Novel machine learning approaches for improving structural discrimination in cryo-electron tomography

项目摘要 细胞冷冻电子断层扫描(Cryo-ET)使观察细胞细胞器和 具有天然构象的纳米分辨率的大分子络合物。快速增长的数量 然而，现有的Cryo-ET数据带来了一些重大的分析挑战，我们将适时地- 穿上这件求婚礼服。我们将设计新的数据驱动的机器学习算法来改进结构 歧视和解决。特别是，我们有以下特殊的fic目标：(1)我们将开发一种新的 图像倾斜序列无标记对齐的自动编码和迭代区域匹配(AIM)算法 构造分辨率更高的断层图像；(2)我们将开发一种基于显著性的自动拾取算法，用于 更好地检测大分子复合体，并将其与创新的2D到3D框架相结合，以进一步 提高了结构检测的准确率；(3)设计了一种端到端的位姿不变量卷积模型 亚断层图像的聚类。该模型将产生一个初始聚类，该聚类将被一个新的子集重新命名(fiNed)。 对噪声和贡献较小的子图进行自动降权的图像平均算法； 我们将使用以前报道的细菌分泌系统和有丝分裂素来进行实验评估。 线粒体超微结构数据集，以提高fiNAL分辨率。在目标1-3中实现算法，我们将 开发一个用户友好的开源图形用户界面-tom，以直接造福于fific社区。 -TOM将与现有的软件进行系统的比较，包括IMOD、EMAN2和Relion上的模拟 和基准数据集。为了便于分发，-tom将集成到现有的软件平台Sci- Pion和TomoMiner。我们的数据驱动算法和软件不仅将促进和加速未来 使用CRYO-ET，还可以方便地用于分析现有的大量CRYO-ET数据，以便将其输入到数据库中。 证明我们对大分子络合物的结构、功能和空间组织的理解 SITE。

Project Summary Cellular cryo-electron tomography (Cryo-ET) has made possible the observation of cellular organelles and macromolecular complexes at nanometer resolution with native conformations. The rapid increasing amount of Cryo-ET data available however brings along some major challenges for analysis which we will timely ad- dress in this proposal. We will design novel data-driven machine learning algorithms for improving structural discrimination and resolution. In particular, we have the following specific aims: (1) We will develop a novel Autoencoder and Iterative region Matching (AIM) algorithm for marker-free alignment of image tilt-series to re- construct tomograms with improved resolution; (2) We will develop a saliency-based auto-picking algorithm for better detecting macromolecular complexes, and combine it with an innovative 2D-to-3D framework to further improve structure detection accuracy; (3) We will design an end-to-end convolutional model for pose-invariant clustering of subtomograms. This model will produce an initial clustering which will be refined by a new subto- mogram averaging algorithm that automatically down-weights subtomograms of noise and little contribution; (4) We will perform experimental evaluations by using previously reported bacterial secretion systems and mito- chondrial ultrastructures datasets to improve the final resolution. Implementing algorithms in Aims 1-3, we will develop a user-friendly open-source graphical user interface -tom to directly benefit the scientific community. -tom will be systematically compared with existing software including IMOD, EMAN2, and Relion on simulated and benchmark datasets. To facilitate distribution, -tom will be integrated into existing software platforms Sci- pion and TomoMiner. Our data-driven algorithms and software not only will facilitate and accelerate the future use of Cryo-ET, but also can be readily used on analyzing the existing large amounts of Cryo-ET data to im- prove our understanding of the structure, function, and spatial organization of macromolecular complexes in situ.