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
