all metals.

CdaA is the most widespread diadenylate cyclase in many bacterial species, including several multidrug-resistant human pathogens. The enzymatic product of CdaA, cyclic di-AMP, is a secondary messenger that is essential for the viability of many bacteria. Its absence in humans makes CdaA a very promising and attractive target for the development of new antibiotics. Here, the structural results are presented of a crystallographic fragment screen against CdaA from Listeria monocytogenes, a saprophytic Gram-positive bacterium and an opportunistic food-borne pathogen that can cause listeriosis in humans and animals. Two of the eight fragment molecules reported here were localized in the highly conserved ATP-binding site. These fragments could serve as potential starting points for the development of antibiotics against several CdaA-dependent bacterial species.

Fragment-based drug design using X-ray crystallography is a powerful technique to enable the development of new lead compounds, or probe molecules, against biological targets. This study addresses the need to determine fragment binding orientations for low-occupancy fragments with incomplete electron density, an essential step before further development of the molecule. Halogen atoms play multiple roles in drug discovery due to their unique combination of electronegativity, steric effects and hydrophobic properties. Fragments incorporating halogen atoms serve as promising starting points in hit-to-lead development as they often establish halogen bonds with target proteins, potentially enhancing binding affinity and selectivity, as well as counteracting drug resistance. Here, the aim was to unambiguously identify the binding orientations of fragment hits for SARS-CoV-2 nonstructural protein 1 (nsp1) which contain a combination of sulfur and/or chlorine, bromine and iodine substituents. The binding orientations of carefully selected nsp1 analogue hits were focused on by employing their anomalous scattering combined with Pan-Dataset Density Analysis (PanDDA). Anomalous difference Fourier maps derived from the diffraction data collected at both standard and long-wavelength X-rays were compared. The discrepancies observed in the maps of iodine-containing fragments collected at different energies were attributed to site-specific radiation-damage stemming from the strong X-ray absorption of I atoms, which is likely to cause cleavage of the C—I bond. A reliable and effective data-collection strategy to unambiguously determine the binding orientations of low-occupancy fragments containing sulfur and/or halogen atoms while mitigating radiation damage is presented.

The expansive scientific software ecosystem, characterized by millions of titles across various platforms and formats, poses significant challenges in maintaining reproducibility and provenance in scientific research. The diversity of independently developed applications, evolving versions and heterogeneous components highlights the need for rigorous methodologies to navigate these complexities. In response to these challenges, the SBGrid team builds, installs and configures over 530 specialized software applications for use in the on-premises and cloud-based computing environments of SBGrid Consortium members. To address the intricacies of supporting this diverse application collection, the team has developed the Capsule Software Execution Environment, generally referred to as Capsules. Capsules rely on a collection of programmatically generated bash scripts that work together to isolate the runtime environment of one application from all other applications, thereby providing a transparent cross-platform solution without requiring specialized tools or elevated account privileges for researchers. Capsules facilitate modular, secure software distribution while maintaining a centralized, conflict-free environment. The SBGrid platform, which combines Capsules with the SBGrid collection of structural biology applications, aligns with FAIR goals by enhancing the findability, accessibility, interoperability and reusability of scientific software, ensuring seamless functionality across diverse computing environments. Its adaptability enables application beyond structural biology into other scientific fields.

When solving a structure of a protein from single-wavelength anomalous diffraction X-ray data, the initial phases obtained by phasing from an anomalously scattering substructure usually need to be improved by an iterated electron-density modification. In this manuscript, the use of convolutional neural networks (CNNs) for segmentation of the initial experimental phasing electron-density maps is proposed. The results reported demonstrate that a CNN with U-net architecture, trained on several thousands of electron-density maps generated mainly using X-ray data from the Protein Data Bank in a supervised learning, can improve current density-modification methods.

The formation of a vitrified thin film embedded with randomly oriented macromolecules is an essential prerequisite for cryogenic sample electron microscopy. Most commonly, this is achieved using the plunge-freeze method first described nearly 40 years ago. Although this is a robust method, the behaviour of different macromolecules shows great variation upon freezing and often needs to be optimized to obtain an isotropic, high-resolution reconstruction. For a macromolecule in such a film, the probability of encountering the air–water interface in the time between blotting and freezing and adopting preferred orientations is very high. 3D reconstruction using preferentially oriented particles often leads to anisotropic and uninterpretable maps. Currently, there are no general solutions to this prevalent issue, but several approaches largely focusing on sample preparation with the use of additives and novel grid modifications have been attempted. In this study, the effect of physical and chemical factors on the orientations of macromolecules was investigated through an analysis of selected well studied macromolecules, and important parameters that determine the behaviour of proteins on cryo-EM grids were revealed. These insights highlight the nature of the interactions that cause preferred orientations and can be utilized to systematically address orientation bias for any given macromolecule and to provide a framework to design small-molecule additives to enhance sample stability and behaviour.

Protein crystallography is an established method to study the atomic structures of macromolecules and their complexes. A prerequisite for successful structure determination is diffraction-quality crystals, which may require extensive optimization of both the protein and the conditions, and hence projects can stretch over an extended period, with multiple users being involved. The workflow from crystallization and crystal treatment to deposition and publication is well defined, and therefore an electronic laboratory information management system (LIMS) is well suited to management of the data. Completion of the project requires key information on all the steps being available and this information should also be made available according to the FAIR principles. As crystallized samples are typically shipped between facilities, a key feature to be captured in the LIMS is the exchange of metadata between the crystallization facility of the home laboratory and, for example, synchrotron facilities. On completion, structures are deposited in the Protein Data Bank (PDB) and the LIMS can include the PDB code in its database, completing the chain of custody from crystallization to structure deposition and publication. A LIMS designed for macromolecular crystallography, IceBear, is available as a standalone installation and as a hosted service, and the implementation of key features for the capture of metadata in IceBear is discussed as an example.

