Dataset Information

Cryo micrographs of microtubules (GDP state) decorated with NDC-NDC chimera of human doublecortin

ABSTRACT:

SUBMITTER: Joseph Atherton

PROVIDER: EMPIAR-10300 | biostudies-other |

REPOSITORIES: biostudies-other

ACCESS DATA

Publications

A microtubule RELION-based pipeline for cryo-EM image processing.

Cook Alexander D AD Manka Szymon W SW Wang Su S Moores Carolyn A CA Atherton Joseph J

Journal of structural biology 20191011 1

Microtubules are polar filaments built from αβ-tubulin heterodimers that exhibit a range of architectures in vitro and in vivo. Tubulin heterodimers are arranged helically in the microtubule wall but many physiologically relevant architectures exhibit a break in helical symmetry known as the seam. Noisy 2D cryo-electron microscopy projection images of pseudo-helical microtubules therefore depict distinct but highly similar views owing to the high structural similarity of α- and β-tubulin. The de ...[more]

PMID: 31610239

Similar Datasets

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.

Dataset Information

Cryo micrographs of microtubules (GDP state) decorated with NDC-NDC chimera of human doublecortin

Publications

A microtubule RELION-based pipeline for cryo-EM image processing.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure