Cryo electron micrographs of 70S Escherichia coli ribosomes
Ontology highlight
ABSTRACT:
SUBMITTER: Felix Gersteuer
PROVIDER: EMPIAR-11758 | biostudies-other |
REPOSITORIES: biostudies-other
Similar Datasets
Project description:Escherichia coli 70S ribosomes tightly bind 8 equiv of Zn(II), and EXAFS spectra indicate that Zn(II) may be protein-bound. Ribosomes were incubated with EDTA and Zn(II), and after dialysis, the resulting ribosomes bound 5 and 11 equiv of Zn(II), respectively. EXAFS studies show that the additional Zn(II) in the zinc-supplemented ribosomes binds in part to the phosphate backbone of the ribosome. Lastly, in vitro translation studies demonstrate that EDTA-treated ribosomes do not synthesize an active Zn(II)-bound metalloenzyme, while the as-isolated ribosomes do. These studies demonstrate that the majority of intracellular Zn(II) resides in the ribosome.
| S-EPMC3220600 | biostudies-literature
Project description:Transcription and translation are coupled processes in bacteria. A role of transcription elongation cofactor NusG in coupling has been suggested by in vitro structural studies. NMR revealed association of the NusG carboxy-terminal domain with S10 (NusE), implying a direct role for NusG as a bridge linking RNAP and the lead ribosome. Here we present the first in vitro and in vivo evidence of full-length NusG association with mature 70S ribosomes. Binding did not require accessory factors in vitro. Mutating the NusG:S10 binding interface at NusG F165 or NusE M88 and D97 residues weakened NusG:S10 association in vivo and completely abolished it in vitro, supporting the specificity of this interaction. Mutations in the binding interface increased sensitivity to chloramphenicol. This phenotype was suppressed by rpoB*35, an RNAP mutation that reduces replisome-RNAP clashes. We propose that weakened NusG:S10 interaction leads to uncoupling when translation is inhibited, with resulting RNAP backtracking, replication blocks and formation of lethal DNA double-strand breaks.
| S-EPMC5980749 | biostudies-literature
Project description: Not available
| EMPIAR-10985 | biostudies-other
Project description: Not available
| EMPIAR-10316 | biostudies-other
Project description: Not available
| EMPIAR-11111 | biostudies-other
Project description:BackgroundIdentification and selection of protein particles in cryo-electron micrographs is an important step in single particle analysis. In this study, we developed a deep learning-based particle picking network to automatically detect particle centers from cryoEM micrographs. This is a challenging task due to the nature of cryoEM data, having low signal-to-noise ratios with variable particle sizes, shapes, distributions, grayscale variations as well as other undesirable artifacts.ResultsWe propose a double convolutional neural network (CNN) cascade for automated detection of particles in cryo-electron micrographs. This approach, entitled Deep Regression Picker Network or "DRPnet", is simple but very effective in recognizing different particle sizes, shapes, distributions and grayscale patterns corresponding to 2D views of 3D particles. Particles are detected by the first network, a fully convolutional regression network (FCRN), which maps the particle image to a continuous distance map that acts like a probability density function of particle centers. Particles identified by FCRN are further refined to reduce false particle detections by the second classification CNN. DRPnet's first CNN pretrained with only a single cryoEM dataset can be used to detect particles from different datasets without retraining. Compared to RELION template-based autopicking, DRPnet results in better particle picking performance with drastically reduced user interactions and processing time. DRPnet also outperforms the state-of-the-art particle picking networks in terms of the supervised detection evaluation metrics recall, precision, and F-measure. To further highlight quality of the picked particle sets, we compute and present additional performance metrics assessing the resulting 3D reconstructions such as number of 2D class averages, efficiency/angular coverage, Rosenthal-Henderson plots and local/global 3D reconstruction resolution.ConclusionDRPnet shows greatly improved time-savings to generate an initial particle dataset compared to manual picking, followed by template-based autopicking. Compared to other networks, DRPnet has equivalent or better performance. DRPnet excels on cryoEM datasets that have low contrast or clumped particles. Evaluating other performance metrics, DRPnet is useful for higher resolution 3D reconstructions with decreased particle numbers or unknown symmetry, detecting particles with better angular orientation coverage.
