.png){kind=icon}
返回
DiffraGAN: a conditional generative adversarial network for phasing single molecule diffraction data to atomic resolution
.png){kind=icon}
OA
AI
DOI：10.3389/fmolb.2024.1386963.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
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.
Keyword:
diffraction
cryo-EM
deep learning
generative adversarial network
simED
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
4
论文数:
6.1K
被引数:
2.0W
机构
引用论文
.png){kind=icon}
分享收藏Distribution of heme oxygenase-2 and NADPH-diaphorase in the spinal trigeminal nucleus of the rat
PREAI
分享收藏Is SA shared or distributed in team work? An exploratory study in an intelligence analysis task
PREAI
分享收藏
查看更多内容