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South Korea's first Nobel laureate in literature, Han Kang, said she is surprised and absolutely honored to have won the prestigious prize. In a phone interview with the Swedish Academy, which awards the Nobel prizes, Han expressed appreciation for all the support she’s received. Asked for a comment ...

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How can CO2 be stored? | CCS  British Geological Survey

The results of the seventh blind test of crystal structure prediction are presented, focusing on structure generation methods.

A detailed structural characterization of the δ1-MnZn9.7 phase is presented.

The results of the seventh blind test of crystal structure prediction are presented, focusing on structure ranking methods.

The crystal structure of calcium atorvastatin trihydrate was redetermined from previously published synchrotron powder diffraction data to give a much-improved agreement with two independent density-functional theory calculations.

A comparative description is presented of the symmetry and the magnetic structures found in the family of double perovskites A2MnB'O6 (mainly B' = Co and some Ni compounds for comparative purposes).

The hydro­thermal synthesis and structural characterization of four novel 3D pillared-layer metal–organic frameworks are studied, revealing how the malleability of copper coordination geometries drives diverse supramolecular isomerism. The findings provide new insights into designing advanced hybrid materials with tailored properties, emphasizing the significant role of reaction conditions and metal ion flexibility in determining network topologies.

A detailed structural analysis of the Zn-rich δ1-MnZn9.7 phase using single-crystal X-ray diffraction is presented. The δ1 phase has been synthesized by the high-temperature synthetic route. The structure crystallizes in space group P63/mmc (Pearson symbol hP556) with unit-cell parameters: a = b = 12.9051 (2) Å and c = 57.640 (1) Å. The 556 atoms are distributed over 52 Wyckoff positions in the hexagonal unit cell: seven ordered Mn sites, 37 ordered Zn sites and eight positionally disordered Zn sites. The structure predominantly consists of Frank–Kasper polyhedra (endohedral icosahedra Zn12 and icosioctahedron Zn16) and four distinct types of glue Zn atoms. The structure comprises a 127-atom supercluster (Mn13Zn114), a 38-atom extended Pearce cluster (Mn3Zn35), a 46-atom L-tetrahedron (Mn4Zn42), a Friauf polyhedron (Zn17), a disordered icosahedral cluster (MnZn12) and four glue Zn atoms. Positionally disordered Zn sites around an Mn site can be visualized as the superimposition of three differently oriented Zn12 icosahedra.

With ever-improving quantum-mechanical computational methods, the accuracy requirements for experimental crystal structures increase. The crystal structure of calcium atorvastatin trihydrate, which has 56 degrees of freedom when determined with a real-space algorithm, was determined from powder diffraction data by Hodge et al. [Powder Diffr. (2020), 35, 136–143]. The crystal structure was a good fit to the experimental data, indicating that the electron density had been captured essentially correctly, but two independent quantum-mechanical calculations disagreed with the experimental structure and with each other. Using the same experimental data, the crystal structure was redetermined from scratch and it was shown that it can be reproduced within a root-mean-square Cartesian displacement of 0.1 Å by two independent quantum-mechanical calculations. The consequences for the calculated energies and solubilities are described.

Accurate modeling of conformational energies is key to the crystal structure prediction of conformational polymorphs. Focusing on molecules XXXI and XXXII from the seventh blind test of crystal structure prediction, this study employs various electronic structure methods up to the level of domain-local pair natural orbital coupled cluster singles and doubles with perturbative triples [DLPNO-CCSD(T1)] to benchmark the conformational energies and to assess their impact on the crystal energy landscapes. Molecule XXXI proves to be a relatively straightforward case, with the conformational energies from generalized gradient approximation (GGA) functional B86bPBE-XDM changing only modestly when using more advanced density functionals such as PBE0-D4, ωB97M-V, and revDSD-PBEP86-D4, dispersion-corrected second-order Møller–Plesset perturbation theory (SCS-MP2D), or DLPNO-CCSD(T1). In contrast, the conformational energies of molecule XXXII prove difficult to determine reliably, and variations in the computed conformational energies appreciably impact the crystal energy landscape. Even high-level methods such as revDSD-PBEP86-D4 and SCS-MP2D exhibit significant disagreements with the DLPNO-CCSD(T1) benchmarks for molecule XXXII, highlighting the difficulty of predicting conformational energies for complex, drug-like molecules. The best-converged predicted crystal energy landscape obtained here for molecule XXXII disagrees significantly with what has been inferred about the solid-form landscape experimentally. The identified limitations of the calculations are probably insufficient to account for the discrepancies between theory and experiment on molecule XXXII, and further investigation of the experimental solid-form landscape would be valuable. Finally, assessment of several semi-empirical methods finds r2SCAN-3c to be the most promising, with conformational energy accuracy intermediate between the GGA and hybrid functionals and a low computational cost.

