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.

For protein crystals in which more than two thirds of the volume is occupied by solvent, the featureless nature of the solvent region often generates a constraint that is powerful enough to allow direct phasing of X-ray diffraction data. Practical implementation relies on the use of iterative projection algorithms with good global convergence properties to solve the difficult nonconvex phase-retrieval problem. In this paper, some aspects of phase retrieval using iterative projection algorithms are systematically explored, where the diffraction data and density-value distributions in the protein and solvent regions provide the sole constraints. The analysis is based on the addition of random error to the phases of previously determined protein crystal structures, followed by evaluation of the ability to recover the correct phase set as the distance from the solution increases. The properties of the difference-map (DM), relaxed–reflect–reflect (RRR) and relaxed averaged alternating reflectors (RAAR) algorithms are compared. All of these algorithms prove to be effective for crystallographic phase retrieval, and the useful ranges of the adjustable parameter which controls their behavior are established. When these algorithms converge to the solution, the algorithm trajectory becomes stationary; however, the density function continues to fluctuate significantly around its mean position. It is shown that averaging over the algorithm trajectory in the stationary region, following convergence, improves the density estimate, with this procedure outperforming previous approaches for phase or density refinement.

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.

Serial electron diffraction (SerialED), which applies a snapshot data acquisition strategy for each crystal, was introduced to tackle the problem of radiation damage in the structure determination of beam-sensitive materials by three-dimensional electron diffraction (3DED). The snapshot data acquisition in SerialED can be realized using both transmission and scanning transmission electron microscopes (TEM/STEM). However, the current SerialED workflow based on STEM setups requires special external devices and software, which limits broader adoption. Here, we present a simplified experimental implementation of STEM-based SerialED on Thermo Fisher Scientific STEMs using common proprietary software interfaced through Python scripts to automate data collection. Specifically, we utilize TEM Imaging and Analysis (TIA) scripting and TEM scripting to access the STEM functionalities of the microscope, and DigitalMicrograph scripting to control the camera for snapshot data acquisition. Data analysis adapts the existing workflow using the software CrystFEL, which was developed for serial X-ray crystallography. Our workflow for STEM SerialED can be used on any Gatan or Thermo Fisher Scientific camera. We apply this workflow to collect high-resolution STEM SerialED data from two aluminosilicate zeolites, zeolite Y and ZSM-25. We demonstrate, for the first time, ab initio structure determination through direct methods using STEM SerialED data. Zeolite Y is relatively stable under the electron beam, and STEM SerialED data extend to 0.60 Å. We show that the structural model obtained using STEM SerialED data merged from 358 crystals is nearly identical to that using continuous rotation electron diffraction data from one crystal. This demonstrates that accurate structures can be obtained from STEM SerialED. Zeolite ZSM-25 is very beam-sensitive and has a complex structure. We show that STEM SerialED greatly improves the data resolution of ZSM-25, compared with serial rotation electron diffraction (SerialRED), from 1.50 to 0.90 Å. This allows, for the first time, the use of standard phasing methods, such as direct methods, for the ab initio structure determination of ZSM-25.

The application of grazing-incidence total X-ray scattering (GITXS) for pair distribution function (PDF) analysis using >50 keV X-rays from synchrotron light sources has created new opportunities for structural characterization of supported thin films with high resolution. Compared with grazing-incidence wide-angle X-ray scattering, which is only useful for highly ordered materials, GITXS/PDFs expand such analysis to largely disordered or nanostructured materials by examining the atomic pair correlations dependent on the direction relative to the surface of the supporting substrate. A characterization of nanocrystalline In2O3-derived thin films is presented here with in-plane-isotropic and out-of-plane-anisotropic orientational ordering of the atomic structure, each synthesized using different techniques. The atomic orientations of such films are known to vary based on the synthetic conditions. Here, an azimuthal orientational analysis of these films using GITXS with a single incident angle is shown to resolve the markedly different orientations of the atomic structures with respect to the planar support and the different degrees of long-range order, and hence, the terminal surface chemistries. It is anticipated that orientational analysis of GITXS/PDF data will offer opportunities to extend structural analyses of thin films by providing a means to qualitatively determine the major atomic orientation within nanocrystalline and, eventually, non-crystalline films.

Interoperability of crystallographic data with other disciplines is essential for the smooth and rapid progress of structure-based science in the computer age. Within crystallography and closely related subject areas, there is already a high level of conformance to the generally accepted FAIR principles (that data be findable, accessible, interoperable and reusable) through the adoption of common information exchange protocols by databases, publishers, instrument vendors, experimental facilities and software authors. Driven by the success within these domains, the IUCr has worked closely with CODATA (the Committee on Data of the International Science Council) to help develop the latter's commitment to cross-domain integration of discipline-specific data. The IUCr has, in particular, emphasized the need for standards relating to data quality and completeness as an adjunct to the FAIR data landscape. This can ensure definitive reusable data, which in turn can aid interoperability across domains. A microsymposium at the IUCr 2023 Congress provided an up-to-date survey of data interoperability within and outside of crystallography, expounded using a broad range of examples.

