Volume 42(2) - Summer 2026

To fully understand the functional structures of proteins, it is crucial to investigate their states under physiological conditions, which are often difficult to capture using conventional X-ray crystallography or cryo-electron microscopy (cryo-EM). This article introduces two examples (SOD1 and RNase H) in which electrospray ionization mass spectrometry (ESI-MS) was utilized to analyze the reversible formation and structural states of protein-metal ion-substrate complexes in aqueous solutions. Although challenges remain regarding instrumental constraints and the preservation of weakly bound noncovalent complexes in the gas phase, ESI-MS has emerged as a powerful tool capable of visualizing in-solution structural states and binding modes at the molecular level, potentially playing a vital complementary role in the future of structural biology.

In this article, we present a method for improving the image quality of X-ray CT reconstruction images using deep learning. The method utilizes low- and high-noise X-ray CT reconstruction images acquired with different scanning times as training data to create a model to remove noise patterns from the reconstructions. This noise-reduction method is demonstrated using a battery and a plastic object as examples. We also present analysis results of these images and show that deep learning also improves analysis accuracy.

X-ray fluorescence (XRF) spectrometry enables the rapid, nondestructive analysis of a wide range of different sample types. However, as it is a relative analytical method, quantitative analysis requires preparing standard samples, establishing calibration curves, and optimizing correction conditions, which results in longer analysis times and higher costs. This report describes the features and effectiveness of “GEO-TRACE-PAK,” a pre-calibration analytical package specifically designed for analyzing trace elements, including rare earth elements, in rocks. To validate this package, which includes optimized measurement conditions and correction settings based on approximately 110 standard samples, multiple geological reference materials were analyzed. The results demonstrated good agreement with reported reference and literature values for the constituent elements. GEO-TRACE-PAK is expected to be an effective and practical method for geological sample analysis, enabling the immediate analysis of test samples without the need for additional standard samples.

Crystal structure prediction (CSP) is increasingly relevant to pharmaceutical solid-form development because it provides a computational view of the crystal-energy landscape and helps assess whether experimentally observed polymorphs are likely to represent the most stable or otherwise accessible crystal forms. When used together with powder X-ray diffraction (PXRD), integrated CSP workflows also offers a practical route to structure solution from powder data in cases where suitable single crystals are not available. In this article, a CSP-assisted PXRD workflow is outlined using cimetidine as a demonstration sample. The X-ray powder data were measured on a Rigaku SmartLab system, with the CSP candidates and landscape determined from the molecular structure of cimetidine by XtalPi. Targeted CSP candidate structures were selected from the CSP landscape using two scoring parameters: comparison of simulated powder patterns with the measured powder pattern, and the similarity of the indexed unit cell from the measured data with the CSP unit cell. The candidate structures were then refined in SmartLab Studio II by whole-pattern profile fitting with the unit-cell lattice parameters and molecular Z-matrix parameters as variables. The resulting fit showed strong agreement with the measured data and the refined structure overlaid well with the published single-crystal structure. The same workflow can be extended to automated high-throughput PXRD screening by comparing measured patterns against a database of CSP-derived CIF files in real time.

The next-generation XSight Micron LC detector enhances the imaging performance of the high-resolution 3D X-ray microscope nano3DX by improving system resolution by more than 40%. Since its introduction in 2012, the nano3DX has been used as an X-ray microscope capable of high-resolution, high-contrast 3D imaging with resolution down to 0.6 μm. With the new detector, resolution, signal-to-noise ratio, and field of view have all been improved, enabling more detailed visualization of fine internal structures. The next-generation XSight Micron LC can be installed in newly delivered nano3DX systems and provided as an upgrade for existing instruments, enabling current users to enhance system performance without replacing the entire microscope.