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

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着关键的功能角色。识别细胞中新的大分子的结构是我们理解生物分子如何协同工作以维持生命以及疾病如何发生的基础。低温电子断层成像(CRYO-ET)是一种革命性的成像方法，它能够以近原子分辨率系统地识别和发现天然细胞环境中的未知结构。然而，由于目前计算方法的限制，低温辐射的这种显著潜力仍然没有得到充分利用。现有的计算方法只能恢复已知结构或需要大量的人工工作来识别未知结构。为了填补这一空白，该项目旨在开发一种计算管道，通过开发和集成一系列最先进的计算方法，从低温数据中自动识别新的和未知的生物分子结构。该项目将要开发的开源软件和算法将在生命科学中有广泛的应用。开发的计算算法将在其他相关领域广泛受益，如医学图像分析和通用计算机视觉。该项目将通过跨学科的课程工作和直接参与卡内基梅隆大学和普渡大学的项目，培养博士后研究员以及不同背景的研究生和本科生。该项目传播的知识将通过国内和国际在线计算生物学研讨会和黑客松向研究生、本科生和高中学生和教师展示。Cryo-et在可视化单细胞中大分子复合体的结构和空间定位方面具有独特的优势。该项目将开发三项关键技术来从头识别被Cryo-et捕获的大分子：1)一种新的快速和穷尽的基于搜索的亚层析比对方法，用于改进大分子络合物的从头结构发现。2)根据结构数据库快速形状搜索已发现的大分子络合物的新方法。3)然后，开发的方法将被集成到一个计算管道中，从而能够自动大规模识别Cryo-ET图像中的大分子络合物。将在该项目中开发的管道和软件将整合到公共数据库和开放源码平台AITom中，以便供结构和细胞生物界使用。总体而言，该项目将把CRYO-ET带到一个新的水平，允许通过数据库搜索进行系统的高分子形状确定和鉴定。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.