The seventh blind test of crystal structure prediction (CSP) methods substantially increased the level of complexity of the target compounds relative to the previous tests organized by the Cambridge Crystallographic Data Centre. In this work, the performance of density-functional methods is assessed using numerical atomic orbitals and the exchange-hole dipole moment dispersion correction (XDM) for the energy-ranking phase of the seventh blind test. Overall, excellent performance was seen for the two rigid molecules (XXVII, XXVIII) and for the organic salt (XXXIII). However, for the agrochemical (XXXI) and pharmaceutical (XXXII) targets, the experimental polymorphs were ranked fairly high in energy amongst the provided candidate structures and inclusion of thermal free-energy corrections from the lattice vibrations was found to be essential for compound XXXI. Based on these results, it is proposed that the importance of vibrational free-energy corrections increases with the number of rotatable bonds.

A seventh blind test of crystal structure prediction has been organized by the Cambridge Crystallographic Data Centre. The results are presented in two parts, with this second part focusing on methods for ranking crystal structures in order of stability. The exercise involved standardized sets of structures seeded from a range of structure generation methods. Participants from 22 groups applied several periodic DFT-D methods, machine learned potentials, force fields derived from empirical data or quantum chemical calculations, and various combinations of the above. In addition, one non-energy-based scoring function was used. Results showed that periodic DFT-D methods overall agreed with experimental data within expected error margins, while one machine learned model, applying system-specific AIMnet potentials, agreed with experiment in many cases demonstrating promise as an efficient alternative to DFT-based methods. For target XXXII, a consensus was reached across periodic DFT methods, with consistently high predicted energies of experimental forms relative to the global minimum (above 4 kJ mol−1 at both low and ambient temperatures) suggesting a more stable polymorph is likely not yet observed. The calculation of free energies at ambient temperatures offered improvement of predictions only in some cases (for targets XXVII and XXXI). Several avenues for future research have been suggested, highlighting the need for greater efficiency considering the vast amounts of resources utilized in many cases.

A seventh blind test of crystal structure prediction was organized by the Cambridge Crystallographic Data Centre featuring seven target systems of varying complexity: a silicon and iodine-containing molecule, a copper coordination complex, a near-rigid molecule, a cocrystal, a polymorphic small agrochemical, a highly flexible polymorphic drug candidate, and a polymorphic morpholine salt. In this first of two parts focusing on structure generation methods, many crystal structure prediction (CSP) methods performed well for the small but flexible agrochemical compound, successfully reproducing the experimentally observed crystal structures, while few groups were successful for the systems of higher complexity. A powder X-ray diffraction (PXRD) assisted exercise demonstrated the use of CSP in successfully determining a crystal structure from a low-quality PXRD pattern. The use of CSP in the prediction of likely cocrystal stoichiometry was also explored, demonstrating multiple possible approaches. Crystallographic disorder emerged as an important theme throughout the test as both a challenge for analysis and a major achievement where two groups blindly predicted the existence of disorder for the first time. Additionally, large-scale comparisons of the sets of predicted crystal structures also showed that some methods yield sets that largely contain the same crystal structures.

