Acta Crystallographica. Section D, Structural Biology最新文献

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.

In late 2020, the results of CASP14, the 14th event in a series of competitions to assess the latest developments in computational protein structure-prediction methodology, revealed the giant leap forward that had been made by Google's Deepmind in tackling the prediction problem. The level of accuracy in their predictions was the first instance of a competitor achieving a global distance test score of better than 90 across all categories of difficulty. This achievement represents both a challenge and an opportunity for the field of experimental structural biology. For structure determination by macromolecular X-ray crystallography, access to highly accurate structure predictions is of great benefit, particularly when it comes to solving the phase problem. Here, details of new utilities and enhanced applications in the CCP4 suite, designed to allow users to exploit predicted models in determining macromolecular structures from X-ray diffraction data, are presented. The focus is mainly on applications that can be used to solve the phase problem through molecular replacement.

Siderophore-binding proteins from two thermophilic bacteria, Geobacillus stearothermophilus and Parageobacillus thermoglucosidasius, were identified from a search of sequence databases, cloned and overexpressed. They are homologues of the well characterized protein CjCeuE from Campylobacter jejuni. The iron-binding histidine and tyrosine residues are conserved in both thermophiles. Crystal structures were determined of the apo proteins and of their complexes with iron(III)-azotochelin and its analogue iron(III)-5-LICAM. The thermostability of both homologues was shown to be about 20°C higher than that of CjCeuE. Similarly, the tolerance of the homologues to the organic solvent dimethylformamide (DMF) was enhanced, as reflected by the respective binding constants for these ligands measured in aqueous buffer at pH 7.5 in the absence and presence of 10% and 20% DMF. Consequently, these thermophilic homologues offer advantages in the development of artificial metalloenzymes using the CeuE family.

Muramidases (also known as lysozymes) hydrolyse the peptidoglycan component of the bacterial cell wall and are found in many glycoside hydrolase (GH) families. Similar to other glycoside hydrolases, muramidases sometimes have noncatalytic domains that facilitate their interaction with the substrate. Here, the identification, characterization and X-ray structure of a novel fungal GH24 muramidase from Trichophaea saccata is first described, in which an SH3-like cell-wall-binding domain (CWBD) was identified by structure comparison in addition to its catalytic domain. Further, a complex between a triglycine peptide and the CWBD from T. saccata is presented that shows a possible anchor point of the peptidoglycan on the CWBD. A `domain-walking' approach, searching for other sequences with a domain of unknown function appended to the CWBD, was then used to identify a group of fungal muramidases that also contain homologous SH3-like cell-wall-binding modules, the catalytic domains of which define a new GH family. The properties of some representative members of this family are described as well as X-ray structures of the independent catalytic and SH3-like domains of the Kionochaeta sp., Thermothielavioides terrestris and Penicillium virgatum enzymes. This work confirms the power of the module-walking approach, extends the library of known GH families and adds a new noncatalytic module to the muramidase arsenal.

The spike protein (S) of SARS-CoV-2 is the major target of neutralizing antibodies and vaccines. Antibodies that target the receptor-binding domain (RBD) of S have high potency in preventing viral infection. The ongoing evolution of SARS-CoV-2, especially mutations occurring in the RBD of new variants, has severely challenged the development of neutralizing antibodies and vaccines. Here, a murine monoclonal antibody (mAb) designated E77 is reported which engages the prototype RBD with high affinity and potently neutralizes SARS-CoV-2 pseudoviruses. However, the capability of E77 to bind RBDs vanishes upon encountering variants of concern (VOCs) which carry the N501Y mutation, such as Alpha, Beta, Gamma and Omicron, in contrast to its performance with the Delta variant. To explain the discrepancy, cryo-electron microscopy was used to analyze the structure of an RBD-E77 Fab complex, which reveals that the binding site of E77 on RBD belongs to the RBD-1 epitope, which largely overlaps with the binding site of human angiotensin-converting enzyme 2 (hACE2). Both the heavy chain and the light chain of E77 interact extensively with RBD and contribute to the strong binding of RBD. E77 employs CDRL1 to engage Asn501 of RBD and the Asn-to-Tyr mutation could generate steric hindrance, abolishing the binding. In sum, the data provide the landscape for an in-depth understanding of immune escape of VOCs and rational antibody engineering against emerging variants of SARS-CoV-2.

