Acta Crystallographica Section D Structural Biology

Calculation of density maps from atomic models is essential for structural studies using crystallography and electron cryo-microscopy (cryoEM). These maps serve various purposes, including atomic model building, refinement, visualization, and validation. However, accurately comparing model-calculated maps to experimental data poses challenges, particularly because the resolution of cryoEM experimental maps varies across the map. Traditional crystallography methods generate finite-resolution maps with uniform resolution throughout the unit cell volume, while most modern software in cryoEM employ Gaussian-like functions to generate these maps, which does not adequately account for atomic model parameters and resolution. Recent work by Urzhumtsev & Lunin (2022, IUCr Journal, 9, 728-734) introduces a novel method for computing atomic model maps that incorporate local resolution and can be expressed as analytically differentiable functions of all atomic parameters. This approach enhances the accuracy of matching atomic models to experimental maps. In this paper, we detail the implementation of this method in CCTBX and Phenix. SynopsisNew tools implemented in CCTBX and Phenix allow the calculation of variable-resolution maps through a sum of atomic images expressed as analytic functions of all atomic parameters, along with their associated local resolution.

Monte Carlo neutron ray-tracing simulations of time-of-flight (TOF)-Laue neutron macromolecular crystal diffraction (n-MX) using the McStas software package were done for the upcoming NMX Macromolecular Diffractometer at the European Spallation Source. Splitting neutron rays that arrive at the crystal lead to dramatic improvements in event formation with minimal computational overhead. The simulated event probability data was sampled using a new single-pass weighted reservoir sampling method, and processed like real n-MX data using DIALS. The effects of air and beamstop scatter on simulated data was investigated. SynopsisMonte Carlo simulations of neutron protein diffraction experiments provide useful data that models instrumental components that interact with neutrons, as well as the crystal diffraction itself. These data can be applied to instrument development, such as the commissioning of the NMX Macromolecular Diffractometer at ESS.

Capturing meta-stable conformations of enzymes and ligand complexes demands structural snapshots beyond static crystal structures. While time-resolved serial crystallography at room temperature, offers a time-resolution down to the femto-second domain it requires large amounts of micro crystals, specialized beamlines and considerable experience. Moreover, as the majority of enzymes displays turnover-times in the millisecond domain or slower, simpler methods can provide meaningful structural insight into enzyme catalysis. Vitrification of protein crystals can trap reaction intermediates by rapid cooling to {inverted exclamation} 100 K, and has traditionally been used to gain insight into long lived reaction intermediates such as product complexes. However, manual vitrification procedures are limited to long delay times of at least several seconds and heavily suffer from operator variability. A solution to this problem is provided by automatic crystal plunging devices, such as the Spitrobot, that plunge loop-mounted protein crystals into liquid nitrogen within millisecond time-scales. Versatile means of reaction initiation can be achieved either by micro dispensing a ligand droplet, or via optical excitation of light-sensitive proteins, or via the photoactivation of caged compounds. In addition to the conceptual simplicity, another benefit of cryo-trapping is that data can be collected at conventional synchrotron beamlines, exploiting their robust high-throughput capabilities. Thus, compared to room-temperature time-resolved crystallography, users not only benefit from uncoupling sample-preparation and data-collection, but also from a reduction in the required technical expertise and ready access to radiation sources. However, as cryo-trapping crystallography explores dynamic structural changes that become only visible by the comparison of several samples, experiments have to be carefully planned to carry out the necessary controls and to avoid mis- or over-interpretation of the results. Here we describe a detailed protocol for cryo-trapping time-resolved crystallography using automated crystal-plungers that enables researchers to map enzymatic reaction coordinate pathways within the millisecond domain.

Affinity chromatography is a powerful and therefore popular method for the purification of proteins for structural studies. The success of the technique relies on the specificity of the interaction between the target protein and the affinity resin. Here, we present the identification of two protein contaminants isolated from HEK293 cell lysate following affinity purification of twin Strep-tagged or FLAG-tagged proteins. The contaminants were identified as human propionyl-coenzyme A carboxylase (hPCC) and protein arginine methyltransferase 5 in complex with methylosome protein 50 (PRMT5:MEP50) via a combination of cryo-EM data processing and proteomic analyses. This report serves to illustrate how these contaminants may appear in cryo-EM datasets and to highlight the paramount importance of affinity chromatography resin specificity for efficient protein purification.

