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）使观察到了细胞的观察 细胞器和大分子复合物在纳米分辨率下具有天然构型。 可用的冷冻数据量迅速增加，但是带来了一些专业 分析的挑战，我们将在此提案中及时解决。我们将设计新的数据 - 驱动的机器学习算法，用于改善结构歧视和解决方案。在 特别是，我们有以下具体目的：（1）我们将开发一个新颖的自动编码器，并且 迭代区域匹配（AIM）算法，用于图像倾斜系列的无标记对齐 通过改进分辨率重建断层图； （2）我们将开发基于显着的自动 选择算法以更好地检测大分子复合物，并将其与 创新的2D到3D框架，以进一步提高结构检测准确性； （3）我们将设计 端到端卷积模型，用于姿势不变的亚图图。这个模型将 产生初始聚类，该聚类将通过新的子图平均图来完善，该算法平均 自动减少噪声的子图和贡献很少的子图； （4）我们将表演 通过使用先前报道的细菌分泌系统和 线粒体超微结构数据集可改善最终分辨率。在中实现算法 AIMS 1-3，我们将开发一个用户友好的开源图形用户界面α-TOM直接 受益于科学界。 α-TOM将与现有软件进行系统的比较 包括IMOD，EMAN2和模拟和基准数据集的Relion。促进 分布，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会促进和加速冷冻的未来使用 ET，但也可以很容易地用于分析现有的大量冷冻数据 提高我们对大分子的结构，功能和空间组织的理解 原位配合物。