Histidine can be protonated on either or both of the two N atoms of the imidazole moiety. Each of the three possible forms occurs as a result of the stereochemical environment of the histidine side chain. In an atomic model, comparing the possible protonation states in situ, looking at possible hydrogen bonding and metal coordination, it is possible to predict which is most likely to be correct. A more direct method is described that uses quantum-mechanical methods to calculate, also in situ, the minimum geometry and energy for comparison, and therefore to more accurately identify the most likely proton­ation state.

The Mycobacterium tuberculosis trifunctional enzyme (MtTFE) is an α2β2 tetrameric enzyme in which the α-chain harbors the 2E-enoyl-CoA hydratase (ECH) and 3S-hydroxyacyl-CoA dehydrogenase (HAD) active sites, and the β-chain provides the 3-ketoacyl-CoA thiolase (KAT) active site. Linear, medium-chain and long-chain 2E-enoyl-CoA molecules are the preferred substrates of MtTFE. Previous crystallographic binding and modeling studies identified binding sites for the acyl-CoA substrates at the three active sites, as well as the NAD binding pocket at the HAD active site. These studies also identified three additional CoA binding sites on the surface of MtTFE that are different from the active sites. It has been proposed that one of these additional sites could be of functional relevance for the substrate channeling (by surface crawling) of reaction intermediates between the three active sites. Here, 226 fragments were screened in a crystallographic fragment-binding study of MtTFE crystals, resulting in the structures of 16 MtTFE–fragment complexes. Analysis of the 121 fragment-binding events shows that the ECH active site is the `binding hotspot' for the tested fragments, with 41 binding events. The mode of binding of the fragments bound at the active sites provides additional insight into how the long-chain acyl moiety of the substrates can be accommodated at their proposed binding pockets. In addition, the 20 fragment-binding events between the active sites identify potential transient binding sites of reaction intermediates relevant to the possible channeling of substrates between these active sites. These results provide a basis for further studies to understand the functional relevance of the latter binding sites and to identify substrates for which channeling is crucial.

Advances in structural biology have relied heavily on synchrotron cryo-crystallography and cryogenic electron microscopy to elucidate biological processes and for drug discovery. However, disparities between cryogenic and room-temperature (RT) crystal structures pose challenges. Here, Cryo2RT, a high-throughput RT data-collection method from cryo-cooled crystals that leverages the cryo-crystallography workflow, is introduced. Tested on endothiapepsin crystals with four soaked fragments, thaumatin and SARS-CoV-2 3CLpro, Cryo2RT reveals unique ligand-binding poses, offers a comparable throughput to cryo-crystallography and eases the exploration of structural dynamics at various temperatures.

The interpretation of cryo-EM maps often includes the docking of known or predicted structures of the components, which is particularly useful when the map resolution is worse than 4 Å. Although it can be effective to search the entire map to find the best placement of a component, the process can be slow when the maps are large. However, frequently there is a well-founded hypothesis about where particular components are located. In such cases, a local search using a map subvolume will be much faster because the search volume is smaller, and more sensitive because optimizing the search volume for the rotation-search step enhances the signal to noise. A Fourier-space likelihood-based local search approach, based on the previously published em_placement software, has been implemented in the new emplace_local program. Tests confirm that the local search approach enhances the speed and sensitivity of the computations. An interactive graphical interface in the ChimeraX molecular-graphics program provides a convenient way to set up and evaluate docking calculations, particularly in defining the part of the map into which the components should be placed.

AlphaFold2 has revolutionized structural biology by offering unparalleled accuracy in predicting protein structures. Traditional methods for determining protein structures, such as X-ray crystallography and cryo-electron microscopy, are often time-consuming and resource-intensive. AlphaFold2 provides models that are valuable for molecular replacement, aiding in model building and docking into electron density or potential maps. However, despite its capabilities, models from AlphaFold2 do not consistently match the accuracy of experimentally determined structures, need to be validated experimentally and currently miss some crucial information, such as post-translational modifications, ligands and bound ions. In this paper, the advantages are explored of collecting X-ray anomalous data to identify chemical elements, such as metal ions, which are key to understanding certain structures and functions of proteins. This is achieved through methods such as calculating anomalous difference Fourier maps or refining the imaginary component of the anomalous scattering factor f''. Anomalous data can serve as a valuable complement to the information provided by AlphaFold2 models and this is particularly significant in elucidating the roles of metal ions.

A group of three deep-learning tools, referred to collectively as CHiMP (Crystal Hits in My Plate), were created for analysis of micrographs of protein crystallization experiments at the Diamond Light Source (DLS) synchrotron, UK. The first tool, a classification network, assigns images into categories relating to experimental outcomes. The other two tools are networks that perform both object detection and instance segmentation, resulting in masks of individual crystals in the first case and masks of crystallization droplets in addition to crystals in the second case, allowing the positions and sizes of these entities to be recorded. The creation of these tools used transfer learning, where weights from a pre-trained deep-learning network were used as a starting point and repurposed by further training on a relatively small set of data. Two of the tools are now integrated at the VMXi macromolecular crystallography beamline at DLS, where they have the potential to absolve the need for any user input, both for monitoring crystallization experiments and for triggering in situ data collections. The third is being integrated into the XChem fragment-based drug-discovery screening platform, also at DLS, to allow the automatic targeting of acoustic compound dispensing into crystallization droplets.

