Institute of Crystallography

Understanding antimicrobial peptide (AMP) structural determinants is crucial for clinical development. While most designed AMPs are short and helical, many natural ones have unstructured or cyclic C-terminal tails with poorly defined functions. We studied mesco-2 from the flatworm Mesocestoides corti, with an N-terminal α-helix kinked around a palindromic GRGIGRG motif and an unstructured C-terminal tail containing a disulfide-forming CLGRC motif, along with its disulfide-reduced analogue mesco-2 A. Similar CXXXC motifs are common in flatworm AMPs and typically occur in unstructured regions, as indicated by the sequence analysis. Molecular modelling revealed that the C-terminal disulfide loop modulates mesco-2 flexibility and oligomerization. Both peptides displayed strong antibacterial activity and low cytotoxicity. Differences appeared in their effect on bacterial growth kinetics at sub-bactericidal concentrations. Flow cytometry and fluorescence imaging confirmed membrane-related mechanisms, but for mesco-2 A the membrane-disruptive effect was slower. Atomic force microscopy confirmed their distinct membrane interaction modes, and circular dichroism in anionic liposomes revealed secondary-structure differences. Microscale thermophoresis confirmed distinct liposome binding, with mesco-2 A likely binding as monomers and mesco-2 forming assemblies, as also suggested by the modelling results. Overall, our findings show that the C-terminal cyclic tail is a tunable element for peptide engineering, enabling control over the speed, extent, and cooperativity of antimicrobial activity.

Magnetic Nanoparticles In their Research Article (10.1002/adfm.202522732), Teresa Pellegrino and co-workers report the gram-scale synthesis of unique anisotropic iron oxide cubic nanoparticles featuring cubic morphology and multicore architecture. They exhibit low coercivity, high saturation magnetization, record magnetic heating losses, and strong MPI signals, enabling advanced theranostic oncology applications.

Abstract The presence of hydroxyapatite (HAp) in the Cuticle of laying hen eggshells was investigated through an extensive and detailed study combining scanning electron microscopy (SEM) coupled with energy-dispersive spectroscopy (EDS), as well as micro-Raman, micro-FTIR, X-ray diffraction (XRD), and thermogravimetric analysis (TG). Additionally, Raman and FTIR spectra of Cuticle HAp were compared with those obtained from the internal surface of chicken femur fragments and from a bovine HAp sample. Examination of the same region by SEM in SE (topographic) and BSE (subsurface compositional contrast) modes revealed unidirectional (nanofibrous) calcite growth within the Vertical Layer (VL) and Palisade Layer (PL), which together constitute nearly the entire eggshell thickness. Shell thickening in the VL and PL layers appears to proceed via an additive mechanism characterized by the successive deposition of nanodroplets containing, according to our hypothesis, the mineral phase, water, and organic components. This multiphasic system generates lamellae that progressively increase in thickness through the continuous incorporation of new nanodroplets onto the pre-existing surface. This additive nanodroplet-mediated growth contributes to understanding how micropores form in the PL and VL. Biomineralization via an additive mechanism is strongly supported by the presence of nano-hemispheres attached to growing lamellae in the VL and PL. Statistical analyses corroborate the relationship between the diameter of Cuticle nanospheres and that of nano-hemispheres in the Vertical Layer. Fractures observed in the VL indicate structural continuity between the Cuticle and the Vertical Layer, suggesting that additive growth involves a continuous supply of HAp — possibly across the entire uterine surface — which, through a yet undescribed mechanism, dissolves and/or transforms calcium phosphate nanospheres into calcium carbonate nanofibers.

K$_2$ReCl$_6$ belongs to the antifluorite family and exhibits a sequence of structural transitions above the onset of magnetic order at $T_N$ = 12 K. Because of its 5d3 electronic configuration in an octahedral coordination, the ground state is a pure spin state without orbital degeneracy within the LS coupling scheme, but it can become Jahn-Teller active in the strong spin-orbit coupling limit described by the $jj$ coupling [S. Streltsov and D. I. Khomskii, Phys. Rev. X 10, 031043 (2020)]. While the structural transitions in K$_2$ReCl$_6$ are understood in terms of octahedral rotation and tilting, the possible impact of a Jahn-Teller distortion remains an open issue. We report on comprehensive crystalstructure studies by means of powder neutron and single-crystal x-ray diffraction on K$_2$ReCl$_6$ and on K$_2$SnCl$_6$. The latter material is used as a reference, because it exhibits the same sequence of structural transitions as K$_2$ReCl$_6$, but possesses a filled 4d shell ruling out a Jahn-Teller distortion. While the ReCl$_6$ octahedron in K$_2$ReCl$_6$ presents sizable distortions at intermediate temperatures, there is no such distortion persisting to low temperatures excluding a sizable Jahn-Teller effect. Studies on polycrystalline samples of Rb$_2$ReCl$_6$ and Cs$_2$ReCl$_6$, in which the structural transitions are suppressed due to the larger alkaline ionic radius, also do not find any indications for a Jahn-Teller distortion.

