Acta Crystallographica. Section D, Structural Biology最新文献

Nanobodies (VHHs) are single-domain antibodies with three antigenic CDR regions and are used in diverse scientific applications. Here, an ∼14 kDa nanobody (A5) specific for the endonuclease VIII (Nei)-like 1 or NEIL1 DNA glycosylase involved in the first step of the base-excision repair pathway was crystallized and its structure was determined to 2.1 Å resolution. The crystals posed challenges due to potential twinning and anisotropic diffraction. Despite inconclusive twinning indicators, reprocessing in an orthorhombic setting and molecular replacement in space group P2₁2₁2 enabled the successful modeling of 96% of residues in the asymmetric unit, with final R_work and R_free values of 0.199 and 0.229, respectively.

The Guest Editors provide an introduction to the special issue of articles based on talks at the CCP4 Study Weekend 2022, which is available at https://journals.iucr.org/special_issues/2023/CCP42022/.

X-ray imaging of virus particles at the European XFEL could eventually allow their complete structures to be solved, potentially approaching the resolution of other structural virology methods. To achieve this ambitious goal with today's technologies, about 1 ml of purified virus suspension containing at least 10¹² particles per millilitre is required. Such large amounts of concentrated suspension have never before been obtained for enveloped viruses. Tick-borne encephalitis virus (TBEV) represents an attractive model system for the development of enveloped virus purification and concentration protocols, given the availability of large amounts of inactivated virus material provided by vaccine-manufacturing facilities. Here, the development of a TBEV vaccine purification and concentration scheme is presented combined with a quality-control protocol that allows substantial amounts of highly concentrated non-aggregated suspension to be obtained. Preliminary single-particle imaging experiments were performed for this sample at the European XFEL, showing distinct diffraction patterns.

The use of artificial intelligence to process diffraction images is challenged by the need to assemble large and precisely designed training data sets. To address this, a codebase called Resonet was developed for synthesizing diffraction data and training residual neural networks on these data. Here, two per-pattern capabilities of Resonet are demonstrated: (i) interpretation of crystal resolution and (ii) identification of overlapping lattices. Resonet was tested across a compilation of diffraction images from synchrotron experiments and X-ray free-electron laser experiments. Crucially, these models readily execute on graphics processing units and can thus significantly outperform conventional algorithms. While Resonet is currently utilized to provide real-time feedback for macromolecular crystallography users at the Stanford Synchrotron Radiation Lightsource, its simple Python-based interface makes it easy to embed in other processing frameworks. This work highlights the utility of physics-based simulation for training deep neural networks and lays the groundwork for the development of additional models to enhance diffraction collection and analysis.

Hydrogen (H) atoms are abundant in macromolecules and often play critical roles in enzyme catalysis, ligand-recognition processes and protein-protein interactions. However, their direct visualization by diffraction techniques is challenging. Macromolecular X-ray crystallography affords the localization of only the most ordered H atoms at (sub-)atomic resolution (around 1.2 Å or higher). However, many H atoms of biochemical significance remain undetectable by this method. In contrast, neutron diffraction methods enable the visualization of most H atoms, typically in the form of deuterium (²H) atoms, at much more common resolution values (better than 2.5 Å). Thus, neutron crystallography, although technically demanding, is often the method of choice when direct information on protonation states is sought. REFMAC5 from the Collaborative Computational Project No. 4 (CCP4) is a program for the refinement of macromolecular models against X-ray crystallographic and cryo-EM data. This contribution describes its extension to include the refinement of structural models obtained from neutron crystallographic data. Stereochemical restraints with accurate bond distances between H atoms and their parent atom nuclei are now part of the CCP4 Monomer Library, the source of prior chemical information used in the refinement. One new feature for neutron data analysis in REFMAC5 is refinement of the protium/deuterium (¹H/²H) fraction. This parameter describes the relative ¹H/²H contribution to neutron scattering for hydrogen isotopes. The newly developed REFMAC5 algorithms were tested by performing the (re-)refinement of several entries available in the PDB and of one novel structure (FutA) using either (i) neutron data only or (ii) neutron data supplemented by external restraints to a reference X-ray crystallographic structure. Re-refinement with REFMAC5 afforded models characterized by R-factor values that are consistent with, and in some cases better than, the originally deposited values. The use of external reference structure restraints during refinement has been observed to be a valuable strategy, especially for structures at medium-low resolution.

In structural biology, atomic displacement parameters, commonly used in the form of B values, describe uncertainties in atomic positions. Their distribution over the structure can provide hints on local structural reliability and mobility. A spatial macromolecular model can be represented by a graph whose nodes are atoms and whose edges correspond to all interatomic contacts within a certain distance. Small connected subgraphs, called graphlets, provide information about the wiring of a particular atom. The multiple linear regression approach based on this information aims to predict a distribution of values of isotropic atomic displacement parameters (B values) within a protein structure, given the atomic coordinates and molecular packing. By modeling the dynamic component of atomic uncertainties, this method allows the B values obtained from experimental crystallographic or cryo-electron microscopy studies to be reproduced relatively well.

