CryoET of ß-amyloid and tau within postmortem Alzheimer's disease brain.

A defining pathological feature of most neurodegenerative diseases is the assembly of proteins into amyloid that form disease-specific structures1. In Alzheimer's disease, this is characterized by the deposition of ß-amyloid and tau with disease-specific conformations. The in situ structure of amyloid in the human brain is unknown. Here, using cryo-fluorescence microscopy-targeted cryo-sectioning, cryo-focused ion beam-scanning electron microscopy lift-out and cryo-electron tomography, we determined in-tissue architectures of ß-amyloid and tau pathology in a postmortem Alzheimer's disease donor brain. ß-amyloid plaques contained a mixture of fibrils, some of which were branched, and protofilaments, arranged in parallel arrays and lattice-like structures. Extracellular vesicles and cuboidal particles defined the non-amyloid constituents of ß-amyloid plaques. By contrast, tau inclusions formed parallel clusters of unbranched filaments. Subtomogram averaging a cluster of 136 tau filaments in a single tomogram revealed the polypeptide backbone conformation and filament polarity orientation of paired helical filaments within tissue. Filaments within most clusters were similar to each other, but were different between clusters, showing amyloid heterogeneity that is spatially organized by subcellular location. The in situ structural approaches outlined here for human donor tissues have applications to a broad range of neurodegenerative diseases.

Likelihood-based interactive local docking into cryo-EM maps in ChimeraX.

The interpretation of cryo-EM maps often includes the docking of known or predicted structures of the components, which is particularly useful when the map resolution is worse than 4 Å. Although it can be effective to search the entire map to find the best placement of a component, the process can be slow when the maps are large. However, frequently there is a well-founded hypothesis about where particular components are located. In such cases, a local search using a map subvolume will be much faster because the search volume is smaller, and more sensitive because optimizing the search volume for the rotation-search step enhances the signal to noise. A Fourier-space likelihood-based local search approach, based on the previously published em_placement software, has been implemented in the new emplace_local program. Tests confirm that the local search approach enhances the speed and sensitivity of the computations. An interactive graphical interface in the ChimeraX molecular-graphics program provides a convenient way to set up and evaluate docking calculations, particularly in defining the part of the map into which the components should be placed.

Outcomes of the EMDataResource cryo-EM Ligand Modeling Challenge.

The EMDataResource Ligand Model Challenge aimed to assess the reliability and reproducibility of modeling ligands bound to protein and protein-nucleic acid complexes in cryogenic electron microscopy (cryo-EM) maps determined at near-atomic (1.9-2.5 Å) resolution. Three published maps were selected as targets: Escherichia coli beta-galactosidase with inhibitor, SARS-CoV-2 virus RNA-dependent RNA polymerase with covalently bound nucleotide analog and SARS-CoV-2 virus ion channel ORF3a with bound lipid. Sixty-one models were submitted from 17 independent research groups, each with supporting workflow details. The quality of submitted ligand models and surrounding atoms were analyzed by visual inspection and quantification of local map quality, model-to-map fit, geometry, energetics and contact scores. A composite rather than a single score was needed to assess macromolecule+ligand model quality. These observations lead us to recommend best practices for assessing cryo-EM structures of liganded macromolecules reported at near-atomic resolution.

Welcoming five new Co-editors.

Five new Co-editors are appointed to the Editorial Board of Acta Cryst. D - Structural Biology.

Outcomes of the EMDataResource Cryo-EM Ligand Modeling Challenge.

The EMDataResource Ligand Model Challenge aimed to assess the reliability and reproducibility of modeling ligands bound to protein and protein/nucleic-acid complexes in cryogenic electron microscopy (cryo-EM) maps determined at near-atomic (1.9-2.5 Å) resolution. Three published maps were selected as targets: E. coli beta-galactosidase with inhibitor, SARS-CoV-2 RNA-dependent RNA polymerase with covalently bound nucleotide analog, and SARS-CoV-2 ion channel ORF3a with bound lipid. Sixty-one models were submitted from 17 independent research groups, each with supporting workflow details. We found that (1) the quality of submitted ligand models and surrounding atoms varied, as judged by visual inspection and quantification of local map quality, model-to-map fit, geometry, energetics, and contact scores, and (2) a composite rather than a single score was needed to assess macromolecule+ligand model quality. These observations lead us to recommend best practices for assessing cryo-EM structures of liganded macromolecules reported at near-atomic resolution.

AlphaFold predictions are valuable hypotheses and accelerate but do not replace experimental structure determination.

Artificial intelligence-based protein structure prediction methods such as AlphaFold have revolutionized structural biology. The accuracies of these predictions vary, however, and they do not take into account ligands, covalent modifications or other environmental factors. Here, we evaluate how well AlphaFold predictions can be expected to describe the structure of a protein by comparing predictions directly with experimental crystallographic maps. In many cases, AlphaFold predictions matched experimental maps remarkably closely. In other cases, even very high-confidence predictions differed from experimental maps on a global scale through distortion and domain orientation, and on a local scale in backbone and side-chain conformation. We suggest considering AlphaFold predictions as exceptionally useful hypotheses. We further suggest that it is important to consider the confidence in prediction when interpreting AlphaFold predictions and to carry out experimental structure determination to verify structural details, particularly those that involve interactions not included in the prediction.

