Journal of structural biology最新文献

In humans, the growth pattern of the acellular extrinsic fibre cementum (AEFC) has been useful to estimate the age-at-death. However, the structural organization behind such a pattern remains poorly understood. In this study tooth cementum from seven individuals from a Mexican modern skeletal series were analyzed with the aim of unveiling the AEFC collagenous and mineral structure using multimodal imaging approaches. The organization of collagen fibres was first determined using: light microscopy, transmission electron microscopy (TEM), electron tomography, and plasma FIB scanning electron microscopy (PFIB-SEM) tomography. The mineral properties were then investigated using: synchrotron small-angle X-ray scattering (SAXS) for T-parameter (correlation length between mineral particles); synchrotron X-ray diffraction (XRD) for L-parameter (mineral crystalline domain size estimation), alignment parameter (crystals preferred orientation) and lattice parameters a and c; as well as synchrotron X-ray fluorescence for spatial distribution of calcium, phosphorus and zinc. Results show that Sharpey’s fibres branched out fibres that cover and uncover other collagen bundles forming aligned arched structures that are joined by these same fibres but in a parallel fashion. The parallel fibres are not set as a continuum on the same plane and when they are superimposed project the AEFC incremental lines due to the collagen birefringence. The orientation of the apatite crystallites is subject to the arrangement of the collagen fibres, and the obtained parameter values along with the elemental distribution maps, revealed this mineral tissue as relatively homogeneous. Therefore, no intrinsic characteristics of the mineral phase could be associated with the alternating AEFC incremental pattern.

While protein activity is traditionally studied with a major focus on the active site, the activity of enzymes has been hypothesized to be linked to the flexibility of adjacent regions, warranting more exploration into how the dynamics in these regions affects catalytic turnover. One such enzyme is Xylanase A (XylA), which cleaves hemicellulose xylan polymers by hydrolysis at internal β-1,4-xylosidic linkages. It contains a “thumb” region whose flexibility has been suggested to affect the activity. The double mutation D11F/R122D was previously found to affect activity and potentially bias the thumb region to a more open conformation. We find that the D11F/R122D double mutation shows substrate-dependent effects, increasing activity on the non-native substrate ONPX2 but decreasing activity on its native xylan substrate. To characterize how the double mutant causes these kinetics changes, nuclear magnetic resonance (NMR) and molecular dynamics (MD) simulations were used to probe structural and flexibility changes. NMR chemical shift perturbations revealed structural changes in the double mutant relative to the wild-type, specifically in the thumb and fingers regions. Increased slow-timescale dynamics in the fingers region was observed as intermediate-exchange line broadening. Lipari-Szabo order parameters show negligible changes in flexibility in the thumb region in the presence of the double mutation. To help understand if there is increased energetic accessibility to the open state upon mutation, alchemical free energy simulations were employed that indicated thumb opening is more favorable in the double mutant. These studies aid in further characterizing how flexibility in adjacent regions affects the function of XylA.

Cryo-electron microscopy has become a powerful tool to determine three-dimensional (3D) structures of rigid biological macromolecules from noisy micrographs with single-particle reconstruction. Recently, deep neural networks, e.g., CryoDRGN, have demonstrated conformational and compositional heterogeneity of complexes. However, the lack of ground-truth conformations poses a challenge to assess the performance of heterogeneity analysis methods. In this work, variational autoencoders (VAE) with three types of deep generative priors were learned for latent variable inference and heterogeneous 3D reconstruction via Bayesian inference. More specifically, VAEs with “Variational Mixture of Posteriors” priors (VampPrior-SPR), non-parametric exemplar-based priors (ExemplarPrior-SPR) and priors from latent score-based generative models (LSGM-SPR) were quantitatively compared with CryoDRGN. We built four simulated datasets composed of hypothetical continuous conformation or discrete states of the hERG K + channel. Empirical and quantitative comparisons of inferred latent representations were performed with affine-transformation-based metrics. These models with more informative priors gave better regularized, interpretable factorized latent representations with better conserved pairwise distances, less deformed latent distributions and lower within-cluster variances. They were also tested on experimental datasets to resolve compositional and conformational heterogeneity (50S ribosome assembly, cowpea chlorotic mottle virus, and pre-catalytic spliceosome) with comparable high resolution. Codes and data are available: https://github.com/benjamin3344/DGP-SPR.

