Abstract
A protein's 3D structure is the key to understanding its biological function. In recent years, cryo-electron microscopy or cryo-EM has established itself as a mainstream technique to capture proteins' structure at near-native conditions. However the vast majority of cryo-EM data are at medium (5-10A) or low (>10A) resolutions, which is insufficient to capture a protein's atomic structure. Fortunately, at such resolutions, some intrinsic structures of a protein, such as secondary structure elements (SSEs) and smooth c-alpha backbone fragments (SCBFs), can be recognized or robustly detected. In this dissertation, we present efficient protein fitting pipelines to recover a protein's atomic structure given the protein's cryo-EM density map by leveraging the intrinsic structure information detected from the density map. Specifically, we first compute the correspondences between the protein features (SSEs or SCBFs) detected from the cryo-EM density map and those extracted from the template model. Then we fit the template model into the cryo-EM density map guided by the obtained correspondences.
Committee Chair
Tao Ju
Committee Members
Matthew L. Baker, Michael Brent, Jeremy Buhler, Yasutaka Furukawa
Degree
Doctor of Philosophy (PhD)
Author's Department
Computer Science & Engineering
Document Type
Dissertation
Date of Award
Summer 8-15-2017
Language
English (en)
DOI
https://doi.org/10.7936/K72J6B7X
Recommended Citation
Dou, Hang, "Efficient Geometric Approaches for Mining Protein Structure from Cryo-EM Density Maps" (2017). McKelvey School of Engineering Theses & Dissertations. 304.
The definitive version is available at https://doi.org/10.7936/K72J6B7X
Comments
Permanent URL: https://doi.org/10.7936/K72J6B7X