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.
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.
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.