Calcium carbonate is present in many biominerals, including in the exoskeletons of crustaceans and shells of mollusks. High Mg-containing calcium carbonate was synthesized by high temperatures, high pressures or high molecular organic matter. For example, biogenic high Mg-containing calcite is synthesized under strictly controlled Mg concentration at ambient temperature and pressure. The spines of sea urchins consist of calcite, which contain a high percentage of magnesium. In this study, we investigated the factors that increase the magnesium content in calcite from the spines of the sea urchin, Heliocidaris crassispina. X-ray diffraction and inductively coupled plasma mass spectrometry analyses showed that sea urchin spines contain about 4.8% Mg. The organic matrix extracted from the H. crassispina spines induced the crystallization of amorphous phase and synthesis of magnesium-containing calcite, while amorphous was synthesized without SUE (sea urchin extract). In addition, aragonite was synthesized by SUE treated with protease-K. HC tropomyosin was specifically incorporated into Mg precipitates. Recombinant HC-tropomyosin induced calcite contained 0.1–2.5% Mg synthesis. Western blotting of sea urchin spine extracts confirmed that HC tropomyosin was present in the purple sea urchin spines at a protein weight ratio of 1.5%. These results show that HC tropomyosin is one factor that increases the magnesium concentration in the calcite of H. crassispina spines.

Efficient and high-accuracy filtering of cryo-electron microscopy (cryo-EM) micrographs is an emerging challenge with the growing speed of data collection and sizes of datasets. Convolutional neural networks (CNNs) are machine learning models that have been proven successful in many computer vision tasks, and have been previously applied to cryo-EM micrograph filtering. In this work, we demonstrate that two strategies, fine-tuning models from pretrained weights and including the power spectrum of micrographs as input, can greatly improve the attainable prediction accuracy of CNN models. The resulting software package, Miffi, is open-source and freely available for public use (https://github.com/ando-lab/miffi).

TetR family regulators (TFRs) represent a large group of one-component bacterial signal transduction systems which recognize environmental signals, like the presence of antibiotics or other bactericidal compounds, and trigger the cell response by regulating the expression of genes that secure bacterial survival in harsh environmental conditions. TFRs act as homodimers, each protomer is composed of a conserved DNA-binding N-terminal domain (NTD) and a variable ligand-binding C-terminal domain (CTD). Currently, there are about 500 structures of TFRs available in the Protein Data Bank and one-fourth of them represent the structures of TFR-ligand complexes. In this review, we summarized information on the ligands interacting with TFRs and based on structural data, we compared the CTDs of the TFR family members, as well as their ligand-binding cavities. Additionally, we divided the whole TFR family, including more than half of a million sequences, into subfamilies according to calculated multiple sequence alignment and phylogenetic tree. We also highlighted structural elements characteristic of some of the subfamilies. The presented comprehensive overview of the TFR CTDs provides good bases and future directions for further studies on TFRs that are not only important targets for battling multidrug resistance but also good candidates for many biotechnological approaches, like TFR-based biosensors.

CRISPR-Cas system is an RNA-guided adaptive immune system widespread in bacteria and archaea. Among them, type III CRISPR-Cas systems are the most ancient throughout the CRISPR-Cas family, proving anti-phage defense through a crRNA-guided RNA targeting manner and possessing multiple enzymatic activities. Type III CRISPR-Cas systems comprise four typical members (type III-A to III-D) and two atypical members (type III-E and type III-F), providing immune defense through distinct mechanisms. Here, we delve into structural studies conducted on three well-characterized members: the type III-A, III-B, and III-E systems, provide an overview of the structural insights into the crRNA-guided target RNA cleavage, self/non-self discrimination, and the target RNA-dependent regulation of enzymatic subunits in the effector complex.

Cellular cryo-electron tomography (cryo-ET) has emerged as a key method to unravel the spatial and structural complexity of cells in their near-native state at unprecedented molecular resolution. To enable quantitative analysis of the complex shapes and morphologies of lipid membranes, the noisy three-dimensional (3D) volumes must be segmented. Despite recent advances, this task often requires considerable user intervention to curate the resulting segmentations. Here, we present ColabSeg, a Python-based tool for processing, visualizing, editing, and fitting membrane segmentations from cryo-ET data for downstream analysis. ColabSeg makes many well-established algorithms for point-cloud processing easily available to the broad community of structural biologists for applications in cryo-ET through its graphical user interface (GUI). We demonstrate the usefulness of the tool with a range of use cases and biological examples. Finally, for a large Mycoplasma pneumoniae dataset of 50 tomograms, we show how ColabSeg enables high-throughput membrane segmentation, which can be used as valuable training data for fully automated convolutional neural network (CNN)-based segmentation.