Acta Crystallographica Section D Structural Biology

Here, we present MC-Bayes, a Python-based script for processing cryo-electron microscopy EER movies on one or more GPUs using MotionCor3 in a user-friendly manner. Further, it generates the .star files necessary for RELION to perform Bayesian polishing (a.k.a.: reference-based motion correction) with EER movies. Until now, Bayesian polishing of EER data was only possible if the CPU-based "RELIONCor" implementation of MotionCor2 was used, which is sub-optimal on GPU-heavy cryo-EM processing systems. This wrapper was created for those facilities and/or users who may have (many) powerful GPUs, but for whatever reason have few CPU cores or less system RAM. Leveraging MotionCor3, MC-Bayes allows motion correction of EER data 2 or more times faster (depending on system) than the RELION CPU implementation, except in circumstances where dozens or hundreds of CPU cores with high quantities of system RAM can be utilised.

Three-dimensional electron diffraction (3DED) is an emerging and useful technique for solving molecular structures of small and biological macro-molecules from nanometre-sized crystals. We present our automated data processing workflow for 3DED datasets collected at Diamond Light Sources electron Bio-Imaging Centre (eBIC). For this purpose, we developed a package called AutoED. The processing pipeline includes data collection, analysis of the beam position, metadata gathering, file conversion, and finally data processing using xia2 (which supports both DIALS and XDS). The processing results are captured in a summary report produced by AutoED. Our main goal is to reduce the workload of electron diffraction scientists, but also to enforce good standards already used in macromolecular crystallography (MX). All the collected 3DED datasets are automatically converted into NeXus data format which is considered a Gold Standard for MX. This standardized data format allows for all the relevant metadata about the experiment to be kept together with diffraction images. We also discuss the methods used in AutoED to determine the electron beam position on diffraction images.

Cryo-electron tomography is an expanding technology for the study of macromolecules, viruses, and cells. It is often applied to specimens that are too large or heterogeneous for methods based on 2D image averaging such as single particle analysis, e.g., intracellular membranes or organelles. Current practice records a tilt series of projection images in rotation. Reconstruction is normally an ill-posed mathematical problem. Particularly for the under-determined case of sparse data, discrete tilt angles, and a limited tilt range, characteristic artifacts appear in the reconstructed slices. Much of what appears as noise is in fact structural: the projection of contrast from different planes. Various schemes are employed to regularize the reconstruction, including machine-learning frameworks built on neural networks. To the extent that the noise is structural, it might be suppressed by deconvolution with a suitable kernel. This was demonstrated and has been used regularly in cryo-STEM tomography of thick specimens where the under-sampling problem is particularly acute. Here we present 3dcon as an open-source extension of the entropy-regularized deconvolution algorithm that had been adopted from fluorescence microscopy. It takes advantage of modern computing hardware for convenient and fast processing. Deconvolution is entirely algorithmic, meaning that successful processing of the data does not depend on the data itself. As such it should be robust in a wide variety of applications.

Directional anisotropy in electron density provides key information about chemical bonding that is not readily accessible from conventional electron-density maps. Here, a model-independent framework is presented for decomposing experimental structure factors into angular components using spherical harmonics. Reciprocal-space projection onto spherical harmonics followed by standard Fourier synthesis yields angularly filtered density maps. The{ell} = 0 component captures the isotropic part of the density, while the{ell} = 1 components resemble px, py and pz-like dipolar functions that highlight directional electronic structure. Applications to high-resolution datasets, including urea, the Gly-Ala dipeptide and a 0.97 [A]{beta}-lactamase structure, reveal chemically interpretable dipolar features associated with carbonyl and amide bonds, N-H interactions and aromatic{pi} systems. Quantitative analysis using bond-centred sampling demonstrates stable dipolar signatures that remain detectable under moderate resolution truncation. These results establish spherical-harmonic angular decomposition as a practical framework for extracting directional electronic information from crystallographic electron-density maps. SynopsisAngular decomposition of experimental structure factors reveals dipolar anisotropy and directional electron-density features that are directly meaningful for chemical interpretation.

