Journal of Applied Crystallography最新文献

The development of a Pb-shielded fixture for the execution of a small-angle neutron scattering (SANS)-based workflow for interrogation of highly irradiated nuclear materials has been explored. The Pb shielding was specially designed to reduce the detected radioactivity from the specimen during SANS experiments, and the overall configuration is termed shielded magnetic SANS (SM-SANS). Two FeCrAl-based alloys, C35M and 125YF, were examined with the SM-SANS technique using a free-form size distribution locally monodisperse model in both the as-received and irradiated states. Quantitative values derived from the free-form size distribution were compared with atom probe tomography experiments. Microstructural and compositional parameters determined using the two characterization techniques were complements of each other. The results demonstrate that the SM-SANS technique is an effective means of characterizing nanoscale clustering in irradiated material systems and provides new avenues for investigating radioactive material microstructures.

Over the past ten years, there has been a surge in the demand for in crystallo optical spectroscopy (icOS), since optical spectroscopy is one of the few biophysical characterization methods applicable to both protein solutions and crystals. Historically, icOS has been used to compare the state of proteins in crystals and in solution, and to assess their functionality by determining the redox state of metal ions, cofactors or chromophores. The recent rejuvenation of time-resolved crystallography experiments has sparked a renewed interest in optical spectroscopy as a bridge between kinetic studies in solution and in the crystalline state. The method of icOS can be defined as the ensemble of spectroscopic techniques in the UV-visible-infrared range that can be applied to crystals. It has also been instrumental in understanding specific X-ray radiation damage to redox-sensitive parts of proteins. Spectra recorded from crystals are affected by crystal orientation, shape or position due to various optical phenomena. Fortunately, these can be modelled and their effect can be corrected. The icOS laboratory at the European Synchrotron Radiation Facility (ESRF) specializes in recording UV-Vis absorption, fluorescence emission and Raman spectra from protein crystals. Here, we present a suite of utilities that streamline the analysis and correction of UV-Vis absorption icOS data, encased in a graphical interface. This was originally developed for the icOS laboratory at ESRF but is available as a standalone package, with the aim of making icOS more accessible.

Contrast variation small-angle neutron scattering (CV-SANS) is a powerful tool for evaluating the structure of multi-component systems. In CV-SANS, the scattering intensities I(Q) measured with different scattering contrasts are de-com-posed into partial scattering functions S(Q) of the self- and cross-correlations between components. Since the measurement has a measurement error, S(Q) must be estimated statistically from I(Q). If no prior knowledge about S(Q) is available, the least-squares method is best, and this is the most popular estimation method. However, if prior knowledge is available, the estimation can be improved using Bayesian inference in a statistically authorized way. In this paper, we propose a novel method to improve the estimation of S(Q), based on Gaussian process regression using prior knowledge about the smoothness and flatness of S(Q). We demonstrate the method using synthetic core-shell and experimental polyrotaxane SANS data.

We present a theoretical framework for understanding diffuse multiple scattering (DMS) in single crystals, focusing on diffuse scattering Bragg channels. These channels, when probed with high-flux low-divergence monochromatic synchrotron X-rays, provide well defined visualizations of Bragg cones. Our main contribution lies in modelling the intensity distribution along these lines by considering diffuse scattering (DS) around individual reciprocal-lattice nodes. The model incorporates contributions from both general DS and mosaicity, elucidating their connection to second-order scattering events. This comprehensive approach advances our understanding of DMS phenomena, enabling their use as probes for complex material behaviour, particularly under extreme conditions.

We have carried out theoretical analysis, Monte Carlo simulations and machine-learning analysis to quantify microscopic rearrangements of dilute dispersions of spherical colloidal particles from coherent scattering intensity. Both monodisperse and polydisperse dispersions of colloids were created and underwent a rearrangement consisting of an affine simple shear and non-affine rearrangement using the Monte Carlo method. We calculated the coherent scattering intensity of the dispersions and the correlation function of intensity before and after the rearrangement and generated a large data set of angular correlation functions for varying system parameters, including number density, polydispersity, shear strain and non-affine rearrangement. Singular value decomposition of the data set shows the feasibility of machine-learning inversion from the correlation function for the polydispersity, shear strain and non-affine rearrangement using only three parameters. A Gaussian process regressor is then trained on the data set and can retrieve the affine shear strain, non-affine rearrangement and polydispersity with relative errors of 3%, 1% and 6%, respectively. Altogether, our model provides a framework for quantitative studies of both steady and non-steady microscopic dynamics of colloidal dispersions using coherent scattering methods.

Approximately 50% of entries in the Inorganic Crystal Structure Database (ICSD; https://www.fiz-karlsruhe.de/en) exhibit some form of structural disorder. This work aims to provide a thorough analysis of structurally disordered materials within the ICSD, using data extracted from crystallographic information files. To achieve this, we derive a classification of structurally disordered crystalline materials described by their spatially averaged structures and introduce a range of quantitative measures of structural disorder. The overarching aim of this classification and analysis is to facilitate high-throughput and machine learning studies of disordered materials. To demonstrate the application of our approach, we perform statistical analysis of the disordered compounds reported in the ICSD to identify general trends in the distribution of disorder across different chemical elements, structures and classes of materials.

