CryoGEM: Physics-Informed Generative Cryo-Electron Microscopy

• URL: http://arxiv.org/abs/2312.02235v2
• Date: Sun, 29 Sep 2024 16:03:04 GMT
• Title: CryoGEM: Physics-Informed Generative Cryo-Electron Microscopy
• Authors: Jiakai Zhang, Qihe Chen, Yan Zeng, Wenyuan Gao, Xuming He, Zhijie Liu, Jingyi Yu,
• Abstract summary: We introduce physics-informed generative cryo-electron microscopy (CryoGEM) CryoGEM integrates physics-based cryo-EM simulation with a generative unpaired noise translation to generate realistic noises. Experiments show that CryoGEM is capable of generating authentic cryo-EM images.
• Score: 38.57626501108458
• License:
• Abstract: 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.

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