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

用于改善冷冻电子断层扫描结构辨别的新型机器学习方法-行政补充

• 批准号: 10388867
• 负责人: Min Xu
• 金额: $ 11.28万
• 依托单位: CARNEGIE-MELLON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-06-10 至 2024-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10388867
• 关键词: 3-Dimensional; Address; Administrative Supplement; Algorithmic Software; Algorithms; Benchmarking; Communities; Computer software; Cryo-electron tomography; Data; Data Set; Detection; Discrimination; Evaluation; Future; Image; In Situ; Machine Learning; Macromolecular Complexes; Mitochondria; Modeling; Molecular Conformation; Monitor; Noise; Organelles; Performance; Publishing; Reporting; Resolution; Series; Structure; System; Time; Tomogram; Weight; autoencoder; automated algorithm; base; design; graphical user interface; improved; innovation; machine learning algorithm; nanometer resolution; novel; novel strategies; open source; reconstruction; user-friendly

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 address 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 reconstruct 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 subtomogram 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 mitochondrial ultrastructures datasets to improve the final resolution. Implementing algorithms in Aims 1-3, we will develop a user-friendly open-source graphical user interface α-tom to directly benefit the scientific community. α-tom will be systematically compared with existing software including IMOD, EMAN2, and Relion on simulated and benchmark datasets. To facilitate distribution, α-tom will be integrated into existing software platforms Scipion 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 improve our understanding of the structure, function, and spatial organization of macromolecular complexes in situ.

项目摘要 细胞冷冻电子断层扫描(Cryo-ET)使细胞的观察成为可能 细胞器和具有天然构象的纳米分辨率的大分子复合体。 然而，可用的Cryo-ET数据量的快速增长带来了一些主要的 我们将在本提案中及时解决分析方面的挑战。我们将设计新的数据- 改进结构辨别和分辨率的驱动机器学习算法。在……里面 具体地说，我们有以下具体目标：(1)我们将开发一种新型的自动编码器和 图像倾斜序列无标记对齐的迭代区域匹配(AIM)算法 以更高的分辨率重建断层图像；(2)我们将开发一种基于显著性的自动图像处理系统 一种更好地检测大分子络合物的拾取算法，并将其与一种 创新2D到3D框架，进一步提高结构检测精度；(3)我们将设计 一种用于亚断层图像姿势不变聚类的端到端卷积模型。这款车型将 生成初始聚类，该初始聚类将通过新的亚断层图平均算法进行细化 自动对噪声和贡献很小的子图进行加权；(4)我们将执行 使用以前报道的细菌分泌系统和 线粒体超微结构数据集，提高最终分辨率。在中实现算法 目标1-3，我们将开发一个用户友好的开源图形用户界面α-TOM，以直接 造福科学界。α-TOM将与现有软件进行系统比较 包括模拟数据集和基准数据集上的IMOD、EMAN2和Relion。为了方便 发布后，α-TOM将集成到现有的软件平台Scipion和TomoMiner中。我们的 数据驱动的算法和软件不仅将促进和加速未来对Cryo的使用- ET，也可以很容易地用于分析现有的大量低温数据 提高我们对大分子的结构、功能和空间组织的理解 原位复合体。