A Geometric Approach for Deciphering Protein Structure from Cryo-EM Volumes

Electron Cryo-Microscopy or cryo-EM is an area that has received much attention in the recent past. Compared to the traditional methods of X-Ray Crystallography and NMR Spectroscopy, cryo-EM can be used to image much larger complexes, in many different conformations, and under a wide range of biochemical conditions. This is because it does not require the complex to be crystallisable. However, cryo-EM reconstructions are limited to intermediate resolutions, with the state-of-the-art being 3.6A, where secondary structure elements can be visually identified but not individual amino acid residues. This lack of atomic level resolution creates new computational challenges for protein structure identification. In this dissertation, we present a suite of geometric algorithms to address several aspects of protein modeling using cryo-EM density maps. Specifically, we develop novel methods to capture the shape of density volumes as geometric skeletons. We then use these skeletons to find secondary structure elements: SSEs) of a given protein, to identify the correspondence between these SSEs and those predicted from the primary sequence, and to register high-resolution protein structures onto the density volume. In addition, we designed and developed Gorgon, an interactive molecular modeling system, that integrates the above methods with other interactive routines to generate reliable and accurate protein backbone models.

Name thesis:degree_name

Doctor of Philosophy (PhD)

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Computer Science and Engineering

Year dc:date.available

2010

Author dc:creator

• Abeysinghe, Sasakthi

Contributors dc:contributor

• Tao Ju

• Computer Science
• Biology
• Molecular
• cryo-em
• de-novo
• electron cryomicroscopy
• feature registration
• geometric approach
• gorgon

Language dc:language

English (en)

DOI

https://doi.org/10.7936/K7BZ642T

OAI identifier oai:identifier

oai:openscholarship.wustl.edu:etd-1004