A novel particle picking approach for cryo-electron microscopy images

Cryo-electron microscopy (cryo-EM) has become a crucial tool for determining the structures of proteins and macromolecular complexes. However, the extremely low signal-to-noise ratio (SNR) and large image scale(approximately 4K) of electron microscopy images pose significant challenges in selecting hundreds of thousands of particles from these images for reconstructing high-resolution protein three-dimensional structures. Existing particle picking methods still struggle to meet the requirements of such research. We present a deep neural network model that amalgamates convolutional neural network (CNN) and graph convolutional networks (GCN). The model initially employs CNN for preliminary particle picking, modeling protein particles as the center points of protein particle bounding boxes and leveraging keypoint detection to detect as many particles as possible. Subsequently, the detected particle boxes are cropped from the original electron microscopy images and used as inputs for the GCN for further classification, aiming to distinguish between the background and protein particles to achieve higher precision. Experimental evaluations were conducted on various datasets, and the results indicate that it can acquire accurate results.