Date of Award
Summer 2013
Document Type
Thesis
Degree Name
Master of Science (MS)
Department
Computer Science
Committee Director
Nikos Chrisochoides
Committee Member
Jing He
Committee Member
Andrey Chernikov
Abstract
In the post-genomic era, proteomics research presents a new frontier in life science. Proteins play roles in virtually every biological process, and understanding their atomic structures is the key to unraveling how they carry out their work. Compared to the over half million protein sequences in UniProt, only around 25,000 unique sequences have been atomically modeled and deposited to PDB (Protein Databank). Cryoelectron Microscopy (cryoEM) is an important biophysical technique that produces 3D subnanometer resolution images of molecules not amenable to past approaches like x-ray crystallography or nuclear magnetic resonance. De novo modeling is becoming a promising approach to derive the atomic structure of proteins from the cryoEM 3D images at "medium" resolutions{between 5 and 10 A.
Distance measurement along 1D skeletons of 3D images is an important step in de novo modeling. Despite the need of such measurement, little has been investigated about its accuracy in searching for an effective method. We propose a method to refine the skeletal length via line simplification after selecting the appropriate segmentation from the density map using Hausdorff distances. Complementarily, we developed a motion planning approach to estimate the minimum length of a loop lying completely within a contour of the density map. To test the methods, loops between 1 and 10 residues in length were extracted from atomic structures in PDB and used to generate density maps at 8 A resolution, along with experimentally derived density maps from EMDB (Electron Microscopy Databank).
Rights
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DOI
10.25777/cxy3-9791
Recommended Citation
McKnight, Andrew R..
"Protein Loop Length Estimation From Medium Resolution Cryoem Images"
(2013). Master of Science (MS), Thesis, Computer Science, Old Dominion University, DOI: 10.25777/cxy3-9791
https://digitalcommons.odu.edu/computerscience_etds/32