Ligand binding has been shown to induce significant alterations in the conformational landscape of proteins. Traditional crystallography approaches have provided valuable input about the end states in ligand-binding reactions. However, dynamical relationships between ligand binding and backbone rearrangement often remain obscured by crystallographic structures. In the present study, we use time-resolved serial synchrotron crystallography (TR-SSX) to directly visualize indole binding in the cavity of T4 lysozyme L99A in microcrystals under controlled environmental conditions. By integrating fixed target crystallography with LAMA-based ligand delivery, we have been able to track the progression of ligand binding and backbone rearrangement. By utilizing an occupancy refinement protocol, we have been able to quantify structural populations. Our studies reveal that ligand binding for this protein cavity follows a diffusion-limited process that progressively rearranges the F -helix of the protein towards a dominant conformational state. These findings establish an observable link between ligand diffusion, occupancy evolution and conformational adaptation within a crystalline environment. More broadly, our work shows how TR-SSX can quantify ligand and conformational populations during binding, providing a framework to interpret structural adaptation in real time.

Plant-specific membrane receptor kinases with structurally diverse extracellular domains regulate key processes in plant growth, development, immunity and symbiosis. Structural studies of these glycoproteins are often hampered by the limited quantities in which they can be obtained. Here, we describe the LRR crystallization screen, which has enabled the successful crystallization and structure determination of multiple receptor kinase ectodomains, including ligand-and co-receptor-bound complexes. As an example, we report the 1.5 [A] resolution crystal structure of the leucine-rich repeat (LRR) domain of STRUBBELIG-RECEPTOR FAMILY 6 (SRF6) from Arabidopsis thaliana. The SRF6 ectodomain contains seven LRRs and a disulfide-bond-stabilised N-terminal capping domain but lacks the canonical C-terminal cap and the N-glycosylation pattern typically observed in other family members. Previously reported protein-protein interactions between the SRF6 and SRF7 ectodomains and the receptor kinases BRI1, BRL1, BRL3, SERK3 and BIR1-3 could not be confirmed by quantitative isothermal titration calorimetry and grating-coupled interferometry assays, suggesting that these structurally conserved LRR receptor kinases may have signalling functions outside the brassinosteroid pathway. SynopsisA crystallisation screen that has enabled the structural analysis of various extracellular domains of plant membrane receptor kinases is described together.

AbstractThe development of machine learning models for protein-ligand interactions is fundamentally constrained by the quality and diversity of available structural data. Existing databases of protein-ligand complexes present researchers with an unsatisfying trade-off: carefully curated collections such as PDBBind and HiQBind offer high structural reliability but cover only a narrow slice of the Protein Data Bank (PDB), while large-scale resources like PLInder provide broad coverage at the expense of rigorous quality control. Here, we introduce CROWN (Curated Repository Of Well-resolved Non-covalent interactions), a machine learning-ready dataset that reconciles this tension by applying a comprehensive, fully automated preprocessing pipeline to the PLInder database. Starting from 649,915 protein-ligand interaction systems, CROWN applies a series of interleaved quality filters and processing stages addressing crystallographic resolution, ligand identity, pocket completeness, structural repair, interaction quality, and protonation at physiological pH. A distinguishing feature of the pipeline is a final constrained energy minimisation step using custom flat-bottomed restraints, which balances crystallographic evidence with relaxation of intramolecular strain. This step -- absent from existing protein-ligand datasets -- produces structurally uniform complexes by reconciling the heterogeneous refinement practices of different crystallographers and structure determination protocols, without distorting the experimentally observed binding geometry. The resulting dataset of 153,005 complexes represents a roughly four-fold increase in protein and species diversity over PDBBind and HiQBind, while maintaining rigorous structural standards. Importantly, CROWN adopts a geometry-centric design philosophy that treats the 3D arrangement of atoms at the binding interface as a self-consistent source of information, rather than relying on externally measured binding affinities that cover only a fraction of known structures and introduce well-documented biases. We anticipate that CROWN will serve as a broadly useful resource for training generative models of protein-ligand binding poses, developing scoring functions, and benchmarking interaction prediction methods.