Michael James (1940-2023).

Michael James is remembered.

Multimeric structure of a subfamily III haloalkane dehalogenase-like enzyme solved by combination of cryo-EM and x-ray crystallography.

Haloalkane dehalogenase (HLD) enzymes employ an SN 2 nucleophilic substitution mechanism to erase halogen substituents in diverse organohalogen compounds. Subfamily I and II HLDs are well-characterized enzymes, but the mode and purpose of multimerization of subfamily III HLDs are unknown. Here we probe the structural organization of DhmeA, a subfamily III HLD-like enzyme from the archaeon Haloferax mediterranei, by combining cryo-electron microscopy (cryo-EM) and x-ray crystallography. We show that full-length wild-type DhmeA forms diverse quaternary structures, ranging from small oligomers to large supramolecular ring-like assemblies of various sizes and symmetries. We optimized sample preparation steps, enabling three-dimensional reconstructions of an oligomeric species by single-particle cryo-EM. Moreover, we engineered a crystallizable mutant (DhmeAΔGG ) that provided diffraction-quality crystals. The 3.3 Å crystal structure reveals that DhmeAΔGG forms a ring-like 20-mer structure with outer and inner diameter of ~200 and ~80 Å, respectively. An enzyme homodimer represents a basic repeating building unit of the crystallographic ring. Three assembly interfaces (dimerization, tetramerization, and multimerization) were identified to form the supramolecular ring that displays a negatively charged exterior, while its interior part harboring catalytic sites is positively charged. Localization and exposure of catalytic machineries suggest a possible processing of large negatively charged macromolecular substrates.

AlphaFold and the future of structural biology.

This editorial acknowledges the transformative impact of new machine-learning methods, such as the use of AlphaFold, but also makes the case for the continuing need for experimental structural biology.

AlphaFold and the future of structural biology.

This editorial acknowledges the transformative impact of new machine-learning methods, such as the use of AlphaFold, but also makes the case for the continuing need for experimental structural biology.

AlphaFold and the future of structural biology.

This editorial acknowledges the transformative impact of new machine-learning methods, such as the use of AlphaFold, but also makes the case for the continuing need for experimental structural biology.

Accelerating crystal structure determination with iterative AlphaFold prediction.

Experimental structure determination can be accelerated with artificial intelligence (AI)-based structure-prediction methods such as AlphaFold. Here, an automatic procedure requiring only sequence information and crystallographic data is presented that uses AlphaFold predictions to produce an electron-density map and a structural model. Iterating through cycles of structure prediction is a key element of this procedure: a predicted model rebuilt in one cycle is used as a template for prediction in the next cycle. This procedure was applied to X-ray data for 215 structures released by the Protein Data Bank in a recent six-month period. In 87% of cases our procedure yielded a model with at least 50% of Cα atoms matching those in the deposited models within 2 Å. Predictions from the iterative template-guided prediction procedure were more accurate than those obtained without templates. It is concluded that AlphaFold predictions obtained based on sequence information alone are usually accurate enough to solve the crystallographic phase problem with molecular replacement, and a general strategy for macromolecular structure determination that includes AI-based prediction both as a starting point and as a method of model optimization is suggested.

Structures of apo Cas12a and its complex with crRNA and DNA reveal the dynamics of ternary complex formation and target DNA cleavage.

Cas12a is a programmable nuclease for adaptive immunity against invading nucleic acids in CRISPR-Cas systems. Here, we report the crystal structures of apo Cas12a from Lachnospiraceae bacterium MA2020 (Lb2) and the Lb2Cas12a+crRNA complex, as well as the cryo-EM structure and functional studies of the Lb2Cas12a+crRNA+DNA complex. We demonstrate that apo Lb2Cas12a assumes a unique, elongated conformation, whereas the Lb2Cas12a+crRNA binary complex exhibits a compact conformation that subsequently rearranges to a semi-open conformation in the Lb2Cas12a+crRNA+DNA ternary complex. Notably, in solution, apo Lb2Cas12a is dynamic and can exist in both elongated and compact forms. Residues from Met493 to Leu523 of the WED domain undergo major conformational changes to facilitate the required structural rearrangements. The REC lobe of Lb2Cas12a rotates 103° concomitant with rearrangement of the hinge region close to the WED and RuvC II domains to position the RNA-DNA duplex near the catalytic site. Our findings provide insight into crRNA recognition and the mechanism of target DNA cleavage.

Likelihood-based signal and noise analysis for docking of models into cryo-EM maps.