Recent advances in quantum crystallography have shown that, beyond conventional charge density refinement, a one-electron reduced density matrix (1-RDM) satisfying N-representability conditions can be reconstructed using jointly experimental X-ray structure factors and directional Compton profiles (DCP) through semidefinite programming. So far, such reconstruction methods for 1-RDM, not constrained to idempotency, have been tested only on a toy model system (CO2). In this work, a new method is assessed on crystalline urea [CO(NH2)2] using static (0 K) and dynamic (50 K) artificial experimental data. An improved model, including symmetry constraints and frozen core-electron contribution, is introduced to better handle the increasing system complexity. Reconstructed 1-RDMs, deformation densities and DCP anisotropy are analysed, and it is demonstrated that the changes in the model significantly improve the reconstruction quality, even when there is insufficient information and data corruption. The robustness of the model and the strategy are thus shown to be well adapted to address the reconstruction problem from actual experimental scattering data.

The impact of sample thickness and experimental noise on angular correlation analysis from scanning electron nanobeam diffraction patterns of disordered carbon are investigated and analyzed regarding the interpretability of the analysis results.

A 6D structure model for face-centred icosahedral quasicrystals consisting of so-called pseudo-Mackay and mini-Bergman-type atomic clusters is proposed based on the structure model of the Al69.1Pd22Cr2.1Fe6.8 3/2 cubic approximant crystal (with space group Pa3, a = 40.5 Å) [Fujita et al. (2013). Acta Cryst. A69, 322–340]. The cluster centres form an icosahedral close sphere packing generated by the occupation domains similar to those in the model proposed by Katz & Gratias [J. Non-Cryst. Solids (1993), 153–154, 187–195], but their size is smaller by a factor τ2 [τ = (1 + (5)1/2)/2]. The clusters cover approximately 99.46% of the atomic structure, and the cluster arrangement exhibits 15 and 19 different local configurations, respectively, for the pseudo-Mackay and mini-Bergman-type clusters. The occupation domains that generate cluster shells are modelled and discussed in terms of structural disorder and local reorganization of the cluster arrangements (phason flip).

Small-angle X-ray scattering (SAXS) is a widely used method for nanoparticle characterization. A common approach to analysing nanoparticles in solution by SAXS involves fitting the curve using a parametric model that relates real-space parameters, such as nanoparticle size and electron density, to intensity values in reciprocal space. Selecting the optimal model is a crucial step in terms of analysis quality and can be time-consuming and complex. Several studies have proposed effective methods, based on machine learning, to automate the model selection step. Deploying these methods in software intended for both researchers and industry raises several issues. The diversity of SAXS instrumentation requires assessment of the robustness of these methods on data from various machine configurations, involving significant variations in the q-space ranges and highly variable signal-to-noise ratios (SNR) from one data set to another. In the case of laboratory instrumentation, data acquisition can be time-consuming and there is no universal criterion for defining an optimal acquisition time. This paper presents an approach that revisits the nanoparticle model selection method proposed by Monge et al. [Acta Cryst. (2024), A80, 202–212], evaluating and enhancing its robustness on data from device configurations not seen during training, by expanding the data set used for training. The influence of SNR on predictor robustness is then assessed, improved, and used to propose a stopping criterion for optimizing the trade-off between exposure time and data quality.

To characterize an electron beam, visible synchrotron light is often used and dedicated beamlines at synchrotron sources are becoming a more common feature as instruments and methods for the diagnostics are, along with the accelerators, further developed. At KARA (Karlsruhe Research Accelerator), such a beamline exists and is based on a typical infrared/visible-light configuration. From experience at such beamlines no significant radiation was expected (dose rates larger than 0.5 µSv h−1). This was found not to be the case and a higher dose was measured which fortunately could be shielded to an acceptable level with 0.3 mm of aluminium foil or 2.0 mm of Pyrex glass. The presence of this radiation led to further investigation by both experiment and calculation. A custom setup using a silicon drift detector for energy-dispersive spectroscopy (Ketek GmbH) and attenuation experiments showed the radiation to be predominantly copper K-shell fluorescence and is confirmed by calculation. The measurement of secondary radiation from scattering of synchrotron and other radiation, and its calculation, is important for radiation protection, and, although a lot of experience exists and methods for radiation protection are well established, changes in machine, beamlines and experiments mean a constant appraisal is needed.