The availability of highly accurate protein structure predictions from AlphaFold2 (AF2) and similar tools has hugely expanded the applicability of molecular replacement (MR) for crystal structure solution. Many structures can be solved routinely using raw models, structures processed to remove unreliable parts or models split into distinct structural units. There is therefore an open question around how many and which cases still require experimental phasing methods such as single-wavelength anomalous diffraction (SAD). Here, this question is addressed using a large set of PDB depositions that were solved by SAD. A large majority (87%) could be solved using unedited or minimally edited AF2 predictions. A further 18 (4%) yield straightforwardly to MR after splitting of the AF2 prediction using Slice'N'Dice, although different splitting methods succeeded on slightly different sets of cases. It is also found that further unique targets can be solved by alternative modelling approaches such as ESMFold (four cases), alternative MR approaches such as ARCIMBOLDO and AMPLE (two cases each), and multimeric model building with AlphaFold-Multimer or UniFold (three cases). Ultimately, only 12 cases, or 3% of the SAD-phased set, did not yield to any form of MR tested here, offering valuable hints as to the number and the characteristics of cases where experimental phasing remains essential for macromolecular structure solution.

Most scientific facilities produce large amounts of heterogeneous data at a rapid pace. Managing users, instruments, reports and invoices presents additional challenges. To address these challenges, EMhub, a web platform designed to support the daily operations and record-keeping of a scientific facility, has been introduced. EMhub enables the easy management of user information, instruments, bookings and projects. The application was initially developed to meet the needs of a cryoEM facility, but its functionality and adaptability have proven to be broad enough to be extended to other data-generating centers. The expansion of EMHub is enabled by the modular nature of its core functionalities. The application allows external processes to be connected via a REST API, automating tasks such as folder creation, user and password generation, and the execution of real-time data-processing pipelines. EMhub has been used for several years at the Swedish National CryoEM Facility and has been installed in the CryoEM center at the Structural Biology Department at St. Jude Children's Research Hospital. A fully automated single-particle pipeline has been implemented for on-the-fly data processing and analysis. At St. Jude, the X-Ray Crystallography Center and the Single-Molecule Imaging Center have already expanded the platform to support their operational and data-management workflows.

Fatty acid-derivative prodrugs have been utilized extensively to improve the physicochemical, biopharmaceutical and pharmacokinetic properties of active pharmaceutical ingredients. However, to our knowledge, the crystallization behavior of prodrugs modified with different fatty acids has not been explored. In the present work, a series of paliperidone aliphatic prodrugs with alkyl chain lengths ranging from C4 to C16 was investigated with respect to crystal structure, crystal morphology and crystallization kinetics. The paliperidone derivatives exhibited isostructural crystal packing, despite the different alkyl chain lengths, and crystallized with the dominant (100) face in both melt and solution. The rate of crystallization for paliperidone derivatives in the melt increases with alkyl chain length owing to greater molecular mobility. In contrast, the longer chains prolong the nucleation induction time and reduce the crystal growth kinetics in solution. The results show a correlation between difficulty of nucleation in solution and the interfacial energy. This work provides insight into the crystallization behavior of paliperidone aliphatic prodrugs and reveals that the role of alkyl chain length in the crystallization behavior has a strong dependence on the crystallization method.

Our study compares short-range order parameters refined from the diffuse scattering in single-crystal X-ray and single-crystal electron diffraction data. Nb0.84CoSb was chosen as a reference material. The correlations between neighbouring vacancies and the displacements of Sb and Co atoms were refined from the diffuse scattering using a Monte Carlo refinement in DISCUS. The difference between the Sb and Co displacements refined from the diffuse scattering and the Sb and Co displacements refined from the Bragg reflections in single-crystal X-ray diffraction data is 0.012 (7) Å for the refinement on diffuse scattering in single-crystal X-ray diffraction data and 0.03 (2) Å for the refinement on the diffuse scattering in single-crystal electron diffraction data. As electron diffraction requires much smaller crystals than X-ray diffraction, this opens up the possibility of refining short-range order parameters in many technologically relevant materials for which no crystals large enough for single-crystal X-ray diffraction are available.

Molecular structures can be determined in vitro and in situ with cryo-electron microscopy (cryo-EM). Specimen preparation is a major obstacle in cryo-EM. Typical sample preparation is orders of magnitude slower than biological processes. Time-resolved cryo-EM (TR-cryo-EM) can capture short-lived states. Here, Cryo-EM sample preparation with light-activated molecules (C-SPAM) is presented, an open-source, photochemistry-coupled device for TR-cryo-EM that enables millisecond resolution and tunable timescales across broad biological applications.

Highly accurate protein structure prediction can generate accurate models of protein and protein–protein complexes in X-ray crystallography. However, the question of how to make more effective use of predicted models for completing structure analysis, and which strategies should be employed for the more challenging cases such as multi-helical structures, multimeric structures and extremely large structures, both in the model preparation and in the completion steps, remains open for discussion. In this paper, a new strategy is proposed based on the framework of direct methods and dual-space iteration, which can greatly simplify the pre-processing steps of predicted models both in normal and in challenging cases. Following this strategy, full-length models or the conservative structural domains could be used directly as the starting model, and the phase error and the model bias between the starting model and the real structure would be modified in the direct-methods-based dual-space iteration. Many challenging cases (from CASP14) have been tested for the general applicability of this constructive strategy, and almost complete models have been generated with reasonable statistics. The hybrid strategy therefore provides a meaningful scheme for X-ray structure determination using a predicted model as the starting point.