Serial crystallography (SX) has become an established technique for protein structure determination, especially when dealing with small or radiation-sensitive crystals and investigating fast or irreversible protein dynamics. The advent of newly developed multi-megapixel X-ray area detectors, capable of capturing over 1000 images per second, has brought about substantial benefits. However, this advancement also entails a notable increase in the volume of collected data. Today, up to 2 PB of data per experiment could be easily obtained under efficient operating conditions. The combined costs associated with storing data from multiple experiments provide a compelling incentive to develop strategies that effectively reduce the amount of data stored on disk while maintaining the quality of scientific outcomes. Lossless data-compression methods are designed to preserve the information content of the data but often struggle to achieve a high compression ratio when applied to experimental data that contain noise. Conversely, lossy compression methods offer the potential to greatly reduce the data volume. Nonetheless, it is vital to thoroughly assess the impact of data quality and scientific outcomes when employing lossy compression, as it inherently involves discarding information. The evaluation of lossy compression effects on data requires proper data quality metrics. In our research, we assess various approaches for both lossless and lossy compression techniques applied to SX data, and equally importantly, we describe metrics suitable for evaluating SX data quality.

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.

This paper was motivated by the articles `Same or different – that is the question' in CrystEngComm (July 2020) and `Change to the definition of a crystal' in the IUCr Newsletter (June 2021). Experimental approaches to crystal comparisons require rigorously defined classifications in crystallography and beyond. Since crystal structures are determined in a rigid form, their strongest equivalence in practice is rigid motion, which is a composition of translations and rotations in 3D space. Conventional representations based on reduced cells and standardizations theoretically distinguish all periodic crystals. However, all cell-based representations are inherently discontinuous under almost any atomic displacement that can arbitrarily scale up a reduced cell. Hence, comparison of millions of known structures in materials databases requires continuous distance metrics.

The hardware for data archiving has expanded capacities for digital storage enormously in the past decade or more. The IUCr evaluated the costs and benefits of this within an official working group which advised that raw data archiving would allow ground truth reproducibility in published studies. Consultations of the IUCr's Commissions ensued via a newly constituted standing advisory committee, the Committee on Data. At all stages, the IUCr financed workshops to facilitate community discussions and possible methods of raw data archiving implementation. The recent launch of the IUCrData journal's Raw Data Letters is a milestone in the implementation of raw data archiving beyond the currently published studies: it includes diffraction patterns that have not been fully interpreted, if at all. The IUCr 75th Congress in Melbourne included a workshop on raw data reuse, discussing the successes and ongoing challenges of raw data reuse. This article charts the efforts of the IUCr to facilitate discussions and plans relating to raw data archiving and reuse within the various communities of crystallography, diffraction and scattering.

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.

A series of events underscoring the significant advancements in micro-crystallization and in vivo crystallography were held during the 26th IUCr Congress in Melbourne, positioning microcrystallography as a pivotal field within structural biology. Through collaborative discussions and the sharing of innovative methodologies, these sessions outlined frontier approaches in macromolecular crystallography. This review provides an overview of this rapidly moving field in light of the rich dialogues and forward-thinking proposals explored during the congress workshop and microsymposium. These advances in microcrystallography shed light on the potential to reshape current research paradigms and enhance our comprehension of biological mechanisms at the molecular scale.

Spectroscopic data, particularly diffraction data, are essential for materials characterization due to their comprehensive crystallographic information. The current crystallographic phase identification, however, is very time consuming. To address this challenge, we have developed a real-time crystallographic phase identifier based on a convolutional self-attention neural network (CPICANN). Trained on 692 190 simulated powder X-ray diffraction (XRD) patterns from 23 073 distinct inorganic crystallographic information files, CPICANN demonstrates superior phase-identification power. Single-phase identification on simulated XRD patterns yields 98.5 and 87.5% accuracies with and without elemental information, respectively, outperforming JADE software (68.2 and 38.7%, respectively). Bi-phase identification on simulated XRD patterns achieves 84.2 and 51.5% accuracies, respectively. In experimental settings, CPICANN achieves an 80% identification accuracy, surpassing JADE software (61%). Integration of CPICANN into XRD refinement software will significantly advance the cutting-edge technology in XRD materials characterization.

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.

