Acta Crystallographica. Section D, Structural Biology最新文献

Michael James is remembered.

Septins are membrane-associated, GTP-binding proteins that are present in most eukaryotes. They polymerize to play important roles as scaffolds and/or diffusion barriers as part of the cytoskeleton. α-Helical coiled-coil domains are believed to contribute to septin assembly, and those observed in both human SEPT6 and SEPT8 form antiparallel homodimers. These are not compatible with their parallel heterodimeric organization expected from the current model for protofilament assembly, but they could explain the interfilament cross-bridges observed by microscopy. Here, the first structure of a heterodimeric septin coiled coil is presented, that between SEPT14 and SEPT7; the former is a SEPT6/SEPT8 homolog. This new structure is parallel, with two long helices that are axially shifted by a full helical turn with reference to their sequence alignment. The structure also has unusual knobs-into-holes packing of side chains. Both standard seven-residue (heptad) and the less common 11-residue (hendecad) repeats are present, creating two distinct regions with opposite supercoiling, which gives rise to an overall straight coiled coil. Part of the hendecad region is required for heterodimerization and therefore may be crucial for selective septin recognition. These unconventional sequences and structural features produce a metastable heterocomplex that nonetheless has enough specificity to promote correct protofilament assembly. For instance, the lack of supercoiling may facilitate unzipping and transitioning to the antiparallel homodimeric state.

Raimond B. G. Ravelli is remembered.

The term robustness in statistics refers to methods that are generally insensitive to deviations from model assumptions. In other words, robust methods are able to preserve their accuracy even when the data do not perfectly fit the statistical models. Robust statistical analyses are particularly effective when analysing mixtures of probability distributions. Therefore, these methods enable the discretization of X-ray serial crystallography data into two probability distributions: a group comprising true data points (for example the background intensities) and another group comprising outliers (for example Bragg peaks or bad pixels on an X-ray detector). These characteristics of robust statistical analysis are beneficial for the ever-increasing volume of serial crystallography (SX) data sets produced at synchrotron and X-ray free-electron laser (XFEL) sources. The key advantage of the use of robust statistics for some applications in SX data analysis is that it requires minimal parameter tuning because of its insensitivity to the input parameters. In this paper, a software package called Robust Gaussian Fitting library (RGFlib) is introduced that is based on the concept of robust statistics. Two methods are presented based on the concept of robust statistics and RGFlib for two SX data-analysis tasks: (i) a robust peak-finding algorithm and (ii) an automated robust method to detect bad pixels on X-ray pixel detectors.

Colibactin is a genotoxic natural product produced by select commensal bacteria in the human gut microbiota. The compound is a bis-electrophile that is predicted to form interstrand DNA cross-links in target cells, leading to double-strand DNA breaks. The biosynthesis of colibactin is carried out by a mixed NRPS-PKS assembly line with several noncanonical features. An amidase, ClbL, plays a key role in the pathway, catalyzing the final step in the formation of the pseudodimeric scaffold. ClbL couples α-aminoketone and β-ketothioester intermediates attached to separate carrier domains on the NRPS-PKS assembly. Here, the 1.9 Å resolution structure of ClbL is reported, providing a structural basis for this key step in the colibactin biosynthetic pathway. The structure reveals an open hydrophobic active site surrounded by flexible loops, and comparison with homologous amidases supports its unusual function and predicts macromolecular interactions with pathway carrier-protein substrates. Modeling protein-protein interactions supports a predicted molecular basis for enzyme-carrier domain interactions. Overall, the work provides structural insight into this unique enzyme that is central to the biosynthesis of colibactin.

Due to the structural complexity of proteins, their corresponding crystal arrangements generally contain a significant amount of solvent-occupied space. These areas allow a certain degree of intracrystalline protein flexibility and mobility of solutes. Therefore, knowledge of the geometry of solvent-filled channels and cavities is essential whenever the dynamics inside a crystal are of interest. Especially in soaking experiments for structure-based drug design, ligands must be able to traverse the crystal solvent channels and reach the corresponding binding pockets. Unsuccessful screenings are sometimes attributed to the geometry of the crystal packing, but the underlying causes are often difficult to understand. This work presents LifeSoaks, a novel tool for analyzing and visualizing solvent channels in protein crystals. LifeSoaks uses a Voronoi diagram-based periodic channel representation which can be efficiently computed. The size and location of channel bottlenecks, which might hinder molecular diffusion, can be directly derived from this representation. This work presents the calculated bottleneck radii for all crystal structures in the PDB and the analysis of a new, hand-curated data set of structures obtained by soaking experiments. The results indicate that the consideration of bottleneck radii and the visual inspection of channels are beneficial for planning soaking experiments.

The increasing number of people dying from tuberculosis and the existence of extensively drug-resistant strains has led to an urgent need for new antituberculotic drugs with alternative modes of action. As part of the thioredoxin system, thioredoxin reductase (TrxR) is essential for the survival of Mycobacterium tuberculosis (Mtb) and shows substantial differences from human TrxR, making it a promising and most likely selective target. As a model organism for Mtb, crystals of Mycobacterium smegmatis TrxR that diffracted to high resolution were used in crystallographic fragment screening to discover binding fragments and new binding sites. The application of the 96 structurally diverse fragments from the F2X-Entry Screen revealed 56 new starting points for fragment-based drug design of new TrxR inhibitors. Over 200 crystal structures were analyzed using FragMAXapp, which includes processing and refinement by largely automated software pipelines and hit identification via the multi-data-set analysis approach PanDDA. The fragments are bound to 11 binding sites, of which four are positioned at binding pockets or important interaction sites and therefore show high potential for possible inhibition of TrxR.

The Protein Data Bank (PDB) is the single global archive of atomic-level, three-dimensional structures of biological macromolecules experimentally determined by macromolecular crystallography, nuclear magnetic resonance spectroscopy or three-dimensional cryo-electron microscopy. The PDB is growing continuously, with a recent rapid increase in new structure depositions from Asia. In 2022, the Worldwide Protein Data Bank (wwPDB; https://www.wwpdb.org/) partners welcomed Protein Data Bank China (PDBc; https://www.pdbc.org.cn) to the organization as an Associate Member. PDBc is based in the National Facility for Protein Science in Shanghai which is associated with the Shanghai Advanced Research Institute of Chinese Academy of Sciences, the Shanghai Institute for Advanced Immunochemical Studies and the iHuman Institute of ShanghaiTech University. This letter describes the history of the wwPDB, recently established mechanisms for adding new wwPDB data centers and the processes developed to bring PDBc into the partnership.

X-ray diffraction enables the routine determination of the atomic structure of materials. Key to its success are data-processing algorithms that allow experimenters to determine the electron density of a sample from its diffraction pattern. Scaling, the estimation and correction of systematic errors in diffraction intensities, is an essential step in this process. These errors arise from sample heterogeneity, radiation damage, instrument limitations and other aspects of the experiment. New X-ray sources and sample-delivery methods, along with new experiments focused on changes in structure as a function of perturbations, have led to new demands on scaling algorithms. Classically, scaling algorithms use least-squares optimization to fit a model of common error sources to the observed diffraction intensities to force these intensities onto the same empirical scale. Recently, an alternative approach has been demonstrated which uses a Bayesian optimization method, variational inference, to simultaneously infer merged data along with corrections, or scale factors, for the systematic errors. Owing to its flexibility, this approach proves to be advantageous in certain scenarios. This perspective briefly reviews the history of scaling algorithms and contrasts them with variational inference. Finally, appropriate use cases are identified for the first such algorithm, Careless, guidance is offered on its use and some speculations are made about future variational scaling methods.