Candida auris has emerged as a global health problem with a dramatic spread by nosocomial transmission and a high mortality rate. Antifungal therapy for C. auris infections is currently limited due to widespread resistance to fluconazole and amphotericin B and increasing resistance to the front-line drug echinocandin. Therefore, new treatments are urgently required to combat this pathogen. Dihydrofolate reductase (DHFR) has been validated as a potential drug target for Candida species, although no structure of the C. auris enzyme (CauDHFR) has been reported. Here, crystal structures of CauDHFR are reported as an apoenzyme, as a holoenzyme and in two ternary complexes with pyrimethamine and cycloguanil, which are common antifolates, at near-atomic resolution. Preliminary biochemical and biophysical assays and antifungal susceptibility testing with a variety of classical antifolates were also performed, highlighting the enzyme-inhibition rates and the inhibition of yeast growth. These structural and functional data might provide the basis for a novel drug-discovery campaign against this global threat.

The constant selection and propagation of multi-resistant Plasmodium sp. parasites require the identification of new antimalarial candidates involved in as-yet untargeted metabolic pathways. Subtilisin-like protease 1 (SUB1) belongs to a new generation of drug targets because it plays a crucial role during egress of the parasite from infected host cells at different stages of its life cycle. SUB1 is characterized by an unusual pro-region that tightly interacts with its cognate catalytic domain, thus precluding 3D structural analysis of enzyme-inhibitor complexes. In the present study, to overcome this limitation, stringent ionic conditions and controlled proteolysis of recombinant full-length P. vivax SUB1 were used to obtain crystals of an active and stable catalytic domain (PvS1_Cat) without a pro-region. High-resolution 3D structures of PvS1_Cat, alone and in complex with an α-ketoamide substrate-derived inhibitor (MAM-117), showed that, as expected, the catalytic serine of SUB1 formed a covalent bond with the α-keto group of the inhibitor. A network of hydrogen bonds and hydrophobic interactions stabilized the complex, including at the P1' and P2' positions of the inhibitor, although P' residues are usually less important in defining the substrate specificity of subtilisins. Moreover, when associated with a substrate-derived peptidomimetic inhibitor, the catalytic groove of SUB1 underwent significant structural changes, particularly in its S4 pocket. These findings pave the way for future strategies for the design of optimized SUB1-specific inhibitors that may define a novel class of antimalarial candidates.

Dynamic light scattering (DLS) is routinely employed to assess the homogeneity and size-distribution profile of samples containing microscopic particles in suspension or solubilized polymers. In this work, Raynals, user-friendly software for the analysis of single-angle DLS data that uses the Tikhonov-Phillips regularization, is introduced. Its performance is evaluated on simulated and experimental data generated by different DLS instruments for several proteins and gold nanoparticles. DLS data can easily be misinterpreted and the simulation tools available in Raynals allow the limitations of the measurement and its resolution to be understood. It was designed as a tool to address the quality control of biological samples during sample preparation and optimization and it helps in the detection of aggregates, showing the influence of large particles. Lastly, Raynals provides flexibility in the way that the data are presented, allows the export of publication-quality figures, is free for academic use and can be accessed online on the eSPC data-analysis platform at https://spc.embl-hamburg.de/.

The bromodomain and extra-terminal (BET) family proteins, which are involved in chromatin function, have been shown to be promising drug targets in several pathological conditions, including cancer and inflammation. There is considerable interest in the development of BET inhibitors with novel scaffolds to modulate the epigenesis of such diseases. Here, high-resolution crystal structures of the purine class of FDA-approved drugs (theophylline, doxophylline and acyclovir) and non-FDA-approved compounds (3-methyl-7-propylxanthine and theobromine) complexed with hBRD2 bromodomains BD1 and BD2 are reported. Remarkably, a new binding site is exhibited by stacking the compounds against the WPF shelf of BD1 and BD2. This serendipitous binding, in addition to the known acetyl-lysine binding site, sufficiently anchors the ligands in the solvent-exposed region. In addition, slight variations in the lipophilicity of these molecules significantly affected the in vitro binding affinity and selectivity towards BD1 compared with BD2. This idiosyncratic binding provides a new structural framework to link these sites for the development of next-generation inhibitors of the BET family.