In January 2020, a workshop was held at EMBL-EBI (Hinxton, UK) to discuss data requirements for the deposition and validation of cryoEM structures, with a focus on single-particle analysis. The meeting was attended by 47 experts in data processing, model building and refinement, validation, and archiving of such structures. This report describes the workshop's motivation and history, the topics discussed, and the resulting consensus recommendations. Some challenges for future methods-development efforts in this area are also highlighted, as is the implementation to date of some of the recommendations.

Dynamical refinement is a well established method for refining crystal structures against 3D electron diffraction (ED) data and its benefits have been discussed in the literature [Palatinus, Petříček & Corrêa, (2015). Acta Cryst. A71, 235–244; Palatinus, Corrêa et al. (2015). Acta Cryst. B71, 740–751]. However, until now, dynamical refinements have only been conducted using the independent atom model (IAM). Recent research has shown that a more accurate description can be achieved by applying the transferable aspherical atom model (TAAM), but this has been limited only to kinematical refinements [Gruza et al. (2020). Acta Cryst. A76, 92–109; Jha et al. (2021). J. Appl. Cryst. 54, 1234–1243]. In this study, we combine dynamical refinement with TAAM for the crystal structure of 1-methyl­uracil, using data from precession ED. Our results show that this approach improves the residual Fourier electrostatic potential and refinement figures of merit. Furthermore, it leads to systematic changes in the atomic displacement parameters of all atoms and the positions of hydrogen atoms. We found that the refinement results are sensitive to the parameters used in the TAAM modelling process. Though our results show that TAAM offers superior performance compared with IAM in all cases, they also show that TAAM parameters obtained by periodic DFT calculations on the refined structure are superior to the TAAM parameters from the UBDB/MATTS database. It appears that multipolar parameters transferred from the database may not be sufficiently accurate to provide a satisfactory description of all details of the electrostatic potential probed by the 3D ED experiment.

Serial crystallography requires large numbers of microcrystals and robust strategies to rapidly apply substrates to initiate reactions in time-resolved studies. Here, we report the use of droplet miniaturization for the controlled production of uniform crystals, providing an avenue for controlled substrate addition and synchronous reaction initiation. The approach was evaluated using two enzymatic systems, yielding 3 µm crystals of lysozyme and 2 µm crystals of Pdx1, an Arabidopsis enzyme involved in vitamin B6 biosynthesis. A seeding strategy was used to overcome the improbability of Pdx1 nucleation occurring with diminishing droplet volumes. Convection within droplets was exploited for rapid crystal mixing with ligands. Mixing times of <2 ms were achieved. Droplet microfluidics for crystal size engineering and rapid micromixing can be utilized to advance time-resolved serial crystallography.

Here, a machine-learning method based on a kinetically informed neural network (NN) is introduced. The proposed method is designed to analyze a time series of difference electron-density maps from a time-resolved X-ray crystallographic experiment. The method is named KINNTREX (kinetics-informed NN for time-resolved X-ray crystallography). To validate KINNTREX, multiple realistic scenarios were simulated with increasing levels of complexity. For the simulations, time-resolved X-ray data were generated that mimic data collected from the photocycle of the photoactive yellow protein. KINNTREX only requires the number of intermediates and approximate relaxation times (both obtained from a singular valued decomposition) and does not require an assumption of a candidate mechanism. It successfully predicts a consistent chemical kinetic mechanism, together with difference electron-density maps of the intermediates that appear during the reaction. These features make KINNTREX attractive for tackling a wide range of biomolecular questions. In addition, the versatility of KINNTREX can inspire more NN-based applications to time-resolved data from biological macromolecules obtained by other methods.

Metal-based complexes with their unique chemical properties, including multiple oxidation states, radio-nuclear capabilities and various coordination geometries yield value as potential pharmaceuticals. Understanding the interactions between metals and biological systems will prove key for site-specific coordination of new metal-based lead compounds. This study merges the concepts of target coordination with fragment-based drug methodologies, supported by varying the anomalous scattering of rhenium along with infrared spectroscopy, and has identified rhenium metal sites bound covalently with two amino acid types within the model protein. A time-based series of lysozyme-rhenium-imidazole (HEWL-Re-Imi) crystals was analysed systematically over a span of 38 weeks. The main rhenium covalent coordination is observed at His15, Asp101 and Asp119. Weak (i.e. noncovalent) interactions are observed at other aspartic, asparagine, proline, tyrosine and tryptophan side chains. Detailed bond distance comparisons, including precision estimates, are reported, utilizing the diffraction precision index supplemented with small-molecule data from the Cambridge Structural Database. Key findings include changes in the protein structure induced at the rhenium metal binding site, not observed in similar metal-free structures. The binding sites are typically found along the solvent-channel-accessible protein surface. The three primary covalent metal binding sites are consistent throughout the time series, whereas binding to neighbouring amino acid residues changes through the time series. Co-crystallization was used, consistently yielding crystals four days after setup. After crystal formation, soaking of the compound into the crystal over 38 weeks is continued and explains these structural adjustments. It is the covalent bond stability at the three sites, their proximity to the solvent channel and the movement of residues to accommodate the metal that are important, and may prove useful for future radiopharmaceutical development including target modification.