Fourth-generation synchrotron storage rings represent a significant milestone in synchrotron technology, offering outstandingly bright and tightly focused X-ray beams for a wide range of scientific applications. However, due to their inherently tight magnetic lattices, these storage rings have posed critical challenges for accessing lower-energy radiation, such as infrared (IR) and THz. Here the first-ever IR beamline to be installed and to operate at a fourth-generation synchrotron storage ring is introduced. This work encompasses several notable advancements, including a thorough examination of the new IR source at Sirius, a detailed description of the radiation extraction scheme, and the successful validation of our optical concept through both measurements and simulations. This optimal optical setup has enabled us to achieve an exceptionally wide frequency range for our nanospectroscopy experiments. Through the utilization of synchrotron IR nanospectroscopy on biological and hard matter samples, the practicality and effectiveness of this beamline has been successfully demonstrated. The advantages of fourth-generation synchrotron IR sources, which can now operate with unparalleled stability as a result of the stringent requirements for producing low-emittance X-rays, are emphasized.

The infrared beamline at BESSY II storage ring was upgraded recently to extend the capabilities of infrared microscopy. The endstations available at the beamline are now facilitating improved characterization of molecules and materials at different length scales and time resolutions. Here, the current outline of the beamline is reported and an overview of the endstations available is given. In particular, the first results obtained by using a new microscope for nano-spectroscopy that was implemented are presented. The capabilities of the scattering-type near-field optical microscope (s-SNOM) are demonstrated by investigating cellulose microfibrils, representing nanoscopic objects of a hierarchical structure. It is shown that the s-SNOM coupled to the beamline allows imaging to be performed with a spatial resolution of less than 30 nm and infrared spectra to be collected from an effective volume of less than 30 nm × 30 nm × 12 nm. Potential steps for further optimization of the beamline performance are discussed.

Deflectometric profilometers are used to precisely measure the form of beam shaping optics of synchrotrons and X-ray free-electron lasers. They often utilize autocollimators which measure slope by evaluating the displacement of a reticle image on a detector. Based on our privileged access to the raw image data of an autocollimator, novel strategies to reduce the systematic measurement errors by using a set of overlapping images of the reticle obtained at different positions on the detector are discussed. It is demonstrated that imaging properties such as, for example, geometrical distortions and vignetting, can be extracted from this redundant set of images without recourse to external calibration facilities. This approach is based on the fact that the properties of the reticle itself do not change – all changes in the reticle image are due to the imaging process. Firstly, by combining interpolation and correlation, it is possible to determine the shift of a reticle image relative to a reference image with minimal error propagation. Secondly, the intensity of the reticle image is analysed as a function of its position on the CCD and a vignetting correction is calculated. Thirdly, the size of the reticle image is analysed as a function of its position and an imaging distortion correction is derived. It is demonstrated that, for different measurement ranges and aperture diameters of the autocollimator, reductions in the systematic errors of up to a factor of four to five can be achieved without recourse to external measurements.

Transition-metal nitro­gen-doped carbons (TM-N-C) are emerging as a highly promising catalyst class for several important electrocatalytic processes, including the electrocatalytic CO2 reduction reaction (CO2RR). The unique local environment around the singly dispersed metal site in TM-N-C catalysts is likely to be responsible for their catalytic properties, which differ significantly from those of bulk or nanostructured catalysts. However, the identification of the actual working structure of the main active units in TM-N-C remains a challenging task due to the fluctional, dynamic nature of these catalysts, and scarcity of experimental techniques that could probe the structure of these materials under realistic working conditions. This issue is addressed in this work and the local atomistic and electronic structure of the metal site in a Co–N–C catalyst for CO2RR is investigated by employing time-resolved operando X-ray absorption spectroscopy (XAS) combined with advanced data analysis techniques. This multi-step approach, based on principal component analysis, spectral decomposition and supervised machine learning methods, allows the contributions of several co-existing species in the working Co–N–C catalysts to be decoupled, and their XAS spectra deciphered, paving the way for understanding the CO2RR mechanisms in the Co–N–C catalysts, and further optimization of this class of electrocatalytic systems.

