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

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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.