Structure-based drug design is highly dependent on the availability of structures of the protein of interest in complex with lead compounds. Ideally, this information can be used to guide the chemical optimization of a compound into a pharmaceutical drug candidate. A limitation of the main structural method used today – conventional X-ray crystallography – is that it only provides structural information about the protein complex in its frozen state. Serial crystallography is a relatively new approach that offers the possibility to study protein structures at room temperature (RT). Here, we explore the use of serial crystallography to determine the structures of the pharmaceutical target, soluble epoxide hydro­lase. We introduce a new method to screen for optimal microcrystallization conditions suitable for use in serial crystallography and present a number of RT ligand-bound structures of our target protein. From a comparison between the RT structural data and previously published cryo-temperature structures, we describe an example of a temperature-dependent difference in the ligand-binding mode and observe that flexible loops are better resolved at RT. Finally, we discuss the current limitations and potential future advances of serial crystallography for use within pharmaceutical drug discovery.

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.

OaPAC is a recently discovered blue-light-using flavin adenosine dinucleotide (BLUF) photoactivated adenylate cyclase from the cyanobacterium Oscillatoria acuminata that uses adenosine triphosphate and translates the light signal into the production of cyclic adenosine monophosphate. Here, we report crystal structures of the enzyme in the absence of its natural substrate determined from room-temperature serial crystallography data collected at both an X-ray free-electron laser and a synchrotron, and we compare these structures with cryo-macromolecular crystallography structures obtained at a synchrotron by us and others. These results reveal slight differences in the structure of the enzyme due to data collection at different temperatures and X-ray sources. We further investigate the effect of the Y6W mutation in the BLUF domain, a mutation which results in a rearrangement of the hydrogen-bond network around the flavin and a notable rotation of the side chain of the critical Gln48 residue. These studies pave the way for picosecond–millisecond time-resolved serial crystallography experiments at X-ray free-electron lasers and synchrotrons in order to determine the early structural intermediates and correlate them with the well studied pico­second–millisecond spectroscopic intermediates.

Single-crystal X-ray diffraction analysis of small molecule active pharmaceutical ingredients is a key technique in the confirmation of molecular connectivity, including absolute stereochemistry, as well as the solid-state form. However, accessing single crystals suitable for X-ray diffraction analysis of an active pharmaceutical ingredient can be experimentally laborious, especially considering the potential for multiple solid-state forms (solvates, hydrates and polymorphs). In recent years, methods for the exploration of experimental crystallization space of small molecules have undergone a `step-change', resulting in new high-throughput techniques becoming available. Here, the application of high-throughput encapsulated nanodroplet crystallization to a series of six di­hydro­pyridines, calcium channel blockers used in the treatment of hypertension related diseases, is described. This approach allowed 288 individual crystallization experiments to be performed in parallel on each molecule, resulting in rapid access to crystals and subsequent crystal structures for all six di­hydro­pyridines, as well as revealing a new solvate polymorph of nifedipine (1,4-dioxane solvate) and the first known solvate of nimodipine (DMSO solvate). This work further demonstrates the power of modern high-throughput crystallization methods in the exploration of the solid-state landscape of active pharmaceutical ingredients to facilitate crystal form discovery and structural analysis by single-crystal X-ray diffraction.

Quantum crystallography is an emerging research field of science that has its origin in the early days of quantum physics and modern crystallography when it was almost immediately envisaged that X-ray radiation could be somehow exploited to determine the electron distribution of atoms and molecules. Today it can be seen as a composite research area at the intersection of crystallography, quantum chemistry, solid-state physics, applied mathematics and computer science, with the goal of investigating quantum problems, phenomena and features of the crystalline state. In this article, the state-of-the-art of quantum crystallography will be described by presenting developments and applications of novel techniques that have been introduced in the last 15 years. The focus will be on advances in the framework of multipole model strategies, wavefunction-/density matrix-based approaches and quantum chemical topological techniques. Finally, possible future improvements and expansions in the field will be discussed, also considering new emerging experimental and computational technologies.

The embedded call to a special version of the web-based Bilbao Crystallographic Server tool k-SUBGROUPSMAG from within GSAS-II to form a list of all possible commensurate magnetic subgroups of a parent magnetic grey group is described. It facilitates the selection and refinement of the best commensurate magnetic structure model by having all the analysis tools including Rietveld refinement in one place as part of GSAS-II. It also provides the chosen magnetic space group as one of the 1421 possible standard Belov–Neronova–Smirnova forms or equivalent non-standard versions.

The title compound, C5H5NO2, is a hy­droxy­lated pyridine ring that has been studied for its involvement in microbial degradation of nicotinic acid. Here we describe its synthesis as a formic acid salt, rather than the standard hydro­chloride salt that is commercially available, and its spectroscopic and crystallographic characterization.