The simulation of EXAFS spectra of thin films via ab initio methods is discussed. The procedure for producing the spectra is presented as well as an application to a two-dimensional material (WSe2) where the effectiveness of this method in reproducing the spectrum and the linear dichroic response is shown. A series of further examples in which the method has been employed for the structural determination of materials are given.

Modeling the behavior of a prototype cantilevered X-ray adaptive mirror (held from one end) demonstrates its potential for use on high-performance X-ray beamlines. Similar adaptive mirrors are used on X-ray beamlines to compensate optical aberrations, control wavefronts and tune mirror focal distances at will. Controlled by 1D arrays of piezoceramic actuators, these glancing-incidence mirrors can provide nanometre-scale surface shape adjustment capabilities. However, significant engineering challenges remain for mounting them with low distortion and low environmental sensitivity. Finite-element analysis is used to predict the micron-scale full actuation surface shape from each channel and then linear modeling is applied to investigate the mirrors' ability to reach target profiles. Using either uniform or arbitrary spatial weighting, actuator voltages are optimized using a Moore–Penrose matrix inverse, or pseudoinverse, revealing a spatial dependence on the shape fitting with increasing fidelity farther from the mount.

The inherent ambiguity in reconstructed images from coherent diffraction imaging (CDI) poses an intrinsic challenge, as images derived from the same dataset under varying initial conditions often display inconsistencies. This study introduces a method that employs the Noise2Noise approach combined with neural networks to effectively mitigate these ambiguities. We applied this methodology to hundreds of ambiguous reconstructed images retrieved from a single diffraction pattern using a conventional retrieval algorithm. Our results demonstrate that ambiguous features in these reconstructions are effectively treated as inter-reconstruction noise and are significantly reduced. The post-Noise2Noise treated images closely approximate the average and singular value decomposition analysis of various reconstructions, providing consistent and reliable reconstructions.

The Circular Electron–Positron Collider (CEPC) in China can also work as an excellent powerful synchrotron light source, which can generate high-quality synchrotron radiation. This synchrotron radiation has potential advantages in the medical field as it has a broad spectrum, with energies ranging from visible light to X-rays used in conventional radiotherapy, up to several megaelectronvolts. FLASH radiotherapy is one of the most advanced radiotherapy modalities. It is a radiotherapy method that uses ultra-high dose rate irradiation to achieve the treatment dose in an instant; the ultra-high dose rate used is generally greater than 40 Gy s−1, and this type of radiotherapy can protect normal tissues well. In this paper, the treatment effect of CEPC synchrotron radiation for FLASH radiotherapy was evaluated by simulation. First, a Geant4 simulation was used to build a synchrotron radiation radiotherapy beamline station, and then the dose rate that the CEPC can produce was calculated. A physicochemical model of radiotherapy response kinetics was then established, and a large number of radiotherapy experimental data were comprehensively used to fit and determine the functional relationship between the treatment effect, dose rate and dose. Finally, the macroscopic treatment effect of FLASH radiotherapy was predicted using CEPC synchrotron radiation through the dose rate and the above-mentioned functional relationship. The results show that the synchrotron radiation beam from the CEPC is one of the best beams for FLASH radiotherapy.

The extraction and purification procedures, crystallization and crystal structure refinement (single-crystal X-ray data) of germacrone type II, C15H22O, are presented. The structural results are compared with a previous powder X-ray synchrotron study [Kaduk et al. (2022). Powder Diffr. 37, 98–104], revealing significant improvements in terms of accuracy and precision. Hirshfeld atom refinement (HAR), as well as Hirshfeld surface analysis, give insight into the inter­molecular inter­actions of germacrone type II.

In the title compound, C14H14O3, the dihedral angle between the aromatic rings is 39.76 (9)°. In the crystal, the mol­ecules associate to form centrosymmetric acid–acid dimers linked by pairwise O—H⋯O hydrogen bonds. The precision of the geometric parameters in the present single-crystal study is about an order of magnitude better than the previous powder diffraction study [Chattopadhyay et al. (2013). CrystEngComm, 15, 1077–1085].

