Collaborative Research: III: Medium: Systematic De Novo Identification of Macromolecular Complexes in Cryo-Electron Tomography Images

合作研究：III：介质：冷冻电子断层扫描图像中大分子复合物的系统从头识别

基本信息

批准号：
2211597
负责人：
Min Xu
金额：
$ 44.8万
依托单位：
Carnegie-Mellon University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2022
资助国家：
美国
起止时间：
2022-10-01 至 2025-09-30
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2211597&HistoricalAwards=false
关键词：
Collaborative Research III Medium Systematic

项目摘要

Macromolecular structures inside a cell play key functional roles in various biochemical pathways and cellular processes that govern life. Identifying structures of novel macromolecules in a cell is fundamental for our understanding of how biomolecules work together to sustain life and how diseases occur. Cryo-electron tomography (cryo-ET) is a revolutionary imaging method that enables the systematic identification and discovery of unknown structures in their native cellular context with near-atomic resolution. However, this remarkable potential of cryo-ET is still unused due to the limitations of current computational methods. The existing computational methods can only recover known structures or require extensive manual efforts to identify unknown structures. To fill the gap, this project is aimed at developing a computational pipeline that can automatically identify novel and unknown biomolecular structures from cryo-ET data by developing and integrating a series of state-of-the-art computational methods. The open-source software and the algorithms to be developed in this project will have a wide range of applications in life science. The developed computational algorithms will be beneficial broadly in other related areas, such as medical image analysis and general computer vision. The project will train postdoctoral fellows, and graduate and undergraduate students of different backgrounds through interdisciplinary coursework and direct involvement with the project at Carnegie Mellon University and Purdue University. The knowledge disseminated from the project will be presented to graduate, undergraduate, and high school students and teachers through national and international online computational biology workshops and hackathons.Cryo-ET has a unique strength in visualizing structures and spatial localizations of macromolecular complexes in single cells. This project will develop three key techniques for de novo structural identification of macromolecules captured by Cryo-ET: 1) A novel fast and exhaustive search-based subtomogram alignment approach for improved de novo structural discovery of macromolecular complexes. 2) Novel approaches for fast shape search for discovered macromolecular complexes against a structural database. 3) Then, the developed approaches will be integrated into a computational pipeline that enables automatic large-scale identification of macromolecular complexes in Cryo-ET images. The pipeline and the software to be developed in this project will be integrated into the public database and the open-source platform AITom, so that they are ready to be used by the structural and cell biology community. Overall, this project will bring Cryo-ET to the next level which allows systematic macromolecule shape determination and identification through database searches.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

细胞内的大分子结构在控制生命的各种生化途径和细胞过程中扮演关键功能作用。识别细胞中新型大分子的结构对于我们了解生物分子如何共同维持生命和疾病的发生是基础。冷冻电子层析成像（Cryo-ET）是一种革命性的成像方法，可以通过近原子分辨率在其天然细胞上下文中系统地识别和发现未知结构。但是，由于当前的计算方法的局限性，冷冻-ET的这种显着潜力仍未使用。现有的计算方法只能恢复已知的结构或需要大量的手动努力来识别未知结构。为了填补空白，该项目旨在开发一种计算管道，该计算管道可以通过开发和集成一系列最先进的计算方法来自动从冷冻数据中识别出新颖和未知的生物分子结构。该项目中要开发的开源软件和算法将在生命科学中具有广泛的应用。开发的计算算法将在其他相关领域（例如医学图像分析和一般计算机视觉）中广泛有益。该项目将培训博士后研究员，并通过跨学科的课程工作，并直接参与卡内基·梅隆大学和普渡大学的项目。通过国家和国际在线计算生物学研讨会和黑客马拉松。Cryo-ET在可视化结构和单个细胞中巨细胞群的空间定位方面，通过国家和国际在线计算生物学研讨会和黑客马拉松提供了从该项目中传播的知识。该项目将开发三种关键技术，用于从结构鉴定Cryo-Et捕获的大分子的从头结构鉴定：1）一种新型的快速且基于详尽的基于搜索的亚图表对准方法，可改善从头开始的大分子复合物的从头结构发现。 2）针对结构数据库发现发现的大分子复合物的快速搜索方法。 3）然后，开发的方法将集成到一个计算管道中，该计算管道可以自动对低温-ET图像中的大分子复合物进行自动识别。该项目中要开发的管道和软件将集成到公共数据库和开源平台Aitom中，以便它们可以被结构和细胞生物学社区使用。总体而言，该项目将使冷冻-ET提升到一个新的水平，这可以通过数据库搜索进行系统的大分子形状确定和识别。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的智力优点评估和更广泛的影响来获得支持的。