This paper summarizes brief perspectives on the historic process of establishing an African Crystallographic Association (AfCA) and includes representative references. It covers activities within four arbitrarily selected, approximate time slots, i.e., 1890s–1999, 2000–2013, 2014–2019 and 2020–2023. A genuine attempt is made to include appropriate role players, organizations and accompanying events within these periods. It concludes with the official admission of AfCA as the fifth Regional Associate of the IUCr at the 26th Congress and General Assembly of the IUCr in Melbourne, Australia in 2023.

Single crystals of two basic cadmium phosphates, dicadmium orthophosphate hydroxide, Cd2(PO4)OH, and penta­cadmium bis­(orthophosphate) tetra­kis­(hydroxide), Cd5(PO4)2(OH)4, were obtained under hydro­thermal conditions. Cd2(PO4)OH adopts the triplite [(Mn,Fe)2(PO4)F] structure type. Its asymmetric unit comprises two Cd, one P and five O sites, all situated at the general Wyckoff position 8 f of space group I2/a; two of the O atoms are positionally disordered over two sites, and the H atom could not be localized. Disregarding the disorder, distorted [CdO6] polyhedra form a tri-periodic network by edge-sharing with neighbouring [CdO6] units and by vertex-sharing with [PO4] units. The site associated with the OH group is coordinated by four Cd atoms in a distorted tetra­hedral manner forming 1∞[(OH)Cd4/2] chains parallel to [001]. The oxygen environment around the OH site suggests multiple acceptor atoms for possible O—H⋯O hydrogen-bonding inter­actions and is the putative reason for the disorder. Cd5(PO4)2(OH)4 adopts the arsenoclasite [Mn5(AsO4)2(OH)4] structure type. Its asymmetric unit comprises five Cd, two P, and twelve O sites all located at the general Wyckoff position 4 a of space group P212121; the H atoms could not be localized. The crystal structure of Cd5(PO4)2(OH)4 can be subdivided into two main sub-units. One consists of three edge-sharing [CdO6] octa­hedra, and the other of two edge- and vertex-sharing [CdO6] octa­hedra. Each sub-unit forms corrugated ribbons extending parallel to [100]. The two types of ribbons are linked into the tri-periodic arrangement through vertex-sharing and through common [PO4] tetra­hedra. Qu­anti­tative structure comparisons are made with isotypic M5(XO4)2(OH)4 crystal structures (M = Cd, Mn, Co; X = P, As, V).

Lanthanide-containing materials are of inter­est in the field of crystal engin­eering because of their unique properties and distinct structure types. In this context, a new samarium–sodium heterometallic coordination polymer, poly[tetra­kis­(μ2-2-formyl-6-meth­oxy­phenolato)samarium(III)sodium(I)], {[SmNa(C8H7O3)4]·solvent}n (Sm-1), was synthesized and crystallized via slow evaporation from a mixture of ethanol and aceto­nitrile. The compound features alternating SmIII and NaI ions, which are linked by ortho-vanillin (o-vanillin) ligands to form a mono-periodic chain-like coordination polymer. The chains propagate along the [001] direction. Residual electron density of disordered solvent mol­ecules in the void space could not be reasonably modeled, thus the SQUEEZE function was applied. The structural, vibrational, and optical properties are reported.

Since its founding in 2013, the Young Crystallographers (YC) have become one of the most active working groups not only within their parent organization, the German Crystallographic Society (DGK), but also among other young crystallographers' groups in Europe and the world. The aim of the YC is and always has been to support early-career researchers in the diverse fields of crystallography and the rejuvenation of the field on a national scale. Over the past decade, we have curated events, platforms, and educational content tailored to foster collaboration and knowledge transfer among young crystallographers. In this article, we introduce our group and show how this active and diverse community has shaped the rejuvenation of crystallography in Germany, strengthened by the support of our national society.

Single crystals of the mol­ecular compound, {Cu20Ir6Cl8(C21H24N2)6(C4H4N2)3]·3.18CH3OH or [({Cu10Ir3}Cl4(IMes)3(pyrazine))2(pyrazine)]·3.18CH3OH [where IMes is 1,3-bis­(2,4,6-trimethylphen­yl)imidazol-2-yl­idene], with a unique heterometallic cluster have been prepared and the structure revealed using single-crystal X-ray diffraction. The mol­ecule is centrosymmetric with two {Cu10Ir3} cores bridged by a pyrazine ligand. The polymetallic cluster contains three stabilizing N-heterocyclic carbenes, four Cl ligands, and a non-bridging pyrazine ligand. Notably, the Cu—Ir core is arranged in an unusual shape containing 13 vertices, 22 faces, and 32 sides. The atoms within the trideca­metallic cluster are arranged in four planes, with 2, 4, 4, 3 metals in each plane. Ir atoms are present in alternate planes with an Ir atom featuring in the peripheral bimetallic plane, and two Ir atoms featuring on opposite sides of the non-adjacent tetra­metallic plane. The crystal contains two disordered methanol solvent mol­ecules with an additional region of non-modelled electron density corrected for using the SQUEEZE routine in PLATON [Spek (2015). Acta Cryst. C71, 9–18]. The given chemical formula and other crystal data do not take into account the unmodelled methanol solvent mol­ecule(s).

Characterization of a material structure with pair distribution function (PDF) analysis typically involves refining a structure model against an experimental data set, but finding or constructing a suitable atomic model for PDF modelling can be an extremely labour-intensive task, requiring carefully browsing through large numbers of possible models. Presented here is POMFinder, a machine learning (ML) classifier that rapidly screens a database of structures, here polyoxometallate (POM) clusters, to identify candidate structures for PDF data modelling. The approach is shown to identify suitable POMs from experimental data, including in situ data collected with fast acquisition times. This automated approach has significant potential for identifying suitable models for structure refinement to extract quantitative structural parameters in materials chemistry research. POMFinder is open source and user friendly, making it accessible to those without prior ML knowledge. It is also demonstrated that POMFinder offers a promising modelling framework for combined modelling of multiple scattering techniques.

Serial crystallography experiments at synchrotron and X-ray free-electron laser (XFEL) sources are producing crystallographic data sets of ever-increasing volume. While these experiments have large data sets and high-frame-rate detectors (around 3520 frames per second), only a small percentage of the data are useful for downstream analysis. Thus, an efficient and real-time data classification pipeline is essential to differentiate reliably between useful and non-useful images, typically known as `hit' and `miss', respectively, and keep only hit images on disk for further analysis such as peak finding and indexing. While feature-point extraction is a key component of modern approaches to image classification, existing approaches require computationally expensive patch preprocessing to handle perspective distortion. This paper proposes a pipeline to categorize the data, consisting of a real-time feature extraction algorithm called modified and parallelized FAST (MP-FAST), an image descriptor and a machine learning classifier. For parallelizing the primary operations of the proposed pipeline, central processing units, graphics processing units and field-programmable gate arrays are implemented and their performances compared. Finally, MP-FAST-based image classification is evaluated using a multi-layer perceptron on various data sets, including both synthetic and experimental data. This approach demonstrates superior performance compared with other feature extractors and classifiers.

X-ray Laue microdiffraction aims to characterize microstructural and mechanical fields in polycrystalline specimens at the sub-micrometre scale with a strain resolution of ∼10−4. Here, a new and unique Laue microdiffraction setup and alignment procedure is presented, allowing measurements at temperatures as high as 1500 K, with the objective to extend the technique for the study of crystalline phase transitions and associated strain-field evolution that occur at high temperatures. A method is provided to measure the real temperature encountered by the specimen, which can be critical for precise phase-transition studies, as well as a strategy to calibrate the setup geometry to account for the sample and furnace dilation using a standard α-alumina single crystal. A first application to phase transitions in a polycrystalline specimen of pure zirconia is provided as an illustrative example.

X-ray crystallography is an established tool to probe the structure of macromolecules with atomic resolution. Compared with alternative techniques such as single-particle cryo-electron microscopy and micro-electron diffraction, X-ray crystallography is uniquely suited to room-temperature studies and for obtaining a detailed picture of macromolecules subjected to an external electric field (EEF). The impact of an EEF on proteins has been extensively explored through single-crystal X-ray crystallography, which works well with larger high-quality protein crystals. This article introduces a novel design for a 3D-printed in situ crystallization plate that serves a dual purpose: fostering crystal growth and allowing the concurrent examination of the effects of an EEF on crystals of varying sizes. The plate's compatibility with established X-ray crystallography techniques is evaluated.

Processing of single-crystal X-ray diffraction data from area detectors can be separated into two steps. First, raw intensities are obtained by integration of the diffraction images, and then data correction and reduction are performed to determine structure-factor amplitudes and their uncertainties. The second step considers the diffraction geometry, sample illumination, decay, absorption and other effects. While absorption is only a minor effect in standard macromolecular crystallography (MX), it can become the largest source of uncertainty for experiments performed at long wavelengths. Current software packages for MX typically employ empirical models to correct for the effects of absorption, with the corrections determined through the procedure of minimizing the differences in intensities between symmetry-equivalent reflections; these models are well suited to capturing smoothly varying experimental effects. However, for very long wavelengths, empirical methods become an unreliable approach to model strong absorption effects with high fidelity. This problem is particularly acute when data multiplicity is low. This paper presents an analytical absorption correction strategy (implemented in new software AnACor) based on a volumetric model of the sample derived from X-ray tomography. Individual path lengths through the different sample materials for all reflections are determined by a ray-tracing method. Several approaches for absorption corrections (spherical harmonics correction, analytical absorption correction and a combination of the two) are compared for two samples, the membrane protein OmpK36 GD, measured at a wavelength of λ = 3.54 Å, and chlorite dismutase, measured at λ = 4.13 Å. Data set statistics, the peak heights in the anomalous difference Fourier maps and the success of experimental phasing are used to compare the results from the different absorption correction approaches. The strategies using the new analytical absorption correction are shown to be superior to the standard spherical harmonics corrections. While the improvements are modest in the 3.54 Å data, the analytical absorption correction outperforms spherical harmonics in the longer-wavelength data (λ = 4.13 Å), which is also reflected in the reduced amount of data being required for successful experimental phasing.