| S-EPMC7869254 | biostudies-literature
Project description:Cryo-electron microscopy is a popular method for the determination of protein structures; however, identifying a sufficient number of particles for analysis can take months of manual effort. Current computational approaches find many false positives and require ad hoc postprocessing, especially for unusually shaped particles. To address these shortcomings, we develop Topaz, an efficient and accurate particle-picking pipeline using neural networks trained with a general-purpose positive-unlabeled learning method. This framework enables particle detection models to be trained with few sparsely labeled particles and no labeled negatives. Topaz retrieves many more real particles than conventional picking methods while maintaining low false-positive rates, is capable of picking challenging unusually shaped proteins (for example, small, non-globular and asymmetric particles), produces more representative particle sets and does not require post hoc curation. We demonstrate the performance of Topaz on two difficult datasets and three conventional datasets. Topaz is modular, standalone, free and open source ( http://topaz.csail.mit.edu ).
| S-EPMC6858545 | biostudies-literature
Project description:Enterococcus faecalis is a gram-positive organism responsible for serious infections in humans, but as with many bacterial pathogens, resistance has rendered a number of commonly used antibiotics ineffective. Here, we report the cryo-EM structure of the E. faecalis 70S ribosome to a global resolution of 2.8 Å. Structural differences are clustered in peripheral and solvent exposed regions when compared with Escherichia coli, whereas functional centres, including antibiotic binding sites, are similar to other bacterial ribosomes. Comparison of intersubunit conformations among five classes obtained after three-dimensional classification identifies several rotated states. Large ribosomal subunit protein bL31, which forms intersubunit bridges to the small ribosomal subunit, assumes different conformations in the five classes, revealing how contacts to the small subunit are maintained throughout intersubunit rotation. A tRNA observed in one of the five classes is positioned in a chimeric pe/E position in a rotated ribosomal state. The 70S ribosome structure of E. faecalis now extends our knowledge of bacterial ribosome structures and may serve as a basis for the development of novel antibiotic compounds effective against this pathogen.
| S-EPMC7530986 | biostudies-literature
Project description:Pseudouridine (?) is present at conserved, functionally important regions in the ribosomal RNAs (rRNAs) from all three domains of life. Little, however, is known about the functions of ? modifications in bacterial ribosomes. An Escherichia coli strain has been constructed in which all seven rRNA ? synthases have been inactivated and whose ribosomes are devoid of all ?s. Surprisingly, this strain displays only minor defects in ribosome biogenesis and function, and cell growth is only modestly affected. This is in contrast to a strong requirement for ? in eukaryotic ribosomes and suggests divergent roles for rRNA ? modifications in these two domains.IMPORTANCE Pseudouridine (?) is the most abundant posttranscriptional modification in RNAs. In the ribosome, ? modifications are typically located at conserved, critical regions, suggesting they play an important functional role. In eukarya and archaea, rRNAs are modified by a single pseudouridine synthase (PUS) enzyme, targeted to rRNA via a snoRNA-dependent mechanism, while bacteria use multiple stand-alone PUS enzymes. Disruption of ? modification of rRNA in eukarya seriously impairs ribosome function and cell growth. We have constructed an E. coli multiple deletion strain lacking all ? modifications in rRNA. In contrast to the equivalent eukaryotic mutants, the E. coli strain is only modestly affected in growth, decoding, and ribosome biogenesis, indicating a differential requirement for ? modifications in these two domains.
| S-EPMC5786706 | biostudies-literature
Project description: Not available
| EMPIAR-10986 | biostudies-other