Date of Award

Spring 2015

Document Type


Degree Name

Doctor of Philosophy (PhD)


Computer Science

Committee Director

Jing He

Committee Member

Niko Chrisochoides

Committee Member

Shuiwang Ji

Committee Member

Desh Ranjan

Committee Member

Lesley Greene


Electron cryo-microscopy (cryo-EM) as a cutting edge technology has carved a niche for itself in the study of large-scale protein complex. Although the protein backbone of complexes cannot be derived directly from the medium resolution (5-10 Å) of amino acids from three-dimensional (3D) density images, secondary structure elements (SSEs) such as alpha-helices and beta-sheets can still be detected. The accuracy of SSE detection from the volumetric protein density images is critical for ab initio backbone structure derivation in cryo-EM. So far it is challenging to detect the SSEs automatically and accurately from the density images at these resolutions. This dissertation presents four computational methods - SSEtracer, SSElearner, StrandTwister and StrandRoller for solving this critical problem.

An effective approach, SSEtracer, is presented to automatically identify helices and β- sheets from the cryo-EM three-dimensional maps at medium resolutions. A simple mathematical model is introduced to represent the β-sheet density. The mathematical model can be used for β-strand detection from medium resolution density maps. A machine learning approach, SSElearner, has also been developed to automatically identify helices and β-sheets by using the knowledge from existing volumetric maps in the Electron Microscopy Data Bank (EMDB). The approach has been tested using simulated density maps and experimental cryo-EM maps of EMDB. The results of SSElearner suggest that it is effective to use one cryo-EM map for learning in order to detect the SSE in another cryo-EM map of similar quality.

Major secondary structure elements such as a-helices and β-sheets can be computationally detected from cryo-EM density maps with medium resolutions of 5-10Å. However, a critical piece of information for modeling atomic structures is missing, since there are no tools to detect β-strands from cryo-EM maps at medium resolutions. A new method, StrandTwister, has been proposed to detect the traces of β-strands through the analysis of twist, an intrinsic nature of β-sheet. StrandTwister has been tested using 100 β-sheets simulated at 10Å resolution and 39 β-sheets computationally detected from cryoEM density maps at 4.4-7.4Å resolutions. StrandTwister appears to detect the traces of β-strands on major β-sheets quite accurately, particularly at the central area of a β-sheet.

β-barrel is a structure feature that is formed by multiple β-strands in a barrel shape. There is no existing method to derive the β-strands from the 3D image of β-barrel. A new method, StrandRoller, has been proposed to generate small sets of possible β-traces from the density images at medium resolutions of 5-10Å. The results of StrandRoller suggest that it is possible to derive a small set of possible β-traces from the β-barrel cryo-EM image at medium resolutions even when it is not possible to visualize the separation of β-strands.