Protein crystallization is key to determining the structure of proteins at atomic resolution. It can occur naturally, including in pathological pathways, for instance with aquaporin and γ-crystallin proteins. A fundamental understanding of the underlying crystallization process is both technologically and biologically relevant. A multitechnique approach is employed here to investigate protein crystallization in situ, allowing us to assess the evolution of the liquid suspension and crystallite structure as well as protein diffusion during the crystallization process. The wide range of methods probe the sample on ångström to millimetre length scales, accessing nanosecond to millisecond dynamics information while acquiring data with minute-timescale kinetic resolution during crystallization. This process takes several hours from an initial state of monomers or small clusters until the presence of large crystallites. Employing neutron spectroscopy allows us to distinguish different crystallization pathways and to reveal the presence of coexisting clusters during the entire crystallization process. We demonstrate the multitechnique approach on human serum albumin (HSA) proteins crystallized from aqueous solution in the presence of LaCl₃. For this system, the crystallization kinetics can be consistently described by a sigmoid function across all methods, and the kinetics can be controlled by the salt concentration. Moreover, we compare the HSA-LaCl₃ model system with the crystallization behavior of β-lactoglobulin-CdCl₂, which includes a metastable intermediate state.

Poly(ethyl-ene glycol)-grafted (PEGylated) liposomes receive increasingly more attention due to their practical applications in delivering vaccines, nutrients and drug molecules such as doxorubicin (DOX). PEGylated liposomes have been well documented for their capability in carrying DOX as rod-like crystallites enclosed inside the unilamellar vesicles. This study addresses the previously unresolved question of whether DOX intercalates into liposome bilayers by employing simultaneous small- and wide-angle X-ray scattering (SWAXS), complemented by an integrated asymmetric flow field-flow fractionation system coupled with multi-angle light scattering, dynamic light scattering and refractive index detection. The DOX-loaded PEGylated liposomes used are composed of phosphatidylcholine (N:0 PC) lipids, with different lipid chain lengths N = 18, 20 and 22, and a fixed molar ratio of lipid:cholesterol:DSPE-PEG2000 of 45:50:5. SWAXS analysis reveals that rod-like DOX nanocrystallites-approximately 70-95 nm in length and 14 nm in diameter-are encapsulated within the PEGylated liposomes across all three lipid types, with each exhibiting distinct membrane structural responses to DOX incorporation. Notably, 22:0 PC liposomes demonstrate significant DOX-induced disruption of lipid chain packing, accompanied by enhanced alignment of phosphate headgroups in the outer leaflet. Consistently, cryo-EM imaging reveals pronounced faceted membrane morphologies in DOX-loaded 22:0 PC liposomes. This faceting phenomenon is attributed to the accumulation of DOX within the excess hydro-phobic core regions created by the extended aliphatic chains beyond the cholesterol saturation limit. These DOX-enriched domains locally stiffen the membrane, promoting the formation of rigid, faceted structures.

AI programs such as AlphaFold (AF) are having a major impact on structural biology. However, predicted unstructured regions, the arrangement of linker-connected domains and their conformational changes in response to environmental variables present challenges that are not easily dealt with on purely computational grounds. An approach that uses predicted (or solved) protein modules/domains linked by potentially unstructured regions and that generates ensembles of models optimized against small-angle X-ray scattering (SAXS) data has been recently described [Brookes et al. (2023). J. Appl. Cryst. 56, 910-926]. Its implementation on a public-domain website, SAXS-A-FOLD (https://saxsafold.genapp.rocks), is presented here. User-supplied SAXS experimental intensity I(q) versus scattering vector magnitude q and the derived pair-wise distance distribution function P(r) versus r are first uploaded. An AF or user-supplied structure (currently only single chains without prosthetic groups) is then uploaded and displayed, and its SAXS I(q) and P(r) profiles are computed and compared with the experimental data. If uploaded from AF, the structure is color-coded by the associated confidence level: on this basis, the website automatically proposes potential flexible regions that can be user modified. For user-supplied structures, these regions have to be directly entered. A starting pool of typically 10-50 × 10³ conformations is generated using a Monte Carlo method that samples backbone dihedral angles along the chosen segments of potential flexibility in the protein structures. The initial pool is reduced to obtain a tractable set of models, for which P(r) and I(q) are computed with fast established methods. A global fit is performed using non-negatively constrained least-squares (NNLS) versus original data. The P(r) and I(q) NNLS results are then displayed, showing both the reconstructed curves and the contributing model curves, with their percentage contributions. A WAXSiS (https://waxsis.uni-saarland.de) implementation is utilized to calculate an I(q) for each selected model. These sets can be enhanced by adding a user-defined number of models generated before and after each selected model in the original Monte Carlo pool, ensuring the inclusion of nearby models that might better fit the data. Finally, NNLS is used on the WAXSiS-generated I(q) set versus the original I(q) data, with the results displaying the contributing models and their I(q). Aside from being representative of contributing conformations, the models selected by SAXS-A-FOLD could constitute a set of starting structures for more advanced MD simulations.

We present the elaboration and first generally applicable linearization routines of the parameter space concept (PSC) for determining one-dimensionally projected structures of m independent scatterers. This crystal determination approach does not rely on Fourier inversion but rather considers all structure parameter combinations consistent with available diffraction data in a parameter space of dimension m. The method utilizes m structure-factor amplitudes or intensities represented by piecewise analytic hyper-surfaces to define the acceptable parameter regions. The coordinates of the point scatterers are obtained through the intersection of multiple isosurfaces. This approach allows for the detection of all possible solutions for the given structure-factor amplitudes in a single derivation. Taking the resonant contrast into account, the spatial resolution achieved by the presented method may exceed that of traditional Fourier inversion, and the algorithms can be significantly optimized by exploiting the symmetry properties of the isosurfaces. The applied one-dimensional projection demonstrates the efficiency of the PSC linearization approach based on fewer reflections than Fourier sums. Monte Carlo simulations, using the projections of various random two- and three-atom structure examples, are presented to illustrate the universal applicability of the proposed method. Furthermore, ongoing efforts aim to enhance the efficiency of data handling and to overcome current constraints, promising further advancements in the capabilities and accuracy of the PSC framework.