BL19U1, an energy-tunable protein complex crystallography beamline at the Shanghai Synchrotron Radiation Facility, has emerged as one of the most productive MX beamlines since opening to the public in July 2015. As of October 2023, it has contributed to over 2000 protein structures deposited in the Protein Data Bank (PDB), resulting in the publication of more than 1000 scientific papers. In response to increasing interest in structure-based drug design utilizing X-ray crystallography for fragment library screening, enhancements have been implemented in both hardware and data collection systems on the beamline to optimize efficiency. Hardware upgrades include the transition from MD2 to MD2S for the diffractometer, alongside the installation of a humidity controller featuring a rapid nozzle exchanger. This allows users to opt for either low-temperature or room-temperature data collection modes. The control system has been upgraded from Blu-Ice to MXCuBE3, which supports website-mode data collection, providing enhanced compatibility and easy expansion with new features. An automated data processing pipeline has also been developed to offer users real-time feedback on data quality.

Controlling the shape and size dispersivity and crystallinity of nanoparticles (NPs) has been a challenge in identifying these parameters' role in the physical and chemical properties of NPs. The need for reliable quantitative tools for analyzing the dispersivity and crystallinity of NPs is a considerable problem in optimizing scalable synthesis routes capable of controlling NP properties. The most common tools are electron microscopy (EM) and X-ray scattering techniques. However, each technique has different susceptibility to these parameters, implying that more than one technique is necessary to characterize NP systems with maximum reliability. Wide-angle X-ray scattering (WAXS) is mandatory to access information on crystallinity. In contrast, EM or small-angle X-ray scattering (SAXS) is required to access information on whole NP sizes. EM provides average values on relatively small ensembles in contrast to the bulk values accessed by X-ray techniques. Besides the fact that the SAXS and WAXS techniques have different susceptibilities to size distributions, SAXS is easily affected by NP–NP interaction distances. Because of all the variables involved, there have yet to be proposed methodologies for cross-analyzing data from two techniques that can provide reliable quantitative results of dispersivity and crystallinity. In this work, a SAXS/WAXS-based methodology is proposed for simultaneously quantifying size distribution and degree of crystallinity of NPs. The most reliable easy-to-access size result for each technique is demonstrated by computer simulation. Strategies on how to compare these results and how to identify NP–NP interaction effects underneath the SAXS intensity curve are presented. Experimental results are shown for cubic-like CeO2 NPs. WAXS size results from two analytical procedures are compared, line-profile fitting of individual diffraction peaks in opposition to whole pattern fitting. The impact of shape dispersivity is also evaluated. Extension of the proposed methodology for cross-analyzing EM and WAXS data is possible.

Serial crystallography (SX) involves combining observations from a very large number of diffraction patterns coming from crystals in random orientations. To compile a complete data set, these patterns must be indexed (i.e. their orientation determined), integrated and merged. Introduced here is TORO (Torch-powered robust optimization) Indexer, a robust and adaptable indexing algorithm developed using the PyTorch framework. TORO is capable of operating on graphics processing units (GPUs), central processing units (CPUs) and other hardware accelerators supported by PyTorch, ensuring compatibility with a wide variety of computational setups. In tests, TORO outpaces existing solutions, indexing thousands of frames per second when running on GPUs, which positions it as an attractive candidate to produce real-time indexing and user feedback. The algorithm streamlines some of the ideas introduced by previous indexers like DIALS real-space grid search [Gildea, Waterman, Parkhurst, Axford, Sutton, Stuart, Sauter, Evans & Winter (2014). Acta Cryst. D70, 2652–2666] and XGandalf [Gevorkov, Yefanov, Barty, White, Mariani, Brehm, Tolstikova, Grigat & Chapman (2019). Acta Cryst. A75, 694–704] and refines them using faster and principled robust optimization techniques which result in a concise code base consisting of less than 500 lines. On the basis of evaluations across four proteins, TORO consistently matches, and in certain instances outperforms, established algorithms such as XGandalf and MOSFLM [Powell (1999). Acta Cryst. D55, 1690–1695], occasionally amplifying the quality of the consolidated data while achieving superior indexing speed. The inherent modularity of TORO and the versatility of PyTorch code bases facilitate its deployment into a wide array of architectures, software platforms and bespoke applications, highlighting its prospective significance in SX.