Glutamate racemase (MurI) catalyzes the stereochemical interconversion of L-glutamate to D-glutamate, a key element of bacterial peptidoglycan biosynthesis. In this study, we present the crystal structure of Helicobacter pylori glutamate racemase at 1.43 [A] and in monoclinic symmetry, as previously reported models, but different unit-cell parameters. The present model contains a single dimer and retains the previously described head-to-head dimer arrangement. The differences between the models arise from variations in unit-cell parameters, which lead to altered crystal packing interactions rather than changes in the quaternary assembly. The monomeric fold and active-site architecture remain conserved and are consistent with the catalytic features described for bacterial glutamate racemases. This structure provides an updated, high-resolution structural model for H. pylori glutamate racemase and highlights the variability in crystal packing within the same space group.

We present mdBIRCH, an online clustering method that adapts the BIRCH CF-tree to molecular dynamics (MD) data by using a merge test calibrated directly to RMSD. Each arriving frame is routed to the nearest centroid and added only if the post-merge radius computed from the cluster feature remains within a user-supplied threshold. This keeps the average deviation to each cluster centroid bounded as the cluster grows and preserves a simple interpretation of resolution in physical units. We evaluate mdBIRCH on a {beta}-heptapeptide and the HP35 system. We propose two protocols to make the threshold selection easier: (a) RMSD-anchored runs that use controlled structural edits to define interpretable operating points and (b) blind sweep that tracks how cluster count, occupancy, and coverage change with the threshold. In both systems, increasing the threshold reduces the number of clusters, concentrates coverage in high-occupancy states, and broadens within-cluster RMSD distributions. Furthermore, because decisions rely only on cluster summaries, mdBIRCH completely avoids the need for pairwise distance matrices, scales near-linearly with the number of frames on standard hardware, and naturally supports incremental operation. The method offers a practical combination of speed and interpretability for large-scale trajectory analysis.

Although machine learning has transformed protein structure prediction of folded protein ground states with remarkable accuracy, intrinsically disordered proteins and regions (IDPs/IDRs) are defined by diverse and dynamical structural ensembles that are predicted with low confidence by algorithms such as AlphaFold and RoseTTAFold. We present a new machine learning method, IDPForge (Intrinsically Disordered Protein, FOlded and disordered Region GEnerator), that exploits a transformer protein language diffusion model to create all-atom IDP ensembles and IDR disordered ensembles that maintains the folded domains. IDPForge does not require sequence-specific training, back transformations from coarse-grained representations, nor ensemble reweighting, as in general the created IDP/IDR conformational ensembles show good agreement with solution experimental data, and options for biasing with experimental restraints are provided if desired. We envision that IDPForge with these diverse capabilities will facilitate integrative and structural studies for proteins that contain intrinsic disorder, and is available as an open source resource for general use.

The importance of human aldehyde oxidase (hAOX1) has increased over the last decades due to its involvement in drug metabolism. Inhibition studies concerning hAOX1 are extensive and a common reducing agent, dithiothreitol (DTT), was recently found to inactivate the enzyme. However, in previous crystallographic studies of hAOX1, DTT was found to be essential for crystallization. To surpass this concern another reducing agent used in crystallization trials. Using tris(2-carboxyethyl)phosphine (TCEP), a sulphur-free reducing agent, it was possible to obtain well-ordered crystals from hAOX1 wild type and variant, hAOX1_6A, which diffracted beyond 2.3 [A]. Instead of the typical star-shaped crystals of hAOX1, at pH 4.7, plates are obtained in the orthorhombic space group (P22121) with two molecules in the asymmetric unit. Activity assays with the enzyme incubated with both reducing agents show that contrary to DTT, TCEP does not lead to irreversible inactivation of the enzyme. The replacement of DTT with TCEP in crystallization of hAOX1 provides a strategy to circumvent enzyme inactivation during crystallographic studies, allowing future applications of new assays, such as time-resolved crystallography.

