Paper ID: 2312.02235

GenEM: Physics-Informed Generative Cryo-Electron Microscopy

Jiakai Zhang, Qihe Chen, Yan Zeng, Wenyuan Gao, Xuming He, Zhijie Liu, Jingyi Yu

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, AI models for particle picking and pose estimation have been adopted. However, their performance is still limited as they lack high-quality annotated datasets. To address this, we introduce physics-informed generative cryo-electron microscopy (GenEM), which for the first time integrates physical-based cryo-EM simulation with a generative unpaired noise translation to generate physically correct synthetic cryo-EM datasets with realistic noises. Initially, GenEM 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 GenEM is capable of generating realistic cryo-EM images. The generated dataset can further enhance particle picking and pose estimation models, eventually improving the reconstruction resolution. We will release our code and annotated synthetic datasets.

Submitted: Dec 4, 2023