A deep convolutional neural network approach to single-particle recognition in cryo-electron microscopy.
A deep convolutional neural network approach to single-particle recognition in cryo-electron microscopy.
复制标题
冷冻电子显微镜中单粒子识别的深度卷积神经网络方法
DOI：
10.1186/s12859-017-1757-y
复制
发表时间：
2017-07-21
影响因子：
3
通讯作者：
Mao Y
中科院分区：
文献类型：
--
作者：
Zhu Y;Ouyang Q;Mao Y
背景单粒子冷冻电子显微镜（cryo-EM）已成为生物大分子复合物结构测定的主流工具。然而，高分辨率冷冻-EM重建通常需要数十万个单粒子图像。因此，从实验显微照片中提取颗粒可能是费力的，并且在冷冻EM结构测定中呈现出主要的实际瓶颈。现有的粒子拾取计算方法通常使用低分辨率模板进行粒子匹配，使其容易受到参考依赖性偏差的影响。关键是要开发一个高效的无模板的方法，从cryo-EM显微照片的粒子图像的自动识别。ResultsWe开发了一个基于深度学习的算法框架，DeepEM，从嘈杂的cryo-EM显微照片的单粒子识别，使自动化的粒子拾取，选择和验证在一个集成的方式。DeepEM的内核建立在由八层组成的卷积神经网络（CNN）上，可以递归地训练为高度“知识化”。在标准KLH数据集上测试时，我们的方法表现出更好的性能和准确性。DeepEM的几个具有挑战性的实验cryo-EM数据集的应用表明，它的能力，以避免选择不需要的颗粒和非颗粒，即使当真正的颗粒包含较少的features.ConclusionsThe DeepEM方法，来自深CNN，允许自动粒子提取从原始cryo-EM显微照片在没有模板。它显示了改进的性能、客观性和准确性。这种新方法的应用有望解放单粒子验证中所涉及的劳动力，显着提高低温EM数据处理的效率。
BackgroundSingle-particle cryo-electron microscopy (cryo-EM) has become a mainstream tool for the structural determination of biological macromolecular complexes. However, high-resolution cryo-EM reconstruction often requires hundreds of thousands of single-particle images. Particle extraction from experimental micrographs thus can be laborious and presents a major practical bottleneck in cryo-EM structural determination. Existing computational methods for particle picking often use low-resolution templates for particle matching, making them susceptible to reference-dependent bias. It is critical to develop a highly efficient template-free method for the automatic recognition of particle images from cryo-EM micrographs.ResultsWe developed a deep learning-based algorithmic framework, DeepEM, for single-particle recognition from noisy cryo-EM micrographs, enabling automated particle picking, selection and verification in an integrated fashion. The kernel of DeepEM is built upon a convolutional neural network (CNN) composed of eight layers, which can be recursively trained to be highly “knowledgeable”. Our approach exhibits an improved performance and accuracy when tested on the standard KLH dataset. Application of DeepEM to several challenging experimental cryo-EM datasets demonstrated its ability to avoid the selection of un-wanted particles and non-particles even when true particles contain fewer features.ConclusionsThe DeepEM methodology, derived from a deep CNN, allows automated particle extraction from raw cryo-EM micrographs in the absence of a template. It demonstrates an improved performance, objectivity and accuracy. Application of this novel method is expected to free the labor involved in single-particle verification, significantly improving the efficiency of cryo-EM data processing.
登录
查看更多内容
影响因子：
3
作者：
Langlois R;Frank J
通讯作者：
Frank J
影响因子：
3
作者：
Chen, James Z.;Grigorieff, Nikolaus
通讯作者：
Grigorieff, Nikolaus
人类26S蛋白酶体动态调控的结构基础
DOI：
10.1073/pnas.1614614113
发表时间：
2016-11-15
影响因子：
11.1
作者：
Chen, Shuobing;Wu, Jiayi;Mao, Youdong
通讯作者：
Mao, Youdong
影响因子：
64.8
作者：
RUMELHART, DE;HINTON, GE;WILLIAMS, RJ
通讯作者：
WILLIAMS, RJ
影响因子：
3
作者：
Huang, Z;Penczek, PA
通讯作者：
Penczek, PA