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.