Bacteria microcompartments (BMCs) are pseudo-organelles comprised of a self-assembling, semi-permeable protein shell, most commonly enclosing components of enzymatic pathways. -Carboxysomes (-CBs) are anabolic BMCs known for their role in sequestering Rubisco, the enzyme responsible for carbon fixation in plants, algae and bacteria, along with an upstream enzyme and an assembly protein. Rubisco has low selectivity for its substrate, CO2, and has a slow enzymatic turnover rate, resulting in an inefficient metabolic pathway. Within the -CB, Rubisco has been observed at a range of concentrations and with either a liquid-like assembly or a pseudo-lattice of polymerized fibrils. The biophysical origins of the fibril ultrastructure organization are unclear; however, it is only observed inside -CBs. Quantitative knowledge of the binding constants and energies for assembly and maintenance of these fibrils is critical for understanding this organization and Rubisco regulation, but quantitative methods for in situ analysis of Rubisco polymerization have been lacking. Here, we present an approach to convert tomography-derived -CB volumes and Rubisco particle positions into polymerization binding curves. We used this procedure to determine the Rubisco polymerization constants, including the nucleus size (n) and equilibrium polymerization constant (Kpol). The adopted modeling approach is consistent with in situ constraints, such as concentration-dependent binding interactions and confinement. This approach offers a powerful tool to evaluate both in vitro and potentially in vivo biomolecular interactions, both of Rubisco and of other proteins and polymers suitable for analysis by cryo-electron tomography. Significance StatementCryogenic electron tomography (cryoET) is a powerful method to resolve structures of proteins in their native environment at subnanometer-level resolution. Because tomography data retains spatial relationships of all particles, it intrinsically contains information about component (e.g., protein) binding interactions. Here, we use Rubisco polymerization in -carboxysomes as a model system to demonstrate that quantitative, biochemical binding analysis is possible with cryoET.

CO dehydrogenases (CODH) are metalloenzymes that reversibly oxidize CO to CO2, at a buried NiFe4S4 active site. The substrates, CO and CO2, need therefore to be transported through the protein matrix to reach the active site. The most likely pathway for intra-protein diffusion is the hydrophobic channel identified in the crystal structures. Here, we use site-directed mutagenesis to study the highly conserved isoleucine 563 of Thermococcus sp. AM4 CODH2. Mutations at this position change the biochemical properties (KM for CO, product inhibition constant, catalytic bias...), and increase the resistance of the enzyme to the inhibitor O2, showing that isoleucine 563 indeed lines the gas channel. The I563F mutation decreases the bimolecular rate constant of inhibition by O2 15-fold, and increases the IC50 20-fold, which is the strongest improvement in O2 resistance reported so far. We show that the size of the introduced amino acids is less important than their flexibility - along with the size of the cavity formed near the active site in the channel. We also conclude that O2 access to the active site cannot be slowed down without also affecting CO diffusion. This tradeoff will have to be considered in further attempts to use site-directed mutagenesis to make CODHs more O2 tolerant.

Macrolides are an antibiotic class widely used in both human and veterinary medicine, and function by interfering with protein synthesis. Regrettably, numerous strategies for evading the antibiotic properties of macrolides have been found in bacteria, including enzyme-mediated inactivation. These mechanisms are now widely disseminated among pathogenic, animal-associated and environmental bacteria making them a One Health issue. Macrolide esterases, which hydrolyze the macrolactones ester bond, confer one such resistance mechanism. Two types of macrolide esterases have thus far been identified, the well-studied erythromycin esterases and the recently discovered Est-type enzymes that belong to the /{beta}-hydrolase superfamily. We present detailed structure-function studies for four diverse Est type esterases: which only share 44-66% sequence identity (EstTSf, EstTSt, EstTBc, and EstXEc). In addition to resistance profiling and substrate specificity studies, we present structures for all four enzymes, including structures for EstTBc and EstXEc in complex with tylosin and tylvalosin macrolides, post hydrolysis. Complementing the data with mutational and kinetic studies allowed for a detailed analysis of the structural basis for macrolide-enzyme interactions. Combined the data suggest that promiscuous binding and imprecise positioning, mediated by a water-cage, dictate substrate specificity for Est-type macrolide resistance enzymes. These insights may prove beneficial for next-generation antibiotic development.