Achieving optimal target activity while maintaining synthetic accessibility and drug-likeness represents a major challenge in computational drug discovery. Existing de novo generative models often yield chemically invalid or synthetically intractable structures and struggle to optimize multiple objectives simultaneously. Here, we introduce ALCHIMIA, an interpretable hybrid framework combining reinforcement learning (RL) and a genetic algorithm (GA), built based on a vocabulary of 33 medicinal chemistry-inspired molecular transformations. The RL component trains a policy network to prioritize transformation sequences that improve synthetic accessibility (SA) and quantitative estimate of drug-likeness (QED) scores, embedding these constraints directly into molecular generation. The GA component applies the learned policy as a mutational operator within population-based optimization guided by molecular docking, enabling exploration of diverse chemical lineages while converging toward high-affinity ligands. ALCHIMIA was applied to two different pharmacologically relevant targets, human Cannabinoid Receptor 2 (CB2R) and human sigma non-opioid intracellular receptor 1 (S1R). We considered three different scenarios: i) unconstrained hit-identification; ii) scaffold-constrained lead-optimization and iii) design of dual modulators. Remarkably, the framework generated chemically valid molecules with better QED and SA scores compared to random baselines and state-of-the-art de novo design models. By codifying typical medicinal-chemistry actions as learnable transformations and coupling multi-objective optimization with GA-based diversity maintenance, ALCHIMIA provides a practical, interpretable, and scalable framework for molecular de novo design. The developed RL algorithm is freely available as GitHub repository (https://github.com/alberdom88/ALCHIMIA).

The solid–liquid hydrodynamics in a novel tower crystallizer consisting of seven vertically stacked spherical mixed-suspension, mixed-product removal crystallizers is reported. Mixing in spherical geometries remains underexplored. Therefore, computational fluid dynamics (CFD) simulations (Eulerian–Eulerian multiphase approach with Reynolds-Averaged Navier–Stokes closure at steady state) were performed to systematically assess in silico the impact of stirring rate, impeller type, blade geometry and number, dual-impeller configurations, and baffles on the mixing performance. Results demonstrate the best suspension mixing for representative pharmaceutical crystals (100 μm, 20% solids loading) using an axial-flow pitched blade impeller (four blades, 30° blade angle). Adding baffles further improves mixing, achieving homogeneity (relative standard deviation, σ < 0.2). While mixing is often overlooked in lab-scale crystallizers, possibly due to their small volumes (≤100 mL), this study highlights the critical role of mixing as a fundamental parameter for achieving suspension uniformity, which significantly alters the crystallization performance. The developed and generalizable framework provides mechanistic insights into the solid–liquid mixing, offering a data-driven foundation toward the rational design and scale-up of crystallizers. Ultimately, integrating these CFD findings with population balance modeling in future work will contribute to model-predictive crystallizers, bridging the gap from the lab to industrial scale.

The manipulation of light through its interactions with artificially structured media is a cornerstone of photonics. The rescaling of this concept to the X-ray realm-which will enable us to control X-ray light with the same precision routinely available in the visible/IR range-has so far been hindered by the inherent difficulty of realizing photonic structures with the sub-nanometric resolution dictated by X-ray wavelengths. A promising approach to this challenge is based on the so-called Berry-phase effect, the large beam translations undergone by X-ray photons propagating in a deformed crystal, due to the simultaneous presence of Berry curvatures in real and reciprocal space. In this work, the controlled crystal distortions required to rein in this effect are obtained by pairing the lattice expansion observed upon H irradiation of GaAsN with a spatially selective hydrogenation technique. The macroscopic beam translations measured here are striking manifestations of the Berry curvatures associated with the sub-nanometric lattice distortions induced by H incorporation. Through the comparison with a dedicated theoretical model, the individual translation branches observed in X-ray transmission can be traced back to specific deformation features present within the samples, establishing a predictive framework for the control of X-ray propagation in the fabricated structures.

Structural predictions have reached unprecedented accuracy. They leverage sequence‐specific data to capture all potential interactions a sequence has evolved to fulfill. AlphaFold derives information from three sources: learned parameters capturing intrinsic amino acid secondary structure and environment propensity; models of related proteins providing structural templates; and aligned sequences encoding profiles and concerted evolutionary changes of residues involved in contacts. However, function demands dynamic changes; hence not all possible interactions can coexist simultaneously. Comprehensive information entails contradictions, which resolved in favor of the better‐informed structure will silence less stable states and associations. Here, we introduce a method using all three channels to include prior knowledge: site‐specific variants, predefined alignments and templates. Selecting information relevant to a particular state delimits the functional context of a prediction. Our program VAIRO allows us to rescue asymmetric and weaker interactions to complete the view of molecular assemblies in the architecture of a bacterial surface layer, and reveals otherwise inaccessible dynamic states in a pneumococcal multimeric membrane protein complex. VAIRO is distributed via the python package index (PyPI) (https://pypi.org/project/vairo) and the code is also available on Github (https://github.com/arcimboldo-team/vairo).

Abstract Separating ammonia from the reactor effluent remains a major energy bottleneck in industrial synthesis processes, where cryogenic condensation is typically employed. This method is energy-intensive, and alternative approaches such as pressure swing adsorption have not been widely adopted due to challenges associated with ammonia’s high affinity for adsorbents and the difficulty of complete regeneration under practical conditions. Here, we introduce pure- silica zeolites for this application. We determine first the high-pressure high-temperature adsorption data using a specially designed adsorption equipment. Our findings indicate that pure-silica zeolites exhibit high ammonia working adsorption capacity compared to previously reported commercial adsorbents. Molecular simulations provide information on adsorbate distribution and explain observed trends in adsorbed amounts and enthalpies. A 4-column 4- step pressure and temperature swing adsorption is designed and modeled at process relevant conditions, achieving ammonia purity above 99 % and recovery over 98 %, demonstrating the potential of replacing the energy intensive cryogenic separation.