Acta Crystallographica. Section D, Structural Biology最新文献

Predicting the 3D structure of RNA is a significant challenge despite ongoing advancements in the field. Although AlphaFold has successfully addressed this problem for proteins, RNA structure prediction raises difficulties due to the fundamental differences between proteins and RNA, which hinder its direct adaptation. The latest release of AlphaFold, AlphaFold3, has broadened its scope to include multiple different molecules such as DNA, ligands and RNA. While the AlphaFold3 article discussed the results for the last CASP-RNA data set, the scope of its performance and the limitations for RNA are unclear. In this article, we provide a comprehensive analysis of the performance of AlphaFold3 in the prediction of 3D structures of RNA. Through an extensive benchmark over five different test sets, we discuss the performance and limitations of AlphaFold3. We also compare its performance with ten existing state-of-the-art ab initio, template-based and deep-learning approaches. Our results are freely available on the EvryRNA platform at https://evryrna.ibisc.univ-evry.fr/evryrna/alphafold3/.

Stephen Harrop is remembered.

P-clusters have been statistically analysed using the bond-valence sum (BVS) method together with weighting schemes. The crystallographic data come from the VFe proteins deposited in the Protein Data Bank (PDB) with high resolutions of better than 1.35 Å. Calculations show that the formal oxidation state of a P¹⁺ cluster can be assigned as 2Fe³⁺6Fe²⁺ with high electron delocalization, giving the same oxidation state as that of P^N clusters in VFe proteins. Further comprehensive comparisons of the bond distances suggest that the hydroxyl groups of the β-153 serine residues in P¹⁺ and P^N clusters are in the protonated state, where the Fe6 atoms have the same oxidation state as Fe²⁺. During the transition from P^N to P¹⁺, cleavage of the Fe6-S1 bond is accompanied by the formation of a weak coordination between the Fe6 atom and the hydroxyl group of the β-153 serine residue in the P¹⁺ cluster of the VFe protein. Similarly, oxidation of P^N to P¹⁺/P²⁺ clusters corresponds to the coordination of Fe6(II) by the hydroxyl group of the β-188 serine residue and of Fe5(II) by the peptide amine group of the α-88 cysteine residue in the MoFe protein of Azotobacter vinelandiis without electron and proton transfers.

Cryo-electron tomography is a rapidly developing field for studying macromolecular complexes in their native environments and has the potential to revolutionize our understanding of protein function. However, fast and accurate identification of particles in cryo-tomograms is challenging and represents a significant bottleneck in downstream processes such as subtomogram averaging. Here, we present tomoCPT (Tomogram Centroid Prediction Tool), a transformer-based solution that reformulates particle detection as a centroid-prediction task using Gaussian labels. Our approach, which is built upon the SwinUNETR architecture, demonstrates superior performance compared with both conventional binary labelling strategies and template matching. We show that tomoCPT effectively generalizes to novel particle types through zero-shot inference and can be significantly enhanced through fine-tuning with limited data. The efficacy of tomoCPT is validated using three case studies: apoferritin, achieving a resolution of 3.0 Å compared with 3.3 Å using template matching, SARS-CoV-2 spike proteins on cell surfaces, yielding an 18.3 Å resolution map where template matching proved unsuccessful, and rubisco molecules within carboxysomes, reaching 8.0 Å resolution. These results demonstrate the ability of tomoCPT to handle varied scenarios, including densely packed environments and membrane-bound proteins. The implementation of the tool as a command-line program, coupled with its minimal data requirements for fine-tuning, makes it a practical solution for high-throughput cryo-ET data-processing workflows.

Molecular replacement (MR) is highly effective for biomolecular crystal structure determination, increasingly so as the database of known structures has increased. For candidates without recognizable similarity to known structures, however, crystal structure analyses have nearly always required experiments for de novo phase evaluation. Now, with the unprecedented accuracy of AlphaFold predictions of protein structures from amino-acid sequences, an appreciable expansion of the reach of MR for proteins is realized. Here, we sought to automate an AlphaFold-guided MR procedure that tailors predictions to the MR problem at hand. We first optimized the reliability cutoff parameters for residue inclusion as tested in application to a previously MR-intractable problem. We then examined cases where AlphaFold by default predicts a conformation alternative to that of the candidate structure, devising tests for MR solution either from domain-specific predictions or from predictions based on diverse sequence subclusters. We tested subclustering procedures on an enzyme system that entails multiple MR-challenging conformations. The overall process as implemented in Phenix automatically surveys a succession of trials of increasing computational complexity until an MR solution is found or the options are exhausted. Validated MR solutions were found for 92% of one set of 158 challenging problems from the PDB and 93% of those from a second set of 215 challenges. Thus, many crystal structure analyses that previously required experimental phase evaluation can now be solved by AlphaFold-guided MR. In effect, this and related MR approaches are de novo phasing methods.