Conformational change mediates the biological functions of macromolecules. Crystallographic measurements can map these changes with extraordinary sensitivity as a function of mutations, ligands and time. A popular method for detecting structural differences between crystallographic data sets is the isomorphous difference map. These maps combine the phases of a chosen reference state with the observed changes in structure factor amplitudes to yield a map of changes in electron density. Such maps are much more sensitive to conformational change than structure refinement is, and are unbiased in the sense that observed differences do not depend on refinement of the perturbed state. However, even modest changes in unit-cell properties can render isomorphous difference maps useless. This is unnecessary. Described here is a generalized procedure for calculating observed difference maps that retains the high sensitivity to conformational change and avoids structure refinement of the perturbed state. This procedure is implemented in an open-source Python package, MatchMaps, that can be run in any software environment supporting PHENIX [Liebschner et al. (2019). Acta Cryst. D75, 861–877] and CCP4 [Agirre et al. (2023). Acta Cryst. D79, 449–461]. Worked examples show that MatchMaps `rescues' observed difference electron-density maps for poorly isomorphous crystals, corrects artifacts in nominally isomorphous difference maps, and extends to detecting differences across copies within the asymmetric unit or across altogether different crystal forms.

Structural modelling of operando pair distribution function (PDF) data of complex functional materials can be highly challenging. To aid the understanding of complex operando PDF data, this article demonstrates a toolbox for PDF analysis. The tools include denoising using principal component analysis together with the structureMining, similarityMapping and nmfMapping apps available through the online service `PDF in the cloud' (PDFitc, https://pdfitc.org/). The toolbox is used for both ex situ and operando PDF data for 3 nm TiO2-bronze nanocrystals, which function as the active electrode material in a Li-ion battery. The tools enable structural modelling of the ex situ and operando PDF data, revealing two pristine TiO2 phases (bronze and anatase) and two lithiated LixTiO2 phases (lithiated versions of bronze and anatase), and the phase evolution during galvanostatic cycling is characterized.

Characterization of crystallization processes in situ is of great importance to furthering knowledge of how nucleation and growth processes direct the assembly of organic and inorganic materials in solution and, critically, understanding the influence that these processes have on the final physico-chemical properties of the resulting solid form. With careful specification and design, as demonstrated here, it is now possible to bring combined X-ray diffraction and Raman spectroscopy, coupled to a range of fully integrated segmented and continuous flow platforms, to the laboratory environment for in situ data acquisition for timescales of the order of seconds. The facility used here (Flow-Xl) houses a diffractometer with a micro-focus Cu Kα rotating anode X-ray source and a 2D hybrid photon-counting detector, together with a Raman spectrometer with 532 and 785 nm lasers. An overview of the diffractometer and spectrometer setup is given, and current sample environments for flow crystallization are described. Commissioning experiments highlight the sensitivity of the two instruments for time-resolved in situ data collection of samples in flow. Finally, an example case study to monitor the batch crystallization of sodium sulfate from aqueous solution, by tracking both the solute and solution phase species as a function of time, highlights the applicability of such measurements in determining the kinetics associated with crystallization processes. This work illustrates that the Flow-Xl facility provides high-resolution time-resolved in situ structural phase information through diffraction data together with molecular-scale solution data through spectroscopy, which allows crystallization mechanisms and their associated kinetics to be analysed in a laboratory setting.

The first Federation of European Biochemical Societies Advanced Course on macromolecular crystallization was launched in the Czech Republic in October 2004. Over the past two decades, the course has developed into a distinguished event, attracting students, early career postdoctoral researchers and lecturers. The course topics include protein purification, characterization and crystallization, covering the latest advances in the field of structural biology. The many hands-on practical exercises enable a close interaction between students and teachers and offer the opportunity for students to crystallize their own proteins. The course has a broad and lasting impact on the scientific community as participants return to their home laboratories and act as nuclei by communicating and implementing their newly acquired knowledge and skills.