The Protein Data Bank (PDB) was established as the first open-access digital data resource in biology and medicine in 1971 with seven X-ray crystal structures of proteins. Today, the PDB houses >210 000 experimentally determined, atomic level, 3D structures of proteins and nucleic acids as well as their complexes with one another and small molecules (e.g. approved drugs, enzyme cofactors). These data provide insights into fundamental biology, biomedicine, bioenergy and biotechnology. They proved particularly important for understanding the SARS-CoV-2 global pandemic. The US-funded Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB) and other members of the Worldwide Protein Data Bank (wwPDB) partnership jointly manage the PDB archive and support >60 000 `data depositors' (structural biologists) around the world. wwPDB ensures the quality and integrity of the data in the ever-expanding PDB archive and supports global open access without limitations on data usage. The RCSB PDB research-focused web portal at https://www.rcsb.org/ (RCSB.org) supports millions of users worldwide, representing a broad range of expertise and interests. In addition to retrieving 3D structure data, PDB `data consumers' access comparative data and external annotations, such as information about disease-causing point mutations and genetic variations. RCSB.org also provides access to >1 000 000 computed structure models (CSMs) generated using artificial intelligence/machine-learning methods. To avoid doubt, the provenance and reliability of experimentally determined PDB structures and CSMs are identified. Related training materials are available to support users in their RCSB.org explorations.

Categorization underlies understanding. Conceptualizing solid-state structures of organic molecules with `archetype crystal structures' bridges established categories of disorder, polymorphism and solid solutions and is herein extended to special position and high-Z' structures. The concept was developed in the context of disorder modelling [Dittrich, B. (2021). IUCrJ, 8, 305–318] and relies on adding quantum chemical energy differences between disorder components to other criteria as an explanation as to why disorder – and disappearing disorder – occurs in an average structure. Part of the concept is that disorder, as probed by diffraction, affects entire molecules, rather than just the parts of a molecule with differing conformations, and the finding that an R·T energy difference between disorder archetypes is usually not exceeded. An illustrative example combining disorder and special positions is the crystal structure of oestradiol hemihydrate analysed here, where its space-group/subgroup relationship is required to explain its disorder of hydrogen-bonded hydrogen atoms. In addition, we show how high-Z' structures can also be analysed energetically and understood via archetypes: high-Z' structures occur when an energy gain from combining different rather than overall alike conformations in a crystal significantly exceeds R·T, and this finding is discussed in the context of earlier explanations in the literature. Twinning is not related to archetype structures since it involves macroscopic domains of the same crystal structure. Archetype crystal structures are distinguished from crystal structure prediction trial structures in that an experimental reference structure is required for them. Categorization into archetype structures also has practical relevance, leading to a new practice of disorder modelling in experimental least-squares refinement alluded to in the above-mentioned publication.

This work focuses on molecules that are encoded by the major histocompatibility complex (MHC) and that bind self-, foreign- or tumor-derived peptides and display these at the cell surface for recognition by receptors on T lymphocytes (T cell receptors, TCR) and natural killer (NK) cells. The past few decades have accumulated a vast knowledge base of the structures of MHC molecules and the complexes of MHC/TCR with specificity for many different peptides. In recent years, the structures of MHC-I molecules complexed with chaperones that assist in peptide loading have been revealed by X-ray crystallography and cryogenic electron microscopy. These structures have been further studied using mutagenesis, molecular dynamics and NMR approaches. This review summarizes the current structures and dynamic principles that govern peptide exchange as these relate to the process of antigen presentation.

The large Bunyavirales order includes several families of viruses with a segmented ambisense (−) RNA genome and a cytoplasmic life cycle that starts by synthesizing viral mRNA. The initiation of transcription, which is common to all members, relies on an endonuclease activity that is responsible for cap-snatching. In La Crosse virus, an orthobunyavirus, it has previously been shown that the cap-snatching endonuclease resides in the N-terminal domain of the L protein. Orthobunyaviruses are transmitted by arthropods and cause diseases in cattle. However, California encephalitis virus, La Crosse virus and Jamestown Canyon virus are North American species that can cause encephalitis in humans. No vaccines or antiviral drugs are available. In this study, three known Influenza virus endonuclease inhibitors (DPBA, L-742,001 and baloxavir) were repurposed on the La Crosse virus endonuclease. Their inhibition was evaluated by fluorescence resonance energy transfer and their mode of binding was then assessed by differential scanning fluorimetry and microscale thermophoresis. Finally, two crystallographic structures were obtained in complex with L-742,001 and baloxavir, providing access to the structural determinants of inhibition and offering key information for the further development of Bunyavirales endonuclease inhibitors.

Immunodominant membrane protein (IMP) is a prevalent membrane protein in phytoplasma and has been confirmed to be an F-actin-binding protein. However, the intricate molecular mechanisms that govern the function of IMP require further elucidation. In this study, the X-ray crystallographic structure of IMP was determined and insights into its interaction with plant actin are provided. A comparative analysis with other proteins demonstrates that IMP shares structural homology with talin rod domain-containing protein 1 (TLNRD1), which also functions as an F-actin-binding protein. Subsequent molecular-docking studies of IMP and F-actin reveal that they possess complementary surfaces, suggesting a stable interaction. The low potential energy and high confidence score of the IMP–F-actin binding model indicate stable binding. Additionally, by employing immunoprecipitation and mass spectrometry, it was discovered that IMP serves as an interaction partner for the phytoplasmal effector causing phyllody 1 (PHYL1). It was then shown that both IMP and PHYL1 are highly expressed in the S2 stage of peanut witches' broom phytoplasma-infected Catharanthus roseus. The association between IMP and PHYL1 is substantiated through in vivo immunoprecipitation, an in vitro cross-linking assay and molecular-docking analysis. Collectively, these findings expand the current understanding of IMP interactions and enhance the comprehension of the interaction of IMP with plant F-actin. They also unveil a novel interaction pathway that may influence phytoplasma pathogenicity and host plant responses related to PHYL1. This discovery could pave the way for the development of new strategies to overcome phytoplasma-related plant diseases.

