CryoGEM: Physics-Informed Generative Cryo-Electron Microscopy
This addresses a data bottleneck in cryo-EM for structural biology, offering an incremental improvement by generating realistic synthetic training data.
The paper tackles the lack of high-quality annotated datasets for cryo-EM by introducing CryoGEM, a method that integrates physics-based simulation with generative noise translation to generate synthetic cryo-EM images, which improves reconstruction resolution for tasks like particle picking and pose estimation.
In the past decade, deep conditional generative models have revolutionized the generation of realistic images, extending their application from entertainment to scientific domains. Single-particle cryo-electron microscopy (cryo-EM) is crucial in resolving near-atomic resolution 3D structures of proteins, such as the SARS- COV-2 spike protein. To achieve high-resolution reconstruction, a comprehensive data processing pipeline has been adopted. However, its performance is still limited as it lacks high-quality annotated datasets for training. To address this, we introduce physics-informed generative cryo-electron microscopy (CryoGEM), which for the first time integrates physics-based cryo-EM simulation with a generative unpaired noise translation to generate physically correct synthetic cryo-EM datasets with realistic noises. Initially, CryoGEM simulates the cryo-EM imaging process based on a virtual specimen. To generate realistic noises, we leverage an unpaired noise translation via contrastive learning with a novel mask-guided sampling scheme. Extensive experiments show that CryoGEM is capable of generating authentic cryo-EM images. The generated dataset can used as training data for particle picking and pose estimation models, eventually improving the reconstruction resolution.