In the title compound, C21H27BF2N2O4, a highly fluorescent boron–dipyrromethene dye, the methyl­propionate moieties have different conformations. In the crystal, weak C—H⋯F and C—H⋯O inter­actions link the mol­ecules. Some optical properties are presented.

The crystal structure of a new 1:1 cocrystal of carbamazepine and S-naproxen (C15H12N2O·C14H14O3) was solved from powder X-ray diffraction (PXRD). The PXRD pattern was measured at the high-resolution beamline CRISTAL at synchrotron SOLEIL (France). The structure was solved using Monte Carlo simulated annealing, then refined with Rietveld refinement. The positions of the H atoms were obtained from density functional theory (DFT) ground-state calculations. The symmetry is ortho­rhom­bic with the space group P212121 (No. 19) and the following lattice parameters: a = 33.5486 (9), b = 26.4223 (6), c = 5.3651 (10) Å and V = 4755.83 (19) Å3.

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.

Several proteins from plant pathogenesis-related family 10 (PR10) are highly abundant in the latex of opium poppy and have recently been shown to play diverse and important roles in the biosynthesis of benzylisoquinoline alkaloids (BIAs). The recent determination of the first crystal structures of PR10-10 showed how large conformational changes in a surface loop and adjacent β-strand are coupled to the binding of BIA compounds to the central hydrophobic binding pocket. A more detailed analysis of these conformational changes is now reported to further clarify how ligand binding is coupled to the formation and cleavage of an intermolecular disulfide bond that is only sterically allowed when the BIA binding pocket is empty. To decouple ligand binding from disulfide-bond formation, each of the two highly conserved cysteine residues (Cys59 and Cys155) in PR10-10 was replaced with serine using site-directed mutagenesis. Crystal structures of the Cys59Ser mutant were determined in the presence of papaverine and in the absence of exogenous BIA compounds. A crystal structure of the Cys155Ser mutant was also determined in the absence of exogenous BIA compounds. All three of these crystal structures reveal conformations similar to that of wild-type PR10-10 with bound BIA compounds. In the absence of exogenous BIA compounds, the Cys59Ser and Cys155Ser mutants appear to bind an unidentified ligand or mixture of ligands that was presumably introduced during expression of the proteins in Escherichia coli. The analysis of conformational changes triggered by the binding of BIA compounds suggests a molecular mechanism coupling ligand binding to the disruption of an intermolecular disulfide bond. This mechanism may be involved in the regulation of biosynthetic reactions in plants and possibly other organisms.

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.

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.

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.

This paper uses deep learning to present a proof-of-concept for data-driven chemistry in single-molecule magnets (SMMs). Previous discussions within SMM research have proposed links between molecular structures (crystal structures) and single-molecule magnetic properties; however, these have only interpreted the results. Therefore, this study introduces a data-driven approach to predict the properties of SMM structures using deep learning. The deep-learning model learns the structural features of the SMM molecules by extracting the single-molecule magnetic properties from the 3D coordinates presented in this paper. The model accurately determined whether a molecule was a single-molecule magnet, with an accuracy rate of approximately 70% in predicting the SMM properties. The deep-learning model found SMMs from 20 000 metal complexes extracted from the Cambridge Structural Database. Using deep-learning models for predicting SMM properties and guiding the design of novel molecules is promising.

The discovery of lytic polysaccharide monooxygenases (LPMOs), a family of copper-dependent enzymes that play a major role in polysaccharide degradation, has revealed the importance of oxidoreductases in the biological utilization of biomass. In fungi, a range of redox proteins have been implicated as working in harness with LPMOs to bring about polysaccharide oxidation. In bacteria, less is known about the interplay between redox proteins and LPMOs, or how the interaction between the two contributes to polysaccharide degradation. We therefore set out to characterize two previously unstudied proteins from the shipworm symbiont Teredinibacter turnerae that were initially identified by the presence of carbohydrate binding domains appended to uncharacterized domains with probable redox functions. Here, X-ray crystal structures of several domains from these proteins are presented together with initial efforts to characterize their functions. The analysis suggests that the target proteins are unlikely to function as LPMO electron donors, raising new questions as to the potential redox functions that these large extracellular multi-haem-containing c-type cytochromes may perform in these bacteria.