Appreciating that the role of the solute–solvent and other outer-sphere interactions is essential for understanding chemistry and chemical dynamics in solution, experimental approaches are needed to address the structural consequences of these interactions, complementing condensed-matter simulations and coarse-grained theories. High-energy X-ray scattering (HEXS) combined with pair distribution function analysis presents the opportunity to probe these structures directly and to develop quantitative, atomistic models of molecular systems in situ in the solution phase. However, at concentrations relevant to solution-phase chemistry, the total scattering signal is dominated by the bulk solvent, prompting researchers to adopt a differential approach to eliminate this unwanted background. Though similar approaches are well established in quantitative structural studies of macromolecules in solution by small- and wide-angle X-ray scattering (SAXS/WAXS), analogous studies in the HEXS regime—where sub-ångström spatial resolution is achieved—remain underdeveloped, in part due to the lack of a rigorous theoretical description of the experiment. To address this, herein we develop a framework for differential solution scattering experiments conducted at high energies, which includes concepts of the solvent-excluded volume introduced to describe SAXS/WAXS data, as well as concepts from the time-resolved X-ray scattering community. Our theory is supported by numerical simulations and experiment and paves the way for establishing quantitative methods to determine the atomic structures of small molecules in solution with resolution approaching that of crystallography.

In this work, regenerated cellulose textile fibers, Ioncell-F, dry-wet spun with different draw ratios, have been investigated by scanning wide-angle X-ray scattering (WAXS) using a mesoscopic X-ray beam. The fibers were found to be homogeneous on the 500 nm length scale. Analysis of the azimuthal angular dependence of a crystalline Bragg spot intensity revealed a radial dependence of the degree of orientation of crystallites that was found to increase with the distance from the center of the fiber. We attribute this to radial velocity gradients during the extrusion of the spin dope and the early stage of drawing. On the other hand, the fiber crystallinity was found to be essentially homogeneous over the fiber cross section.

The functionality and efficiency of proteins within a biological membrane are highly dependent on both the membrane lipid composition and the physiochemical properties of the solution. Lipid mesophases are directly influenced by changes in temperature, pH, water content or due to individual properties of single lipids such as photoswitchability. In this work, we were able to induce light- and temperature-driven mesophase transitions in a model membrane system containing a mixture of 1,2-dipalmitoyl-phosphatidylcholine phospho­lipids and azo­benzene amphiphiles. We observed reversible and reproducible transitions between the lamellar and Pn3m cubic phase after illuminating the sample for 5 min with light of 365 and 455 nm wavelengths, respectively, to switch between the cis and trans states of the azo­benzene N=N double bond. These light-controlled mesophase transitions were found for mixed complexes with up to 20% content of the photosensitive molecule and at temperatures below the gel-to-liquid crystalline phase transition temperature of 33°C. Our results demonstrate the potential to design bespoke model systems to study the response of membrane lipids and proteins upon changes in mesophase without altering the environment and thus provide a possible basis for drug delivery systems.

Carbonic anhydrase (CA) was among the first proteins whose X-ray crystal structure was solved to atomic resolution. CA proteins have essentially the same fold and similar active centers that differ in only several amino acids. Primary sulfonamides are well defined, strong and specific binders of CA. However, minor variations in chemical structure can significantly alter their binding properties. Over 1000 sulfonamides have been designed, synthesized and evaluated to understand the correlations between the structure and thermodynamics of their binding to the human CA isozyme family. Compound binding was determined by several binding assays: fluorescence-based thermal shift assay, stopped-flow enzyme activity inhibition assay, isothermal titration calorimetry and competition assay for enzyme expressed on cancer cell surfaces. All assays have advantages and limitations but are necessary for deeper characterization of these protein–ligand interactions. Here, the concept and importance of intrinsic binding thermodynamics is emphasized and the role of structure–thermodynamics correlations for the novel inhibitors of CA IX is discussed – an isozyme that is overexpressed in solid hypoxic tumors, and thus these inhibitors may serve as anticancer drugs. The abundant structural and thermodynamic data are assembled into the Protein–Ligand Binding Database to understand general protein–ligand recognition principles that could be used in drug discovery.

Investigation of the analyte soaking conditions on the crystalline sponge {[(ZnI2)3(tpt)2·x(solvent)]n} method using a statistical design of experiments model has provided fundamental insights into the influence of experimental variables. This approach focuses on a single analyte tested via 60 experiments (20 unique conditions) to identify the main effects for success and overall guest structure quality. This is employed as a basis for the development of a novel molecular structure grading system that enables the quantification of guest exchange quality.

