Accurate cryo-EM protein particle picking by integrating the foundational AI image segmentation model and specialized U-Net.

Cryo-electron microscopy (cryo-EM) has revolutionized the field of structural biology by enabling the precise determination of large protein structures. Picking protein particles in cryo-EM micrographs (images) is a crucial step in the cryo-EM-based structure determination. However, existing methods trained on a limited amount of cryo-EM data still cannot accurately pick protein particles from complex, noisy, and heterogenous cryo-EM images. The general foundational artificial intelligence (AI)-based image segmentation model such as the Segment Anything Model (SAM) trained on huge amounts of general image data cannot segment protein particles well because their training data do not include cryo-EM images. In this work, we present a novel approach (CryoSegNet) of integrating the power of the encoder and decoder-based architecture of an attention-gated U-shape network (U-Net) specially designed and trained for cryo-EM particle picking and the SAM. The U-Net is first trained on a large cryo-EM image dataset and then used to generate input from original cryo-EM images for SAM to make particle pickings. CryoSegNet shows both high precision and recall in segmenting protein particles from cryo-EM micrographs, irrespective of protein type, shape, and size. On several independent datasets of various protein types, CryoSegNet outperforms two top machine learning particle pickers crYOLO and Topaz as well as SAM itself. The average resolution of density maps reconstructed from the particles picked by CryoSegNet is 3.05 Å, 15% better than 3.60 Å of Topaz and 49% better than 5.96 Å of crYOLO. Therefore, CryoSegNet can be applied to enhance the resolution of protein structures constructed from both existing and new cryo-EM data.