Most atomic model refinement methods in cryo-EM fit models to the reconstructed density map and effectively treat Fourier voxels as equally reliable. However, the uncertainty in the estimation of Fourier coefficients is highly anisotropic, primarily due to the common variability in SNR in different frequency shells and the distribution of particle images across viewing directions. First-principles arguments suggest that atomic models should be fitted to particle images rather than volumes; this strategy may be computationally demanding. We show that under certain modeling choices, fitting atomic models to weighted volumes is equivalent to fitting directly to particle images. Furthermore, we argue that various proxies can be used to capture this and other sources of uncertainty and distortions. We propose that the principle can be implemented in most atomic model-fitting software with relative ease, using information readily available in existing pipelines. As a proof of concept, we extracted the necessary information from standard RELION runs and fed it into a modified version of Servalcat in which we implemented a reinterpreted version of the idea.

Images in the electron microscope are formed by electron scattering and focusing. The spherical geometry of these processes gives rise to two coherent, conjugate spherical wave fronts, known as Ewald spheres. These spheres are associated with the two halves of the contrast transfer function (CTF), and their widths are determined by the focal gradient through the specimen. To properly correct for the CTF, each half of the CTF must be applied to an image individually and integrated into the reconstruction into the corresponding Ewald sphere. Theory indicates that this dual Ewald sphere reconstruction method should recover the maximal amount of information possible. This method was compared to the other reconstruction methods commonly used: the projection approximation (ignoring the Ewald sphere), the simple insertion and the single sideband methods. In simulated reconstructions the dual Ewald sphere method recovered the most information when the correct half of the CTF is matched to the corresponding Ewald sphere. If the wrong half is matched, the result worse than the projection approximation method. Examining reconstructions from real data indicated that the dual Ewald sphere method performs at least as well as the simple insertion method, but not as good as in simulations. The likely reason is the two-fold ambiguity in the assigned orientations of the particle images, which remains an issue to pursue in further studies. In conclusion, the dual Ewald sphere reconstruction method may offer the best way to calculate very high resolution reconstructions when the micrograph quality warrants it. HighlightsO_LIThe dual Ewald sphere reconstruction corrects for the two halves of the CTF. C_LIO_LIThe signs of the two halves of the CTF must correspond to the focal gradient. C_LIO_LIDetermining the focal gradient for individual particle images remains unresolved. C_LIO_LIComplex reconstructions indicate any real space phases are artifacts. C_LI

Accurate assessment of ligand coordinate-density consistency across different resolutions remains challenging in macromolecular crystallography. We introduce the atomic Box Correlation Coefficient (aBCC), an atom-level metric for evaluating the consistency between ligand atomic coordinates and electron density in a resolution-standardized framework. To predict aBCC values from electron-density maps, we developed QAEmap, a machine-learning model based on three-dimensional convolutional neural networks (3D-CNNs). The model was trained using Fourier-truncated electron-density maps and corresponding ligand coordinates generated from high-resolution structures in the Protein Data Bank. It was evaluated using both Fourier-truncated electron-density maps and experimentally determined PDB structures. was evaluated using both Fourier-truncated electron-density maps and experimentally determined PDB structures.The prediction accuracy gradually decreased with decreasing resolution, but remained reliable up to [~]3.5 [A]. These results demonstrate that aBCC enables resolution-standardized atom-wise evaluation of coordinate-density consistency across different resolutions and provide a foundation for further development and refinement of machine learning-based coordinate validation. SynopsisWe introduce the atomic box correlation coefficient (aBCC), a machine learning-based metric for the resolution-standardized atom-level evaluation of ligand coordinate-density consistency in crystallographic structures. aBCC provides a common framework for assessing and communicating the local coordinate reliability between structural biologists and researchers in structure-based drug discovery.