The Guest Editors introduce the special issue based on talks at the CCP4 Study Weekend 2023. The virtual issue is available at https://journals.iucr.org/special_issues/2024/CCP42023/.

Recent advances in low-emittance synchrotron X-ray technology and highly sensitive photon-counting detectors have revolutionized protein micro-crystallography in structural biology. These developments and improvements to sample-exchange robots and beamline control have paved the way for automated and efficient unattended data collection. This study analyzed protein crystal structures such as type 2 angiotensin II receptor, CNNM/CorC membrane proteins and polyhedral protein crystals using small-wedge synchrotron crystallography (SWSX), which dramatically improves measurement efficiency through automated measurement. We evaluated the data quality using SWSX, focusing on `massive data collection'. In this context, `massive' refers to data sets with a multiplicity exceeding 100. The findings could potentially lead to the development of more efficient experimental conditions, such as obtaining high-resolution data using a smaller number of crystals. We have demonstrated that the application of machine learning, a modern key component of data science, to classify data groups is an integral part of the analysis process and may play a crucial role in improving data quality. These results indicate that SWSX is one of the essential candidates for crystal structure analysis methods for difficult-to-analyze samples: it can enable diverse and complex protein functional analysis.

The type IV pilus is a diverse molecular machine capable of conferring a variety of functions and is produced by a wide range of bacterial species. The ability of the pilus to perform host-cell adherence makes it a viable target for the development of vaccines against infection by human pathogens such as Pseudomonas aeruginosa. Here, the 1.3 Å resolution crystal structure of the N-terminally truncated type IV pilin from P. aeruginosa strain P1 (ΔP1) is reported, the first structure of its phylogenetically linked group (group I) to be discussed in the literature. The structure was solved from X-ray diffraction data that were collected 20 years ago with a molecular-replacement search model generated using AlphaFold; the effectiveness of other search models was analyzed. Examination of the high-resolution ΔP1 structure revealed a solvent network that aids in maintaining the fold of the protein. On comparing the sequence and structure of P1 with a variety of type IV pilins, it was observed that there are cases of higher structural similarities between the phylogenetic groups of P. aeruginosa than there are between the same phylogenetic group, indicating that a structural grouping of pilins may be necessary in developing antivirulence drugs and vaccines. These analyses also identified the α-β loop as the most structurally diverse domain of the pilins, which could allow it to serve a role in pilus recognition. Studies of ΔP1 in vitro polymerization demonstrate that the optimal hydrophobic catalyst for the oligomerization of the pilus from strain K122 is not conducive for pilus formation of ΔP1; a model of a three-start helical assembly using the ΔP1 structure indicates that the α-β loop and the D-loop prevent in vitro polymerization.

Recently, the conclusions drawn from crystallographic data about the number of oxygen ligands associated with the CaMn₄ cofactor in the oxygen-evolving center (OEC) of Thermosynechococcus vulcanus photosystem II (PSII) have been called into question. Here, using OEC-omit, metal ion-omit and ligand-omit electron-density maps, it is shown that the number of oxygen ligands ranges from three in the functional OEC of monomer B following dark adaption (0F), i.e. in its ground state (PDB entry 6jlj/0F and PDB entry 6jlm/0F), to five for both monomers of PSII in photo-advanced states following exposure to one and two flashes of light. For a significant fraction of the 0F OECs in monomer A, the number is four (PDB entry 6jlj/0F). Following one flash it increases to five (PDB entry 6jlk/1F), where it remains after a second flash (PDB entry 6jlj/2F). Following a third flash (3F), it decreases to three (PDB entry 6jlp/3F), suggesting that an O₂ molecule has been produced. These observations suggest a mechanism for the reaction that transforms the O atoms of the water molecules bound at the O3 and O1 sites of the OEC into O₂.

Metals are essential components for the structure and function of many proteins. However, accurate modelling of their coordination environments remains a challenge due to the complexity and diversity of metal-coordination geometries. To address this, a method is presented for extracting and analysing coordination information, including bond lengths and angles, from the Crystallography Open Database. By using these data, comprehensive descriptions of metal-containing components are generated. A stereochemical information generator for a particular component within a specific macromolecule leverages an example PDB/mmCIF file containing the component to account for the actual surrounding environment. A matching process has been developed and implemented to align the derived metal structures with idealized coordinates from a coordination geometry library. Additionally, various strategies, depending on the quality of the matches, were employed to compile distance and angle statistics for the refinement of macromolecular structures. The developed methods were implemented in a new program, MetalCoord, that classifies and utilizes the metal-coordination geometry. The effectiveness of the developed algorithms was tested using metal-containing components from the PDB. As a result, metal-containing components from the CCP4 monomer library have been updated. The updated monomer dictionaries, in concert with the derived restraints, can be used in most structural biology computations, including macromolecular crystallography, single-particle cryo-EM and even molecular mechanics.