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DiffraGAN: a conditional generative adversarial network for phasing single molecule diffraction data to atomic resolution
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DOI：10.3389/fmolb.2024.1386963.png)
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Abstract
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Introduction Proteins that adopt multiple conformations pose significant challenges in structural biology research and pharmaceutical development, as structure determination via single particle cryo-electron microscopy (cryo-EM) is often impeded by data heterogeneity. In this context, the enhanced signal-to-noise ratio of single molecule cryo-electron diffraction (simED) offers a promising alternative. However, a significant challenge in diffraction methods is the loss of phase information, which is crucial for accurate structure determination.Methods Here, we present DiffraGAN, a conditional generative adversarial network (cGAN) that estimates the missing phases at high resolution from a combination of single particle high-resolution diffraction data and low-resolution image data.Results For simulated datasets, DiffraGAN allows effectively determining protein structures at atomic resolution from diffraction patterns and noisy low-resolution images.Discussion Our findings suggest that combining single particle cryo-electron diffraction with advanced generative modeling, as in DiffraGAN, could revolutionize the way protein structures are determined, offering an alternative and complementary approach to existing methods.
Keywords:
diffraction
cryo-EM
deep learning
generative adversarial network
simED
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