Energy-dispersive Laue diffraction (EDLD) is a powerful method to obtain position-resolved texture information in inhomogeneous biological samples without the need for sample rotation. This study employs EDLD texture scanning to investigate the impact of two salivary peptides, statherin (STN) and histatin-1 (HTN) 21 N-terminal peptides (STN21 and HTN21), on the crystallographic structure of dental enamel. These proteins are known to play crucial roles in dental caries progression. Three healthy incisors were randomly assigned to three groups: artificially demineralized, demineralized after HTN21 peptide pre-treatment and demineralized after STN21 peptide pre-treatment. To understand the micro-scale structure of the enamel, each specimen was scanned from the enamel surface to a depth of 250 µm using microbeam EDLD. Via the use of a white beam and a pixelated detector, where each pixel functions as a spectrometer, pole figures were obtained in a single exposure at each measurement point. The results revealed distinct orientations of hydroxyapatite crystallites and notable texture variation in the peptide-treated demineralized samples compared with the demineralized control. Specifically, the peptide-treated demineralized samples exhibited up to three orientation populations, in contrast to the demineralized control which displayed only a single orientation population. The texture index of the demineralized control (2.00 ± 0.21) was found to be lower than that of either the STN21 (2.32 ± 0.20) or the HTN21 (2.90 ± 0.46) treated samples. Hence, texture scanning with EDLD gives new insights into dental enamel crystallite orientation and links the present understanding of enamel demineralization to the underlying crystalline texture. For the first time, the feasibility of EDLD texture measurements for quantitative texture evaluation in demineralized dental enamel samples is demonstrated.

A procedure is presented to exactly obtain the apparent average crystallite size (ACS) of powder samples using standard in-house powder diffraction experiments without any restriction originating from the Scherrer equation. Additionally, the crystallite size distribution within the sample can be evaluated. To achieve this, powder diffractograms are background corrected and long-range radial distribution functions G(r) up to 300 nm are calculated from the diffraction data. The envelope function fenv of G(r) is approximated by a procedure determining the absolute maxima of G(r) in a certain interval (r range). Fitting of an ACS distribution envelope function to this approximation gives the ACS and its distribution. The method is tested on diffractograms of LaB6 standard reference materials measured with different wavelengths to demonstrate the validity of the approach and to clarify the influence of the wavelength used. The latter results in a general description of the maximum observable average crystallite size, which depends on the instrument and wavelength used. The crystallite site distribution is compared with particle size distributions based on transmission electron microscopy investigations, providing an approximation of the average number of crystallites per particle.

Compared with batch and vapor diffusion methods, counter diffusion can generate larger and higher-quality protein crystals yielding improved diffraction data and higher-resolution structures. Typically, counter-diffusion experiments are conducted in elongated chambers, such as glass capillaries, and the crystals are either directly measured in the capillary or extracted and mounted at the X-ray beamline. Despite the advantages of counter-diffusion protein crystallization, there are few fixed-target devices that utilize counter diffusion for crystallization. In this article, different designs of user-friendly counter-diffusion chambers are presented which can be used to grow large protein crystals in a 2D polymer microfluidic fixed-target chip. Methods for rapid chip fabrication using commercially available thin-film materials such as Mylar, propyl­ene and Kapton are also detailed. Rules of thumb are provided to tune the nucleation and crystal growth to meet users' needs while minimizing sample consumption. These designs provide a reliable approach to forming large crystals and maintaining their hydration for weeks and even months. This allows ample time to grow, select and preserve the best crystal batches before X-ray beam time. Importantly, the fixed-target microfluidic chip has a low background scatter and can be directly used at beamlines without any crystal handling, enabling crystal quality to be preserved. The approach is demonstrated with serial diffraction of photoactive yellow protein, yielding 1.32 Å resolution at room temperature. Fabrication of this standard microfluidic chip with commercially available thin films greatly simplifies fabrication and provides enhanced stability under vacuum. These advances will further broaden microfluidic fixed-target utilization by crystallographers.

Online courses and innovative teaching methods have triggered a trend in education, where the integration of multimedia, online resources and interactive tools is reshaping the view of both virtual and traditional classrooms. The use of interactive tools extends beyond the boundaries of the physical classroom, offering students the flexibility to access materials at their own speed and convenience and enhancing their learning experience. In the field of crystallography, there are a wide variety of free online resources such as web pages, interactive applets, databases and programs that can be implemented in fundamental crystallography courses for different academic levels and curricula. This paper discusses a variety of resources that can be helpful for crystallographic symmetry handling and visualization, discussing four specific resources in detail: the Bilbao Crystallographic Server, the Cambridge Structural Database, VESTA and Jmol. The utility of these resources is explained and shown by several illustrative examples.

This article presents a web-based framework to build a database without in-depth programming knowledge given a set of CIF dictionaries and a collection of CIFs. The framework consists of two main elements: the public site that displays the information contained in the CIFs in an ordered manner, and the restricted administrative site which defines how that information is stored, processed and, eventually, displayed. Thus, the web application allows users to easily explore, filter and access the data, download the original CIFs, and visualize the structures via JSmol. The modulated structures open database B-IncStrDB, the official International Union of Crystallography repository for this type of material and available through the Bilbao Crystallographic Server, has been re-implemented following the proposed framework.