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.
Project description:The Ca2+-activated K+ channel, Slo1, has an unusually large conductance and contains a voltage sensor and multiple chemical sensors. Dual activation by membrane voltage and Ca2+ renders Slo1 central to processes that couple electrical signalling to Ca2+-mediated events such as muscle contraction and neuronal excitability. Here we present the cryo-electron microscopy structure of a full-length Slo1 channel from Aplysia californica in the presence of Ca2+ and Mg2+ at a resolution of 3.5?Å. The channel adopts an open conformation. Its voltage-sensor domain adopts a non-domain-swapped attachment to the pore and contacts the cytoplasmic Ca2+-binding domain from a neighbouring subunit. Unique structural features of the Slo1 voltage sensor suggest that it undergoes different conformational changes than other known voltage sensors. The structure reveals the molecular details of three distinct divalent cation-binding sites identified through electrophysiological studies of mutant Slo1 channels.
Project description:The light-harvesting-reaction center complex (LH1-RC) from the purple phototrophic bacterium Thiorhodovibrio strain 970 exhibits an LH1 absorption maximum at 960?nm, the most red-shifted absorption for any bacteriochlorophyll (BChl) a-containing species. Here we present a cryo-EM structure of the strain 970 LH1-RC complex at 2.82?Å resolution. The LH1 forms a closed ring structure composed of sixteen pairs of the ??-polypeptides. Sixteen Ca ions are present in the LH1 C-terminal domain and are coordinated by residues from the ??-polypeptides that are hydrogen-bonded to BChl a. The Ca2+-facilitated hydrogen-bonding network forms the structural basis of the unusual LH1 redshift. The structure also revealed the arrangement of multiple forms of ?- and ?-polypeptides in an individual LH1 ring. Such organization indicates a mechanism of interplay between the expression and assembly of the LH1 complex that is regulated through interactions with the RC subunits inside.