Although an increasing number of protein structures are determined by cryogenic electron microscopy (cryo-EM), protein structure modeling frequently suffers from residue misassignments and sequence register shifts, particularly in regions with ambiguous density. Here, we present DAQplugin, a ChimeraX plugin that performs real-time evaluation of protein models against cryo-EM density maps using the deep-learning-based residue-wise model quality (DAQ) score. Unlike existing validation tools that are typically applied after model construction, DAQplugin enables real-time deep-learning-based validation during model building and refinement. To our knowledge, DAQplugin is the first tool that provides real-time deep-learning based validation of protein models for cryo-EM map within an interactive modeling environment. In addition to identifying potential modeling errors, DAQplugin also provides guidance for correcting sequence register shifts by suggesting alternative residue placements along the backbone. The computation in this plugin is designed to run efficiently on general CPUs without requiring GPU hardware. Using DAQplugin, users can perform deep-learning-based validation on standard laptops during interactive model building, model-map fitting, and refinement. DAQplugin is able to facilitate more accurate interpretation of cryo-EM density maps and improve the reliability assessment of protein structure models. SynopsisDAQplugin provides real-time residue-wise validation of protein models with cryo-EM maps in ChimeraX.

Cryogenic electron microscopy (cryoEM) is now routinely used for high-resolution structure determination of biological macromolecules. However, many biological specimens exhibit varying degrees of preferred orientation on cryoEM grids, resulting in uneven sampling of three-dimensional Fourier space. This orientation bias produces anisotropic reconstruction artifacts and, in severe cases, can exacerbate particle misalignment during iterative refinement, thereby limiting the success rate of near-atomic resolution cryoEM structure determination. This protocol provides a practical guide for applying spIsoNet, a self-supervised deep-learning method, to mitigate preferred-orientation issues in cryoEM reconstructions. We describe two complementary workflows: (1) map Anisotropy Correction to correct anisotropic artifacts of cryoEM maps and (2) particle Misalignment Correction, which integrates spIsoNet with RELION external reconstruction to improve particle-pose estimation. We demonstrate these workflows using two influenza hemagglutinin (HA) trimer datasets representing moderate and severe degrees of preferred-orientation bias. The protocol includes installation instructions, parameter-selection guidance, quality-control checkpoints and troubleshooting advice, and can typically be completed in ~7 hours on a workstation equipped with four NVIDIA A100 GPUs. Together, these workflows provide step-by-step guidance for using the open-source spIsoNet software to mitigate the preferred-orientation problem directly from experimental data.

Damage to biological specimens by the electron beam is the fundamental resolution-limiting factor in cryoelectron microscopy (cryo-EM) single particle analysis. There is, however, currently no method to accurately infer fluence-dependent changes to the specimen structure during electron irradiation. We develop a Bayesian framework to fit a sequence of atomic models to a series of cryo-EM reconstructions produced at increasing fluence. In particular, our algorithm is able to infer the ensemble average position and atomic displacement parameter of every atom in the macromolecule as a function of fluence. Application of the algorithm to cryo-EM datasets shows that the molecule expands during imaging and identifies environment-dependent variations in beam-induced damage. We use our results to propose a stochastic process model of this phenomenon. We envisage that our method will lead to a better mechanistic understanding of radiation damage to biological specimens and may contribute to efforts to mitigate its effects.

Accurate estimation of the contrast transfer function (CTF) of tilt images is a critical first step in cryo electron tomography (cryoET), enabling reliable recovery of high-resolution structural information from thick, heterogeneous specimens. This challenge is especially acute in in situ cryoET, where macromolecules are imaged in their native cellular environment, often at high tilt and through substantial specimen thickness, with correspondingly low signal-to-noise ratios. Although CTF parameters can be later refined using reference-based approaches, accurate initial estimates are critical for downstream processing and the interpretability of tomographic reconstructions, yet they remain difficult to automate. Here, we present Quinoa, a software package designed to address these challenges. Quinoa first validates the tilt geometry and assesses data quality to generate robust initial estimates of defocus and phase shift. These estimates are then refined through optimization of a single global model, enabling precise fitting of the per-image defoci, tilt-dependent astigmatisms, time-dependent phase shifts, the specimen orientation (rotation, tilt and pitch) and the specimen thickness. Notably, and as a key distinguishing feature of this approach is that Quinoa fits equiphase-binned polar power spectra. This substantially reduces the computational cost of optimization without sacrificing accuracy, enabling more progressive and exhaustive refinement passes that further improve robustness. We validated Quinoa using both simulated and experimental data and benchmarked its performance against Warp, Ctfplotter, CTFMeasure, and AreTomo. Our results show that Quinoa is the most robust approach across all simulated cases, maintaining high accuracy even in the simultaneous presence of severe astigmatism, high specimen inclination and variable phase shift. Integrated recovery mechanisms further allow Quinoa to adapt automatically to a wide range of pixel sizes, defoci, astigmatisms and specimen thicknesses. Despite fitting a more complex and dynamic model, Quinoa remains extremely efficient due to extensive GPU acceleration, making it well suited for real-time monitoring during data collection as well as high-throughput offline batch processing. By improving automated CTF estimation in challenging tomographic data, Quinoa supports more accurate structural analysis of cells and tissues in situ.