Cyanase plays a vital role in the detoxification of cyanate and supplies a continuous nitrogen source for soil microbes by converting cyanate to ammonia and carbon dioxide in a bicarbonate-dependent reaction. The structures of cyanase complexed with dianion inhibitors, in conjunction with biochemical studies, suggest putative binding sites for substrates. However, the substrate-recognition and reaction mechanisms of cyanase remain unclear. Here, crystal structures of cyanase from Escherichia coli were determined in the native form and in complexes with cyanate, bicarbonate and intermediates at 1.5-1.9 Å resolution using synchrotron X-rays and an X-ray free-electron laser. Cyanate and bicarbonate interact with the highly conserved Arg96, Ser122 and Ala123 in the active site. In the presence of a mixture of cyanate and bicarbonate, three different electron densities for intermediates were observed in the cyanase structures. Moreover, the observed electron density could explain the dynamics of the substrate or product. In addition to conformational changes in the substrate-binding pocket, dynamic movement of Leu151 was observed, which functions as a gate for the passage of substrates or products. These findings provide a structural mechanism for the substrate-binding and reaction process of cyanase.

Model building and refinement, and the validation of their correctness, are very effective and reliable at local resolutions better than about 2.5 Å for both crystallography and cryo-EM. However, at local resolutions worse than 2.5 Å both the procedures and their validation break down and do not ensure reliably correct models. This is because in the broad density at lower resolution, critical features such as protein backbone carbonyl O atoms are not just less accurate but are not seen at all, and so peptide orientations are frequently wrongly fitted by 90-180°. This puts both backbone and side chains into the wrong local energy minimum, and they are then worsened rather than improved by further refinement into a valid but incorrect rotamer or Ramachandran region. On the positive side, new tools are being developed to locate this type of pernicious error in PDB depositions, such as CaBLAM, EMRinger, Pperp diagnosis of ribose puckers, and peptide flips in PDB-REDO, while interactive modeling in Coot or ISOLDE can help to fix many of them. Another positive trend is that artificial intelligence predictions such as those made by AlphaFold2 contribute additional evidence from large multiple sequence alignments, and in high-confidence parts they provide quite good starting models for loops, termini or whole domains with otherwise ambiguous density.

Neutron diffraction is one of the three crystallographic techniques (X-ray, neutron and electron diffraction) used to determine the atomic structures of molecules. Its particular strengths derive from the fact that H (and D) atoms are strong neutron scatterers, meaning that their positions, and thus protonation states, can be derived from crystallographic maps. However, because of technical limitations and experimental obstacles, the quality of neutron diffraction data is typically much poorer (completeness, resolution and signal to noise) than that of X-ray diffraction data for the same sample. Further, refinement is more complex as it usually requires additional parameters to describe the H (and D) atoms. The increase in the number of parameters may be mitigated by using the `riding hydrogen' refinement strategy, in which the positions of H atoms without a rotational degree of freedom are inferred from their neighboring heavy atoms. However, this does not address the issues related to poor data quality. Therefore, neutron structure determination often relies on the presence of an X-ray data set for joint X-ray and neutron (XN) refinement. In this approach, the X-ray data serve to compensate for the deficiencies of the neutron diffraction data by refining one model simultaneously against the X-ray and neutron data sets. To be applicable, it is assumed that both data sets are highly isomorphous, and preferably collected from the same crystals and at the same temperature. However, the approach has a number of limitations that are discussed in this work by comparing four separately re-refined neutron models. To address the limitations, a new method for joint XN refinement is introduced that optimizes two different models against the different data sets. This approach is tested using neutron models and data deposited in the Protein Data Bank. The efficacy of refining models with H atoms as riding or as individual atoms is also investigated.

Fucoidanases (EC 3.2.1.-) catalyze the hydrolysis of glycosidic bonds between fucose residues in fucoidans. Fucoidans are a compositionally and structurally diverse class of fucose-containing sulfated polysaccharides that are primarily found in brown seaweeds. Here, the structural characterization of a novel endo-α(1,4)-fucoidanase, Mef1, from the marine bacterium Muricauda eckloniae is presented, showing sequence similarity to members of glycoside hydrolase family 107. Using carbohydrate polyacrylamide gel electrophoresis and nuclear magnetic resonance analyses, it is shown that the fucoidanase Mef1 catalyzes the cleavage of α(1,4)-linkages between fucose residues sulfated on C2 in the structure [-3)-α-L-Fucp2S-(1,4)-α-L-Fucp2S-(1-]_n in fucoidan from Fucus evanescens. Kinetic analysis of Mef1 activity by Fourier transform infrared spectroscopy revealed that the specific Mef1 fucoidanase activity (U_f) on F. evanescens fucoidan was 0.1 × 10^-3 U_f µM^-1. By crystal structure determination of Mef1 at 1.8 Å resolution, a single-domain organization comprising a (β/α)₈-barrel domain was determined. The active site was in an extended, positively charged groove that is likely to be designed to accommodate the binding of the negatively charged, sulfated fucoidan substrate. The active site of Mef1 comprises the amino acids His270 and Asp187, providing acid/base and nucleophile groups, respectively, for the hydrolysis of glycosidic bonds in the fucoidan backbone. Electron densities were identified for two possible Ca²⁺ ions in the enzyme, one of which is partially exposed to the active-site groove, while the other is very tightly coordinated. A water wire was discovered leading from the exterior of the Mef1 enzyme into the active site, passing the tightly coordinated Ca²⁺ site.