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.

Ultra-intense, ultra-fast X-ray free-electron lasers (XFELs) enable the imaging of single protein molecules under ambient temperature and pressure. A crucial aspect of structure reconstruction involves determining the relative orientations of each diffraction pattern and recovering the missing phase information. In this paper, we introduce a predicted model-aided algorithm for orientation determination and phase retrieval, which has been tested on various simulated datasets and has shown significant improvements in the success rate, accuracy and efficiency of XFEL data reconstruction.

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.

Ultrafast, high-intensity X-ray free-electron lasers can perform diffraction imaging of single protein molecules. Various algorithms have been developed to determine the orientation of each single-particle diffraction pattern and reconstruct the 3D diffraction intensity. Most of these algorithms rely on the premise that all diffraction patterns originate from identical protein molecules. However, in actual experiments, diffraction patterns from multiple different molecules may be collected simultaneously. Here, we propose a predicted model-aided one-step classification–multireconstruction algorithm that can handle mixed diffraction patterns from various molecules. The algorithm uses predicted structures of different protein molecules as templates to classify diffraction patterns based on correlation coefficients and determines orientations using a correlation maximization method. Tests on simulated data demonstrated high accuracy and efficiency in classification and reconstruction.

The accuracy of the information in the Protein Data Bank (PDB) is of great importance for the myriad downstream applications that make use of protein structural information. Despite best efforts, the occasional introduction of errors is inevitable, especially where the experimental data are of limited resolution. A novel protein structure validation approach based on spotting inconsistencies between the residue contacts and distances observed in a structural model and those computationally predicted by methods such as AlphaFold2 has previously been established. It is particularly well suited to the detection of register errors. Importantly, this new approach is orthogonal to traditional methods based on stereochemistry or map–model agreement, and is resolution independent. Here, thousands of likely register errors are identified by scanning 3–5 Å resolution structures in the PDB. Unlike most methods, the application of this approach yields suggested corrections to the register of affected regions, which it is shown, even by limited implementation, lead to improved refinement statistics in the vast majority of cases. A few limitations and confounding factors such as fold-switching proteins are characterized, but this approach is expected to have broad application in spotting potential issues in current accessions and, through its implementation and distribution in CCP4, helping to ensure the accuracy of future depositions.

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.

It is pointed out that many authors are unaware that the particular choice of unit-cell origin determines the irreducible representations to which octahedral tilts in perovskites belong. Furthermore, a recommendation is made that the preferred option is with the origin at the B-cation site rather than that of the A site.

Synthesis experiments were conducted in the quaternary system K2O–Na2O–CaO–SiO2, resulting in the formation of a previously unknown compound with the composition K0.72Na1.71Ca5.79Si6O19. Single crystals of sufficient size and quality were recovered from a starting mixture with a K2O:Na2O:CaO:SiO2 molar ratio of 1.5:0.5:2:3. The mixture was confined in a closed platinum tube and slowly cooled from 1150°C at a rate of 0.1°C min−1 to 700°C before being finally quenched in air. The structure has tetragonal symmetry and belongs to space group P4122 (No. 91), with a = 7.3659 (2), c = 32.2318 (18) Å, V = 1748.78 (12) Å3, and Z = 4. The silicate anion consists of highly puckered, unbranched six-membered oligomers with the composition [Si6O19] and point group symmetry 2 (C2). Although several thousands of natural and synthetic oxosilicates have been structurally characterized, this compound is the first representative of a catena-hexasilicate anion, to the best of our knowledge. Structural investigations were completed using Raman spectroscopy. The spectroscopic data was interpreted and the bands were assigned to certain vibrational species with the support of density functional theory at the HSEsol level of theory. To determine the stability properties of the novel oligosilicate compared to those of the chemically and structurally similar cyclo­silicate combeite, we calculated the electronegativity of the respective structures using the electronegativity equalization method. The results showed that the molecular electronegativity of the cyclo­silicate was significantly higher than that of the oligostructure due to the different connectivities of the oxygen atoms within the molecular units.