Cryogenic-electron microscopy (cryo-EM) has revolutionized the field of protein structural biology. The structures of large membrane proteins are now routinely determined by cryo-EM to near atomic resolution. However, in the medium resolution range of cryo-EM maps (>[~]2 [A]), negatively charged sidechains of acidic residues are not well-resolved due to the negative electrostatic potential of the region. This may lead to incorrect sidechain models for residues like glutamic acid or aspartic acid that are central for proton transfer activity in various respiratory and photosynthetic enzymes. We previously proposed that the acidic residues with weak or non-existent cryo-EM density can be modeled to represent their low proton affinity conformations. Here, we tested this hypothesis on a larger data set of acidic amino acid residues in two high-resolution respiratory complex I structures. By using faster sidechain modeling and proton affinity prediction tools, we created a workflow that generates sidechain conformations of selected amino acid residues. We validated the sidechain conformation predictions by Q-score analysis and atomistic molecular dynamics simulations in different charged states. The proposed workflow provides a way to rapidly obtain sidechain conformations of acidic residues with weak cryo-EM densities and can be integrated into the existing cryo-EM modeling pipelines to speed up sidechain rotamer prediction.

The quality of single particle cryo-EM reconstructions can be severely degraded when an insufficient variety of 3D particle orientations is present in the image data, limiting downstream model building and interpretation. However, it is often difficult to ascertain whether or not a particular dataset suffers from such preferred orientation since the required orientation coverage depends on target geometry, alignment accuracy, and particle quality. To simplify diagnosis of preferred orientation, we present two complementary methods. First, the conical Fourier Shell Correlation Area Ratio (cFAR) compares the worst- and best-correlating conical regions of 3D Fourier space to quantify half-map anisotropy into a single, easily interpretable score ranging from zero to one. Second, Relative Signal, a companion to cFAR, directly relates signal content to viewing direction so that under-sampled views can be identified. We characterize our methods and compare them to existing anisotropy detection approaches on synthetic data and on 14 real datasets that span sundry molecular weights and structure types. Implementations of both cFAR and Relative Signal are included in CryoSPARC v4.5 and later versions.

Bordetella pertussis, the causative agent of whooping cough, is a reemerging public health threat. While the Tripartite Tricarboxylate Transporter (TTT) system BctCBA was previously implicated solely in citrate uptake, we demonstrate that the solute-binding protein BctC specifically binds citrate chelated with Zn{superscript 2} and Ni{superscript 2}. To elucidate the molecular mechanism of this interaction, we determined the crystal structures of BctC in three states: apo, open, and closed (citrate-zinc-bound), defining the structural determinants for metal-citrate recognition. Comparative analyses suggest that citrate-mediated divalent cation binding is a widespread feature among bacterial TTT homologs. Finally, in silico modeling of the full BctCBA complex predicts an elevator-type transport mechanism. Together, these findings redefine the functional scope of BctCBA, revealing a sophisticated strategy by which B. pertussis exploits organic chelators to acquire essential trace metals during infection.