Stimulated by informal conversations at the XVII International Small Angle Scattering (SAS) conference (Traverse City, 2017), an international team of experts undertook a round-robin exercise to produce a large dataset from proteins under standard solution conditions. These data were used to generate consensus SAS profiles for xylose isomerase, urate oxidase, xylanase, lysozyme and ribonuclease A. Here, we apply a new protocol using maximum likelihood with a larger number of the contributed datasets to generate improved consensus profiles. We investigate the fits of these profiles to predicted profiles from atomic coordinates that incorporate different models to account for the contribution to the scattering of water molecules of hydration surrounding proteins in solution. Programs using an implicit, shell-type hydration layer generally optimize fits to experimental data with the aid of two parameters that adjust the volume of the bulk solvent excluded by the protein and the contrast of the hydration layer. For these models, we found the error-weighted residual differences between the model and the experiment generally reflected the subsidiary maxima and minima in the consensus profiles that are determined by the size of the protein plus the hydration layer. By comparison, all-atom solute and solvent molecular dynamics (MD) simulations are without the benefit of adjustable parameters and, nonetheless, they yielded at least equally good fits with residual differences that are less reflective of the structure in the consensus profile. Further, where MD simulations accounted for the precise solvent composition of the experiment, specifically the inclusion of ions, the modelled radius of gyration values were significantly closer to the experiment. The power of adjustable parameters to mask real differences between a model and the structure present in solution is demonstrated by the results for the conformationally dynamic ribonuclease A and calculations with pseudo-experimental data. This study shows that, while methods invoking an implicit hydration layer have the unequivocal advantage of speed, care is needed to understand the influence of the adjustable parameters. All-atom solute and solvent MD simulations are slower but are less susceptible to false positives, and can account for thermal fluctuations in atomic positions, and more accurately represent the water molecules of hydration that contribute to the scattering profile.

Here, the novel technique of extended-range high-energy-resolution fluorescence detection (XR-HERFD) has successfully observed the n = 2 satellite in manganese to a high accuracy. The significance of the satellite signature presented is many hundreds of standard errors and well beyond typical discovery levels of three to six standard errors. This satellite is a sensitive indicator for all manganese-containing materials in condensed matter. The uncertainty in the measurements has been defined, which clearly observes multiple peaks and structure indicative of complex physical quantum-mechanical processes. Theoretical calculations of energy eigenvalues, shake-off probability and Auger rates are also presented, which explain the origin of the satellite from physical n = 2 shake-off processes. The evolution in the intensity of this satellite is measured relative to the full Kα spectrum of manganese to investigate satellite structure, and therefore many-body processes, as a function of incident energy. Results demonstrate that the many-body reduction factor S02 should not be modelled with a constant value as is currently done. This work makes a significant contribution to the challenge of understanding many-body processes and interpreting HERFD or resonant inelastic X-ray scattering spectra in a quantitative manner.

Light–oxygen–voltage (LOV) domains are small photosensory flavoprotein modules that allow the conversion of external stimuli (sunlight) into intra­cellular signals responsible for various cell behaviors (e.g. phototropism and chloro­plast relocation). This ability relies on the light-induced formation of a covalent thio­ether adduct between a flavin chromophore and a reactive cysteine from the protein environment, which triggers a cascade of structural changes that result in the activation of a serine/threonine (Ser/Thr) kinase. Recent developments in time-resolved crystallography may allow the activation cascade of the LOV domain to be observed in real time, which has been elusive. In this study, we report a robust protocol for the production and stable delivery of microcrystals of the LOV domain of phototropin Phot-1 from Chlamydomonas reinhardtii (CrPhotLOV1) with a high-viscosity injector for time-resolved serial synchrotron crystallography (TR-SSX). The detailed process covers all aspects, from sample optimization to data collection, which may serve as a guide for soluble protein preparation for TR-SSX. In addition, we show that the crystals obtained preserve the photoreactivity using infrared spectroscopy. Furthermore, the results of the TR-SSX experiment provide high-resolution insights into structural alterations of CrPhotLOV1 from Δt = 2.5 ms up to Δt = 95 ms post-photoactivation, including resolving the geometry of the thio­ether adduct and the C-terminal region implicated in the signal transduction process.

The advent of serial crystallography has rejuvenated and popularized room-temperature X-ray crystal structure determination. Structures determined at physiological temperature reveal protein flexibility and dynamics. In addition, challenging samples (e.g. large complexes, membrane proteins and viruses) form fragile crystals that are often difficult to harvest for cryo-crystallography. Moreover, a typical serial crystallography experiment requires a large number of microcrystals, mainly achievable through batch crystallization. Many medically relevant samples are expressed in mammalian cell lines, producing a meager quantity of protein that is incompatible with batch crystallization. This can limit the scope of serial crystallography approaches. Direct in situ data collection from a 96-well crystallization plate enables not only the identification of the best diffracting crystallization condition but also the possibility for structure determination under ambient conditions. Here, we describe an in situ serial crystallography (iSX) approach, facilitating direct measurement from crystallization plates mounted on a rapidly exchangeable universal plate holder deployed at a microfocus beamline, ID23-2, at the European Synchrotron Radiation Facility. We applied our iSX approach on a challenging project, autotaxin, a therapeutic target expressed in a stable human cell line, to determine the structure in the lowest-symmetry P1 space group at 3.0 Å resolution. Our in situ data collection strategy provided a complete dataset for structure determination while screening various crystallization conditions. Our data analysis reveals that the iSX approach is highly efficient at a microfocus beamline, improving throughput and demonstrating how crystallization plates can be routinely used as an alternative method of presenting samples for serial crystallography experiments at synchrotrons.

