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

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

• 批准号: 9973462
• 负责人: Min Xu
• 金额: $ 34.3万
• 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/9973462
• 关键词: 3-Dimensional; Algorithmic Software; Algorithms; Back; Benchmarking; Biological Process; Cells; Communities; Computer Analysis; Computer software; Cryo-electron tomography; Data; Data Analyses; Data Set; Detection; Discrimination; Evaluation; Future; Gaussian model; Group Structure; Hour; Image; In Situ; Knowledge; Laplacian; Literature; Machine Learning; Macromolecular Complexes; Manuals; Methods; Mitochondria; Modeling; Molecular Conformation; Monitor; Neurophysiology - biologic function; Noise; Organelles; Performance; Process; Publishing; Reporting; Resolution; Series; Signal Transduction; Structure; System; Techniques; Testing; Time; Tomogram; Weight; Work; autoencoder; automated algorithm; base; deep learning; design; falls; feature detection; graphical user interface; improved; innovation; insight; machine learning algorithm; nano; nanometer resolution; novel; novel strategies; open source; particle; pi-Mesons; programs; reconstruction; success; 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 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.

项目总结