Trichomonas vaginalis causes trichomoniasis, the most common non-viral sexually transmitted disease in humans. T. vaginalis pyrophosphate-dependent phosphofructokinase (TvPPi-PFK) is a putative target for rational, structure-based drug discovery, given its absence in mammals and its importance for parasite survival. TvPPi-PFK is a cytosolic enzyme that catalyzes the phosphorylation of fructose-6-phosphate using pyrophosphate (PPi) as the phosphoryl donor. This reversible reaction, catalyzed by TvPPi-PFK, is the first committed step in glycolysis. Its reverse reaction is vital for gluconeogenesis in T. vaginalis. The purification, crystallization, structure determination, and preliminary structure-functional analyses of three crystal structures of TvPPi-PFK are presented. All three structures organize as tetramers with the conserved motifs essential for pyrophosphate binding and PPi-PFK catalytic activity. Comparative analysis with structural neighbors from other organisms demonstrated that despite sharing <29% sequence identity, TvPPi-PFKs protomer shares overall topology with both PPi- and ATP-dependent PFKs. Mass photometry confirmed that TvPPi-PFK formed tetramers under near-physiological conditions. Unexpectedly, TvPPi-PFK crystals dephosphorylate ATP to AMP during soaking. In all three structures, either ATP or AMP is bound at the enzymes dimer interface, typical of ATP-PFKs, but a novel finding for PPi-PFKs. Furthermore, a sugar phosphate binding site was observed in proximity to the ATP-binding site. Thus, the three reported TvPPi-PFK structures validate its established PPi-dependent activity while revealing previously unreported ATP and sugar phosphate binding. This study also lays a foundation for future research into putative ATP-dependent activity of TvPPi-PFK and for evaluating known phosphofructokinase inhibitors as potential therapeutics for trichomoniasis. These findings expand our understanding of PFK superfamily diversity and support the continued exploration of TvPPi-PFK as a drug target for trichomoniasis. SynopsisThe production, crystallization, and three crystal structures of a pyrophosphate-dependent phosphofructokinase from Trichomonas vaginalis (TvPPi-PFK) reveal ATP binding and structural similarity to both ATP-dependent and pyrophosphate-dependent phosphofructokinases. TvPPi-PFK dephosphorylates ATP and has a novel ATP-PFK-like ATP-binding cavity.

Protein function is fundamentally driven by structural dynamics, yet the majority of structural bioinformatics treats proteins as static rigid bodies. While Molecular Dynamics (MD) simulations attempt to capture these motions, they are computationally prohibitive for exploring large-scale conformational changes, such as domain movements or allostery, which occur on timescales often inaccessible to standard simulation. However, the Protein Data Bank (PDB) contains a latent wealth of dynamic information in the form of redundant entries proteins solved in multiple distinct conformational states. Detecting these "shape-shifting" pairs remains challenging because standard structural alignment algorithms (e.g., TM-align) rely on rigid-body superposition, which fails when substantial geometric rearrangement occurs. In this study, we introduce a high-throughput method to systematically mine the PDB for proteins that share identical topology but exhibit divergent tertiary conformations. By utilizing a coarse-grained Secondary Structure Element (SSE) representation, we decouple topological connectivity from geometric rigidity, allowing for the detection of conformational homologues that share low global structural similarity despite high predicted structural similarity. We applied this "conformational scanning" across the entire RCSB database, identifying a curated dataset of proteins undergoing significant structural rearrangements. This work bridges the gap between static structural data and dynamic function, providing a critical "ground truth" dataset for benchmarking data-driven protein design and checking the plausibility of generative structure models.

With the advance of hardware and software for molecular dynamics simulation it has become routine to obtain trajectories that are tens of microseconds in duration for all kinds of protein machinery. This shifts the burden of work onto analysis of the simulation data and opens opportunities for more rigorous and reproducible observations on mechanism. Toward this end we developed an investigator-blind analysis pipeline which operates on featurized simulation data, performs unsupervised clustering, and then identifies which input features are most discriminatory of cluster identity. Application of this pipeline to a large set of G-protein coupled receptor simulation data shows that it identifies several well-known microswitches. Inspection of these structural elements reveals changes in conformation that are known to accompany functional transitions of the receptor. In addition to these known structural elements the analysis also identifies two possibly new structural motifs: the kink in transmembrane helix 2, and a coupled "piston-like" motion of TM2 and TM3.

Understanding the structural dynamics of biomolecules is crucial for uncovering biological functions. As molecular dynamics (MD) simulation data becomes more available, deep generative models have been developed to synthesize realistic MD trajectories. However, existing methods produce fixed-length trajectories by jointly denoising high-dimensional spatiotemporal representations, which conflicts with MDs frame-by-frame integration process and fails to capture time-dependent conformational diversity. Inspired by MDs sequential nature, we introduce a new probabilistic autoregressive (ProAR) framework for trajectory generation. ProAR uses a dual-network system that models each frame as a multivariate Gaussian distribution and employs an anti-drifting sampling strategy to reduce cumulative errors. This approach captures conformational uncertainty and time-coupled structural changes while allowing flexible generation of trajectories of arbitrary length. Experiments on ATLAS, a large-scale protein MD dataset, demonstrate that for long trajectory generation, our model achieves a 7.5% reduction in reconstruction RMSE and an average 25.8% improvement in conformation change accuracy compared to previous state-of-the-art methods. For conformation sampling task, it performs comparably to specialized time-independent models, providing a flexible and dependable alternative to standard MD simulations.

Human RNase 2 (eosinophil-derived neurotoxin, EDN) is a major eosinophil granule protein of the vertebrate-specific RNase A superfamily and is involved in antiviral response and inflammation. Identifying ligand-binding pockets in EDN is thus relevant to structure-based drug design. In our laboratory we identified by protein crystallography a conserved site at the protein surface binding to carboxylic anion molecules (malonate, tartrate and citrate). Searching for potential biomolecules rich in anion groups and considering previous report of EDN binding to glycosaminoglycans, we explored the protein binding to saccharides. Next, EDN crystals were soaked with mono- and disaccharides, and the 3D structures of ten complexes were solved by X-ray crystallography at atomic resolution. We identified protein binding pockets to glucose, fucose, mannose, sucrose, galactose, trehalose, N-acetyl-D-glucosamine, N-acetylmuramic acid, and the sialic acid N-acetylneuraminic acid. A main site for glucose, fucose, and galactose was located adjacent to the spotted carboxylic anion site. Secondarily, N-acetylneuraminic acid, N-acetylmuramic acid, sucrose, galactose, and mannose shared another protein surface region. Overall, the saccharides clustered into seven defined sites, outlining a conserved recognition pattern, which was further analysed by molecular modelling. Interestingly, within the RNase A family, we find amphibian RNases that were initially isolated as carbohydrate binding proteins and named as leczymes, combining enzymatic and lectin properties. The present data is the first systematic structural characterization of a mammalian sugar-binding RNase within the family. The results highlight unique EDN residues that mediate its sugar specific interactions, of particular interest for a better understanding of the protein physiological role. HighlightsO_LIstructure of RNase 2 in complex with mono and disaccharides at atomic resolution C_LIO_LIidentification of RNase 2 unique sugar binding sites C_LIO_LIcharacterization of a mammalian RNase A family enzyme with lectin properties C_LI Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=110 SRC="FIGDIR/small/713198v1_ufig1.gif" ALT="Figure 1"> View larger version (46K): org.highwire.dtl.DTLVardef@1d805f7org.highwire.dtl.DTLVardef@16fcc49org.highwire.dtl.DTLVardef@ccfd92org.highwire.dtl.DTLVardef@1b8f1e_HPS_FORMAT_FIGEXP M_FIG C_FIG

Terminal-based workflows are central to large-scale structural biology, particularly in high-performance computing (HPC) environments and SSH sessions. Yet no existing tool enables real-time, interactive visualization of protein backbone structures directly within a text-only terminal. To address this gap, we present StrucTTY, a fully interactive, terminal-native protein structure viewer. StrucTTY is a single self-contained executable that loads mulitple PDB and mmCIF files, normalizes three-dimensional coordinates, and renders protein structures as ASCII graphics. Users can rotate, translate, and zoom in on structures, adjust visualization modes, inspect chain-level features and view secondary structure assignments. The tool supports simultaneous visualization of up to nine protein structures and can directly display structural alignments using Foldseeks output, enabling rapid comparative analysis in headless environments. The source code is available at https://github.com/steineggerlab/StrucTTY. O_TEXTBOXKey MessagesO_LIReal-time, interactive protein structure visualization directly within text-only terminals C_LIO_LIASCII-based, depth-aware rendering of PDB and mmCIF backbone structures C_LIO_LIMulti-structure comparison with direct application of Foldseek alignment transformations C_LIO_LIDesigned for headless workflows on remote servers and HPC systems C_LI C_TEXTBOX