DeepTracer Diffusion: A Single‐Stage Diffusion Model for Accurate Cryo-EM Backbone Segmentation

Precise atomic-level interpretation of macromolecular structures is vital for understanding biological mechanisms yet remains challenging due to the complex nature of cryo-electron microscopy (cryo-EM) data. Existing approaches have utilized either multiple convolutional neural networks or complex combinations of autoencoder and latent diffusion models to predict atom locations via image segmentation. We introduce DeepTracer Diffusion, a novel framework that leverages a single Denoising Diffusion Probabilistic Model (DDPM) to perform image segmentation, providing higher accuracy in terms of F1 score and predicted residues for predicted backbone atoms.