Cryogenic electron microscopy (cryo-EM) is a pivotal technique for imaging macromolecular structures. However, despite extensive processing of large image sets collected in cryo-EM experiments to amplify the signal-to-noise ratio, the reconstructed 3D protein-density maps are often limited in quality due to residual noise, which in turn affects the accuracy of the macromolecular representation. Here, crefDenoiser is introduced, a denoising neural network model designed to enhance the signal in 3D cryo-EM maps produced with standard processing pipelines. The crefDenoiser model is trained without the need for `clean' ground-truth target maps. Instead, a custom dataset is employed, composed of real noisy protein half-maps sourced from the Electron Microscopy Data Bank repository. Competing with the current state-of-the-art, crefDenoiser is designed to optimize for the theoretical noise-free map during self-supervised training. We demonstrate that our model successfully amplifies the signal across a wide variety of protein maps, outperforming a classic map denoiser and following a network-based sharpening model. Without biasing the map, the proposed denoising method leads to improved visibility of protein structural features, including protein domains, secondary structure elements and modest high-resolution feature restoration.

Crystallographic texture is a key organization feature of many technical and biological materials. In these materials, especially hierarchically structured ones, the preferential alignment of the nano constituents heavily influences the macroscopic behavior of the material. To study local crystallographic texture with both high spatial and angular resolution, we developed Texture Tomography (TexTOM). This approach allows the user to model the diffraction data of polycrystalline materials using the full reciprocal space of the crystal ensemble and describe the texture in each voxel via an orientation distribution function, hence it provides 3D reconstructions of the local texture by measuring the probabilities of all crystal orientations. The TexTOM approach addresses limitations associated with existing models: it correlates the intensities from several Bragg reflections, thus reducing ambiguities resulting from symmetry. Further, it yields quantitative probability distributions of local real space crystal orientations without further assumptions about the sample structure. Finally, its efficient mathematical formulation enables reconstructions faster than the time scale of the experiment. This manuscript presents the mathematical model, the inversion strategy and its current experimental implementation. We show characterizations of simulated data as well as experimental data obtained from a synthetic, inorganic model sample: the silica–witherite biomorph. TexTOM provides a versatile framework to reconstruct 3D quantitative texture information for polycrystalline samples; it opens the door for unprecedented insights into the nanostructural makeup of natural and technical materials.

This study examines various methods for modelling the electron density and, thus, the electrostatic potential of an organometallic complex for use in crystal structure refinement against 3D electron diffraction (ED) data. It focuses on modelling the scattering factors of iron(III), considering the electron density distribution specific for coordination with organic linkers. We refined the structural model of the metal–organic complex, iron(III) acetyl­acetonate (FeAcAc), using both the independent atom model (IAM) and the transferable aspherical atom model (TAAM). TAAM refinement initially employed multipolar parameters from the MATTS databank for acetyl­acetonate, while iron was modelled with a spherical and neutral approach (TAAM ligand). Later, custom-made TAAM scattering factors for Fe—O coordination were derived from DFT calculations [TAAM-ligand-Fe(III)]. Our findings show that, in this compound, the TAAM scattering factor corresponding to Fe3+ has a lower scattering amplitude than the Fe3+ charged scattering factor described by IAM. When using scattering factors corresponding to the oxidation state of iron, IAM inaccurately represents electrostatic potential maps and overestimates the scattering potential of the iron. In addition, TAAM significantly improved the fitting of the model to the data, shown by improved R1 values, goodness-of-fit (GooF) and reduced noise in the Fourier difference map (based on the residual distribution analysis). For 3D ED, R1 values improved from 19.36% (IAM) to 17.44% (TAAM-ligand) and 17.49% (TAAM-ligand-Fe3+), and for single-crystal X-ray diffraction (SCXRD) from 3.82 to 2.03% and 1.98%, respectively. For 3D ED, the most significant R1 reductions occurred in the low-resolution region (8.65–2.00 Å), dropping from 20.19% (IAM) to 14.67% and 14.89% for TAAM-ligand and TAAM-ligand-Fe(III), respectively, with less improvement in high-resolution ranges (2.00–0.85 Å). This indicates that the major enhancements are due to better scattering modelling in low-resolution zones. Furthermore, when using TAAM instead of IAM, there was a noticeable improvement in the shape of the thermal ellipsoids, which more closely resembled those of an SCXRD-refined model. This study demonstrates the applicability of more sophisticated scattering factors to improve the refinement of metal–organic complexes against 3D ED data, suggesting the need for more accurate modelling methods and highlighting the potential of TAAM in examining the charge distribution of large molecular structures using 3D ED.

Reaching beyond the commonly used spherical atomic electron density model allows one to greatly improve the accuracy of hydrogen atom structural param­eters derived from X-ray data. However, the effects of atomic asphericity are less explored for electron diffraction data. In this work, Hirshfeld atom refinement (HAR), a method that uses an accurate description of electron density by quantum mechanical calculation for a system of interest, was applied for the first time to the kinematical refinement of electron diffraction data. This approach was applied here to derive the structure of ordinary hexagonal ice (Ih). The effect of introducing HAR is much less noticeable than in the case of X-ray refinement and it is largely overshadowed by dynamical scattering effects. It led to only a slight change in the O—H bond lengths (shortening by 0.01 Å) compared with the independent atom model (IAM). The average absolute differences in O—H bond lengths between the kinematical refinements and the reference neutron structure were much larger: 0.044 for IAM and 0.046 Å for HAR. The refinement results changed considerably when dynamical scattering effects were modelled – with extinction correction or with dynamical refinement. The latter led to an improvement of the O—H bond length accuracy to 0.021 Å on average (with IAM refinement). Though there is a potential for deriving more accurate structures using HAR for electron diffraction, modelling of dynamical scattering effects seems to be a necessary step to achieve this. However, at present there is no software to support both HAR and dynamical refinement.