Deep learning methods for automated and accurate reconstruction of protein structures from cryo-EM image data

项目摘要 低温电子显微镜(Cryo-EM)已成为测定蛋白质的主要实验技术 最近几年，它已经达到了原子分辨率(1.2-4？)。与传统技术相比(即，X- 射线结晶学和核磁共振)，冷冻-EM具有独特的能力来确定 大的蛋白质复合体和组合体的四级结构，它们很难或不可能处理。这个 冷冻-EM技术的进步引发了研究大蛋白质的结构生物学革命 以前无法很好地研究的络合物和集合体。然而，计算重建的方法 从冷冻-EM图像数据中提取蛋白质结构仍然是一项耗时、费力、容易出错的工作，而且往往 过程不准确，由于在低温EM图像中拾取蛋白质颗粒的瓶颈，3D中存在大量噪声 从粒子图像生成的Cryo-EM密度图，以及缺乏自动和准确的构建方法 来自密度图的蛋白质结构。我们计划开发先进的深度学习方法来重建蛋白质 结构自动和准确地从低温EM图像数据，利用大量的高 实地积累的分辨率低温电磁数据和深度学习技术的最新进展。 我们将开发建立在注意力机制之上的2D变压器网络，其性能优于 传统卷积和递归神经网络在图像处理中提取单个蛋白质颗粒的研究 通过一种新的无监督和有监督的组合，准确自动地处理冷冻-EM图像数据 学习。此外，我们还阐述了由二维粒子生成的三维低温电磁密度图的去噪问题 图像作为一种新的机器学习问题，将开发3D深度自动编码器和旋转- /平移等变变压器网络，以消除低温电磁密度图中的噪声。此外，我们还将 开发端到端3D旋转/平移等变网络，直接识别主干原子 来自3D密度图的蛋白质，而不使用任何已知结构作为模板，这将被一种新的 隐马尔可夫模型可以构建任何蛋白质的高分辨率全原子结构。方法将是 对大量的低温电磁数据进行了严格的评估，并与现有的方法进行了比较。所有这些都是 这些方法将被集成在一起，以创建一个全自动的机器学习管道，这是 在这个领域，与现有的方法相比，从冷冻-EM图像中更准确地重建蛋白质结构。我们会 实现个人深度学习方法以及整个管道作为开放源码包发布 在GitHub供社区使用。我们将通过将这些工具和管道应用于新的 一组重要的膜蛋白复合体(即离子通道)的冷冻-EM数据将在 布鲁克海文国家实验室。

Project Summary Cryogenic electron microscopy (cryo-EM) has emerged as a major experimental technology to determine protein structures as it reached atomic resolution (1.2-4Å) in recent years. Compared to traditional techniques (i.e., X- ray crystallography and nuclear magnetic resonance), cryo-EM has the unique capability of determining the quaternary structures of large protein complexes and assemblies difficult or impossible for them to handle. The advance of cryo-EM technology has stimulated a revolution in structural biology of studying large protein complexes and assemblies that cannot be well studied before. However, the computational reconstruction of protein structures from cryo-EM image data is still a time-consuming, labor-intensive, error-prone, and often inaccurate process, due to the bottleneck in picking protein particles in cryo-EM images, substantial noise in 3D cryo-EM density maps generated from particle images, and lack of automated and accurate methods to build protein structures from density maps. We plan to develop advanced deep learning methods to reconstruct protein structures automatically and accurately from cryo-EM images data, leveraging the large amount of high- resolution cryo-EM data accumulated in the field and the latest advances in the deep learning technology. We will develop 2D transformer networks built on top of the attention mechanism that perform better than traditional convolutional and recurrent neural networks in image processing to pick single protein particles accurately and automatically in cryo-EM image data via a novel combination of unsupervised and supervised learning. Moreover, we formulate the problem of denoising 3D cryo-EM density maps generated from 2D particle images as a novel machine learning problem and will develop both 3D deep autoencoders and rotation- /translation-equivariant transformer networks to remove noise in cryo-EM density maps. Furthermore, we will develop end-to-end 3D rotation-/translation-equivariant networks to directly identify the backbone atoms of proteins from 3D density maps without using any known structure as template, which will be used by a novel hidden Markov model to build the high-resolution full-atom structures of any protein. The methods will be rigorously evaluated on the large amount of cryo-EM data and compared with existing methods. All these methods will be integrated together to create a fully automated machine learning pipeline, the first of its kind in the field, to reconstruct protein structures more accurately from cryo-EM images than existing methods. We will implement the individual deep learning methods as well as the entire pipeline as open-source packages released at GitHub for the community to use. We will further validate the tools and pipeline by applying them to the new cryo-EM data of a group of important membrane protein complexes (i.e., ion channels) to be generated at the Brookhaven National Laboratory.