Fast, reliable docking of models into cryo-EM maps requires understanding of the errors in the maps and the models. Likelihood-based approaches to errors have proven to be powerful and adaptable in experimental structural biology, finding applications in both crystallography and cryo-EM. Indeed, previous crystallographic work on the errors in structural models is directly applicable to likelihood targets in cryo-EM. Likelihood targets in Fourier space are derived here to characterize, based on the comparison of half-maps, the direction- and resolution-dependent variation in the strength of both signal and noise in the data. Because the signal depends on local features, the signal and noise are analysed in local regions of the cryo-EM reconstruction. The likelihood analysis extends to prediction of the signal that will be achieved in any docking calculation for a model of specified quality and completeness. A related calculation generalizes a previous measure of the information gained by making the cryo-EM reconstruction.

Likelihood-based docking of models into cryo-EM maps.

Optimized docking of models into cryo-EM maps requires exploiting an understanding of the signal expected in the data to minimize the calculation time while maintaining sufficient signal. The likelihood-based rotation function used in crystallography can be employed to establish plausible orientations in a docking search. A phased likelihood translation function yields scores for the placement and rigid-body refinement of oriented models. Optimized strategies for choices of the resolution of data from the cryo-EM maps to use in the calculations and the size of search volumes are based on expected log-likelihood-gain scores computed in advance of the search calculation. Tests demonstrate that the new procedure is fast, robust and effective at placing models into even challenging cryo-EM maps.

Putting AlphaFold models to work with phenix.process_predicted_model and ISOLDE.

AlphaFold has recently become an important tool in providing models for experimental structure determination by X-ray crystallography and cryo-EM. Large parts of the predicted models typically approach the accuracy of experimentally determined structures, although there are frequently local errors and errors in the relative orientations of domains. Importantly, residues in the model of a protein predicted by AlphaFold are tagged with a predicted local distance difference test score, informing users about which regions of the structure are predicted with less confidence. AlphaFold also produces a predicted aligned error matrix indicating its confidence in the relative positions of each pair of residues in the predicted model. The phenix.process_predicted_model tool downweights or removes low-confidence residues and can break a model into confidently predicted domains in preparation for molecular replacement or cryo-EM docking. These confidence metrics are further used in ISOLDE to weight torsion and atom-atom distance restraints, allowing the complete AlphaFold model to be interactively rearranged to match the docked fragments and reducing the need for the rebuilding of connecting regions.

Improved AlphaFold modeling with implicit experimental information.

Machine-learning prediction algorithms such as AlphaFold and RoseTTAFold can create remarkably accurate protein models, but these models usually have some regions that are predicted with low confidence or poor accuracy. We hypothesized that by implicitly including new experimental information such as a density map, a greater portion of a model could be predicted accurately, and that this might synergistically improve parts of the model that were not fully addressed by either machine learning or experiment alone. An iterative procedure was developed in which AlphaFold models are automatically rebuilt on the basis of experimental density maps and the rebuilt models are used as templates in new AlphaFold predictions. We show that including experimental information improves prediction beyond the improvement obtained with simple rebuilding guided by the experimental data. This procedure for AlphaFold modeling with density has been incorporated into an automated procedure for interpretation of crystallographic and electron cryo-microscopy maps.

Crystal structures of BMPRII extracellular domain in binary and ternary receptor complexes with BMP10.

Heterozygous mutations in BMPR2 (bone morphogenetic protein (BMP) receptor type II) cause pulmonary arterial hypertension. BMPRII is a receptor for over 15 BMP ligands, but why BMPR2 mutations cause lung-specific pathology is unknown. To elucidate the molecular basis of BMP:BMPRII interactions, we report crystal structures of binary and ternary BMPRII receptor complexes with BMP10, which contain an ensemble of seven different BMP10:BMPRII 1:1 complexes. BMPRII binds BMP10 at the knuckle epitope, with the A-loop and ß4 strand making BMPRII-specific interactions. The BMPRII binding surface on BMP10 is dynamic, and the affinity is weaker in the ternary complex than in the binary complex. Hydrophobic core and A-loop interactions are important in BMPRII-mediated signalling. Our data reveal how BMPRII is a low affinity receptor, implying that forming a signalling complex requires high concentrations of BMPRII, hence mutations will impact on tissues with highest BMPR2 expression such as the lung vasculature.

Submission of structural biology data for review purposes.

The editors discuss the submission of structural biology data.

Implications of AlphaFold2 for crystallographic phasing by molecular replacement.

The AlphaFold2 results in the 14th edition of Critical Assessment of Structure Prediction (CASP14) showed that accurate (low root-mean-square deviation) in silico models of protein structure domains are on the horizon, whether or not the protein is related to known structures through high-coverage sequence similarity. As highly accurate models become available, generated by harnessing the power of correlated mutations and deep learning, one of the aspects of structural biology to be impacted will be methods of phasing in crystallography. Here, the data from CASP14 are used to explore the prospects for changes in phasing methods, and in particular to explore the prospects for molecular-replacement phasing using in silico models.