NDLI: Combining image processing and modeling to generate traces of beta-strands from cryo-EM density images of beta-barrels

Content Provider	IEEE Xplore Digital Library
Author	Dong Si Jing He
Copyright Year	2014
Description	Author affiliation: Comput. Sci. Dept., Old Dominion Univ., Norfolk, VA, USA (Dong Si; Jing He)
Abstract	Electron cryo-microscopy (Cryo-EM) technique produces 3-dimensional (3D) density images of proteins. When resolution of the images is not high enough to resolve the molecular details, it is challenging for image processing methods to enhance the molecular features. β-barrel is a particular structure feature that is formed by multiple β-strands in a barrel shape. There is no existing method to derive β-strands from the 3D image of a β-barrel at medium resolutions. We propose a new method, StrandRoller, to generate a small set of possible β-traces from the density images at medium resolutions of 5-10Å. StrandRoller has been tested using eleven β-barrel images simulated to 10Å resolution and one image isolated from the experimentally derived cryo-EM density image at 6.7Å resolution. StrandRoller was able to detect 81.84% of the β-strands with an overall 1.5Å 2-way distance between the detected and the observed β-traces, if the best of fifteen detections is considered. Our results 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 no separation of the β-strands is visible in the images.
Sponsorship	IEEE Eng. Med. Biol. Soc.
Starting Page	3941
Ending Page	3944
File Size	853564
Page Count	4
File Format	PDF
ISBN	9781424479290
ISSN	1557170X
DOI	10.1109/EMBC.2014.6944486
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-08-26
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image resolution Proteins Surface fitting Mathematical model Three-dimensional displays Amino acids
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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