Cryo electron micrographs of 70S Escherichia coli ribosomes
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ABSTRACT:
SUBMITTER: Felix Gersteuer
PROVIDER: EMPIAR-11758 | biostudies-other |
REPOSITORIES: biostudies-other
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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.
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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.
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