Deep Learning for Resolution Validation of Three Dimensional Cryo-Electron Microscopy Density Maps

Todor K. Avramov, Dong Si
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引用次数: 1

Abstract

Cryo-electron microscopy (cryo-EM) is becoming the imaging method of choice for determining protein structures. Many atomic structures have been resolved based on an exponentially growing number of published three-dimensional (3D) high resolution cryo-EM density maps. The resolution value claimed for the reconstructed 3D density map has been the topic of scientific debate for many years. The Fourier Shell Correlation (FSC) is the currently accepted cryo-EM resolution measure, but it can be subjective and has its own limitations. The FSC indicates the quality of the experimental maps but no the amount of geometric and volumetric feature details present in the 3D map. In this study, we propose supervised deep learning methods to extract representative 3D features at high, medium and low resolutions from simulated protein density maps and build classification models that objectively validate resolutions of experimental 3D cryo-EM maps. Specifically, we build classification models based on dense artificial neural network (DNN) and 3D convolutional neural network (3D CNN) architectures. The trained models can classify a given 3D cryo-EM density map into one of three resolution levels: high, medium, low. The DNN model achieved 92.73% accuracy and the 3D CNN model achieved 99.75% accuracy on simulated test maps. Applying the DNN and 3D CNN models to thirty experimental cryo-EM maps achieved an agreement of 60.0% and 56.7%, respectively, with the author published resolution value of the density maps. The results suggest that deep learning can be utilized to potentially improve the resolution validation process of experimental cryo-EM maps.
三维冷冻电子显微镜密度图的深度学习分辨率验证
冷冻电子显微镜(cryo-EM)正在成为确定蛋白质结构的首选成像方法。许多原子结构已被解决基于指数增长的数量发表的三维(3D)高分辨率低温电镜密度图。重建的三维密度图所要求的分辨率值多年来一直是科学界争论的话题。傅里叶壳相关(FSC)是目前公认的低温电镜分辨率测量方法,但它可能是主观的,并有其自身的局限性。FSC表示实验地图的质量,但不表示3D地图中存在的几何和体积特征细节的数量。在这项研究中,我们提出了监督深度学习方法,从模拟蛋白质密度图中提取高、中、低分辨率的代表性3D特征,并建立分类模型,客观验证实验3D冷冻电镜图的分辨率。具体来说,我们基于密集人工神经网络(DNN)和3D卷积神经网络(3D CNN)架构构建了分类模型。经过训练的模型可以将给定的3D低温电镜密度图分为三个分辨率级别:高、中、低。款模型实现了92.73%的准确性和3 d CNN模型模拟测试地图上已经达到了99.75%的准确率。将DNN和3D CNN模型应用于30张实验冷电镜图,与作者公布的密度图分辨率值的一致性分别为60.0%和56.7%。结果表明,深度学习可以潜在地改善实验低温电镜图的分辨率验证过程。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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