Cryo-EM micrographs of engineered encapsulin P3P4
Ontology highlight
ABSTRACT:
SUBMITTER: Tadej Satler
PROVIDER: EMPIAR-11898 | biostudies-other |
REPOSITORIES: biostudies-other
Similar Datasets
Project description:Encapsulins containing dye-decolorizing peroxidase (DyP)-type peroxidases are ubiquitous among prokaryotes, protecting cells against oxidative stress. However, little is known about how they interact and function. Here, we have isolated a native cargo-packaging encapsulin from Mycobacterium smegmatis and determined its complete high-resolution structure by cryogenic electron microscopy (cryo-EM). This encapsulin comprises an icosahedral shell and a dodecameric DyP cargo. The dodecameric DyP consists of two hexamers with a twofold axis of symmetry and stretches across the interior of the encapsulin. Our results reveal that the encapsulin shell plays a role in stabilizing the dodecameric DyP. Furthermore, we have proposed a potential mechanism for removing the hydrogen peroxide based on the structural features. Our study also suggests that the DyP is the primary cargo protein of mycobacterial encapsulins and is a potential target for antituberculosis drug discovery.
| S-EPMC8072242 | biostudies-literature
Project description:Encapsulin is a class of nanocompartments that is unique in bacteria and archaea to confine enzymatic activities and sequester toxic reaction products. Here we present a 2.87 Å resolution cryo-EM structure of Thermotoga maritima encapsulin with heterologous protein complex loaded. It is the first successful case of expressing encapsulin and heterologous cargo protein in the insect cell system. Although we failed to reconstruct the cargo protein complex structure due to the signal interference of the capsid shell, we were able to observe some unique features of the cargo-loaded encapsulin shell, for example, an extra density at the fivefold pore that has not been reported before. These results would lead to a more complete understanding of the encapsulin cargo assembly process of T. maritima.
| S-EPMC7565527 | biostudies-literature
Project description:Automatic single particle picking is a critical step in the data processing pipeline of cryo-electron microscopy structure reconstruction. In recent years, several deep learning-based algorithms have been developed, demonstrating their potential to solve this challenge. However, current methods highly depend on manually labeled training data, which is labor-intensive and prone to biases especially for high-noise and low-contrast micrographs, resulting in suboptimal precision and recall. To address these problems, we propose UPicker, a semi-supervised transformer-based particle-picking method with a two-stage training process: unsupervised pretraining and supervised fine-tuning. During the unsupervised pretraining, an Adaptive Laplacian of Gaussian region proposal generator is proposed to obtain pseudo-labels from unlabeled data for initial feature learning. For the supervised fine-tuning, UPicker only needs a small amount of labeled data to achieve high accuracy in particle picking. To further enhance model performance, UPicker employs a contrastive denoising training strategy to reduce redundant detections and accelerate convergence, along with a hybrid data augmentation strategy to deal with limited labeled data. Comprehensive experiments on both simulated and experimental datasets demonstrate that UPicker outperforms state-of-the-art particle-picking methods in terms of accuracy and robustness while requiring fewer labeled data than other transformer-based models. Furthermore, ablation studies demonstrate the effectiveness and necessity of each component of UPicker. The source code and data are available at https://github.com/JachyLikeCoding/UPicker.
| S-EPMC11631311 | biostudies-literature
Project description:Cryo-electron microscopy (cryo-EM) is a powerful technique for determining the structures of large protein complexes. Picking single protein particles from cryo-EM micrographs (images) is a crucial step in reconstructing protein structures from them. However, the widely used template-based particle picking process requires some manual particle picking and is labor-intensive and time-consuming. Though machine learning and artificial intelligence (AI) can potentially automate particle picking, the current AI methods pick particles with low precision or low recall. The erroneously picked particles can severely reduce the quality of reconstructed protein structures, especially for the micrographs with low signal-to-noise (SNR) ratios. To address these shortcomings, we devised CryoTransformer based on transformers, residual networks, and image processing techniques to accurately pick protein particles from cryo-EM micrographs. CryoTransformer was trained and tested on the largest labelled cryo-EM protein particle dataset - CryoPPP. It outperforms the current state-of-the-art machine learning methods of particle picking in terms of the resolution of 3D density maps reconstructed from the picked particles as well as F1-score and is poised to facilitate the automation of the cryo-EM protein particle picking.
| S-EPMC10634673 | biostudies-literature
Project description:Prokaryotes rely on proteinaceous compartments such as encapsulin to isolate harmful reactions. Encapsulin are widely expressed by bacteria, including the Mycobacteriaceae, which include the human pathogens Mycobacterium tuberculosis and Mycobacterium leprae. Structures of fully assembled encapsulin shells have been determined for several species, but encapsulin assembly and cargo encapsulation are still poorly characterised, because of the absence of encapsulin structures in intermediate assembly states. We combine in situ and in vitro structural electron microscopy to show that encapsulins are dynamic assemblies with intermediate states of cargo encapsulation and shell assembly. Using cryo-focused ion beam (FIB) lamella preparation and cryo-electron tomography (CET), we directly visualise encapsulins in Mycobacterium marinum, and observed ribbon-like attachments to the shell, encapsulin shells with and without cargoes, and encapsulin shells in partially assembled states. In vitro cryo-electron microscopy (EM) single-particle analysis of the Mycobacterium tuberculosis encapsulin was used to obtain three structures of the encapsulin shell in intermediate states, as well as a 2.3 Å structure of the fully assembled shell. Based on the analysis of the intermediate encapsulin shell structures, we propose a model of encapsulin self-assembly via the pairwise addition of monomers.
| S-EPMC11830004 | biostudies-literature
Project description: Not available
| EMPIAR-10674 | biostudies-other
Project description: Not available
| EMPIAR-10798 | biostudies-other
Project description: Not available
| EMPIAR-11619 | biostudies-other
Project description: Not available
| EMPIAR-11625 | biostudies-other
Project description: Not available
| EMPIAR-11675 | biostudies-other