Microtubules are cytoskeletal filaments that are involved in intracellular transport, cell division, and motility. Despite their biological importance, determining their high-resolution structures via cryo-electron microscopy remains a significant technical challenge due to their polymorphisms and pseudo-helical assembly. Current processing workflows are complex, often requiring the integration of multiple software packages and custom scripts, which creates a steep learning curve for many research groups. To address these limitations, we introduce CsMT, a streamlined workflow implemented entirely within the CryoSPARC environment and using synthetic references. CsMT simplifies microtubule reconstruction by utilizing a novel protofilament-pair classification approach, which effectively handles the inherent pseudo-symmetry and structural heterogeneity of microtubules with minimal manual intervention. Our workflow is versatile, capable of processing both undecorated and decorated microtubules while accurately determining seams and performing high-resolution refinement. We demonstrate the efficacy of this workflow by achieving a 2.3 and 2.7 [A] resolution reconstruction of homotypic and heterotypic maps of undecorated microtubules, matching the best-resolved microtubule structures in the field. By unifying the pipeline into a single and portable workflow, CsMT enhances reproducibility and accessibility, empowering more laboratories to explore the structural biology of microtubules and associated proteins, yielding new insights into their function.

Single-particle cryo-electron microscopy (cryo-EM) pose estimation is traditionally solved anew for each dataset, where iterative refinement is done from scratch while the estimator learns to store the molecule in its weights. In this work, we show that pose inference is a generalizable, specimen-agnostic operation when conditioned explicitly on a reference volume. We introduce ARCHER, an amortized contrastive classifier that models the pose posterior over a discrete rotation grid. Trained across a variety of protein structures, it operates zero-shot without retraining per structure. This transferability is grounded in Fourier-space information mechanics, where all specimen dependence is captured by the reference structure's power spectrum and spatial extent. ARCHER achieves a median angular error of 5.0{degrees} on 100 held-out test structures and 2.5{degrees} on experimental particles, matching dedicated estimators within 0.16[A] in 3D reconstruction. Crucially, downstream conformational signal is preserved. The leading conformational coordinate correlates at 0.97 with deposited benchmarks, faithfully reconstructing free-energy basins and mobile domains. These results overall demonstrate that cryo-EM pose estimation can be generalized across different structures.

Plant Light Harvesting Complex II (LHCII) is found in the thylakoid membranes of chloroplasts and balances two roles: energy collection for photosynthesis and energy dissipation to prevent photo-damage when there is excessive sunlight. The mechanism for LHCII to switch between two energetic states has been debated but may involve a pH-triggered conformational change. Here we present single-particle cryo-electron microscopy (EM) structures of "light-harvesting" LHCII in detergent at pH 7.5 and pH 4.5. The high resolution (2.48 [A]) maps provide clear placements for all bound pigments, giving high confidence in the models. Surprisingly, we find that there is little conformational change to the polypeptide between these new light harvesting structures and previously published crystals structures, thought to be energy dissipating. The crossing angles of helix A/B and the Lutein 1-Chlorophyll 612 separation distances are similar. This contrasts with other recent analyses of LHCII by single-particle EM that suggested a change to the helix A/B angle and a reduction in Lutein 1-Chlorophyll 612 separation may trigger quenching and a photoprotective state. The high resolution of our structures also allowed us to investigate small conformational changes of the lutein within L1/L2 binding sites of LHCII, revealing rotations and distortions in the pigment that could lead to changes in energy transfer. In addition, we find that low pH causes LHCII to form a destabilised structure where pigment loss from the V1 binding site (usually violaxanthin or zeaxanthin) correlated with a disordered C-terminus, often for just one LHCII monomer with an LHCII trimer. Overall, our findings have important implications for the molecular mechanism of photoprotection.

Background: Comparisons between independently predicted wild-type and missense-variant protein structures can generate mechanistic hypotheses, but small apparent differences may reflect model-selection variability rather than mutation-specific effects. Methods: Human mitochondrial DNA polymerase gamma (POLG; UniProt P54098) variants p.Arg627Gln (R627Q) and p.Trp748Ser (W748S) were evaluated using five AlphaFold2-PTM network-model outputs per condition generated with one random seed under matched ColabFold settings. Ten pairwise wild type comparisons at each site described between-network model-selection variability. Variant effects were summarized across five within-network wild-type-versus-variant comparisons using rotation-invariant local C-alpha pair distances and local displacement after global and local alignment. Because these comparison designs differ, the wild-type distribution was used as context rather than a mutation-effect null. Wild-type cryo-EM structure 9GGF was used for contact and interface mapping. Experimental A467T and G848S structures 9GGE and 9GGC provided contextual benchmarks. Results: R627Q measurements fell within the range of between-network wild-type differences: its median mean local pair-distance change was 0.170 angstrom, compared with a wild-type median of 0.170 angstrom, and its locally aligned displacement was 0.265 versus 0.248 angstrom. W748S showed higher median values (0.168 versus 0.132 angstrom for pair-distance change; 0.236 versus 0.182 angstrom for locally aligned displacement), but the ranges overlapped and the comparison-design asymmetry precluded a calibrated mutation-effect percentile. Experimental A467T and G848S comparisons produced local changes of similar magnitude. In 9GGF, R627 and W748 directly shared a local microenvironment, with a minimum heavy-atom distance of 3.53 angstrom. R627 also formed short polar-contact candidates with D629 and D743, whereas W748 occupied a hydrophobic packing environment containing Y622 and F750. Both sites were more than 18 angstrom from nucleic acid, more than 30 angstrom from POLG2, and more than 33 angstrom from PZL-A in a ligand-bound structure. Conclusions: Available AlphaFold2 comparisons do not establish a mutation-specific structural deformation for either variant. Experimental-structure mapping supports testable physicochemical hypotheses involving a shared R627-W748 microenvironment - loss of an arginine-centered polar network for R627Q and disruption of a buried aromatic environment for W748S - but not direct DNA, POLG2, or PZL-A contact mechanisms. Matched control substitutions and independent seeds are required to calibrate small mutation-associated structural deltas.

X-ray fiber diffraction patterns exhibit four-fold symmetry that can be exploited, through folding and averaging, to improve signal-to-noise ratio. Accurate folding requires a precise sub-pixel estimate of the symmetry center and precise orientation of the meridional pattern axis to the fiber axis: small center or angular errors blur diffraction features, reduce layer-line sharpness, and introduce errors in spacing measurements. A pixel-level estimate is often too imprecise for this purpose, and detector gaps further complicate the alignment objective. We formulate the masked quadrant-folding problem, define a four-quadrant symmetry loss that consistently excludes invalid pixels, and evaluate several refinement strategies: hierarchical coarse-to-fine grid search; ECC-based rigid registration with global center/orientation correction fitting; ECC registration followed by local gradient refinement; and a hybrid that appends a local grid search on a cropped pattern. Direct gradient optimization from the rough QF alignment was found to be unreliable. Grid search provides a robust, interpretable baseline that directly minimizes the folding objective but is substantially slower than registration; ECC gives a fast near-correct alignment, and the hybrid closes the accuracy gap to brute-force search at a fraction of its runtime. On real datasets with calibration data, applying a calibration center with optimized rotation is effectively optimal. The hybrid center-refinement method has been integrated into the MuscleX package.

Four-dimensional scanning transmission electron microscopy (4D-STEM) enables the acquisition of diffraction patterns at every probe position in a dense array. For imaging applications this approach offers significant benefits in terms of spatial resolution and contrast enhancement. In this work, we present the development of a synchronous scan generator integrated with SerialEM software to enable automation of complex experimental protocols such as tomography. The proposed hardware functions as an interface between SerialEM, the scan controls of the microscope, a fast annular dark-field detector, and a synchronized trigger for a pixelated detector. Our previous implementation, named SavvyScan, relied on a dedicated computer equipped with a multichannel acquisition and signal-generation cards, as well as a separate microcontroller for synchronization. Here, we report a low-cost implementation based on a Red Pitaya board, utilizing direct programming of its embedded FPGA and Linux server components. We provide detailed instructions for system installation and operation, along with practical guidance for modifying the source code. System performance is validated through oscilloscope measurements and imaging of a replica grating sample. The utility of the approach is further demonstrated by generating a 3D electron tomogram of a cryogenic sample of mitochondria from a tilt series of shadow montage projections.