Abstract/Details

Understanding protein motions by computational modeling and statistical approaches


2008 2008

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Abstract (summary)

Because of its appealing simplicity, the elastic network model (ENM) has been widely accepted and applied to study many molecular motion problems, such as the molecular mechanisms of chaperonin GroEL-GroES function, allosteric changes in hemoglobin, ribosome motions, motor-protein motions, and conformational changes in general. In this dissertation, the ENM is employed to study various protein dynamics problems, and its validity is also examined by comparing with experimental data. First, we apply principal component analysis (PCA) to identify the essential protein motions from multiple structures (X-ray, NMR and MD) of the HIV-1 protease. We find significant similarities between the first few of these key motions and the first few low-frequency normal modes from the ENM, suggesting that the ENM provides a coarse-grained and structurally-based explanation for the experimentally observed conformational changes. Second, we extend these approaches from a single protein (HIV-1 protease) to thousands of proteins whose multiple NMR structures are available. We also find close correspondence between the experimentally observed dynamics and the ENM predicted ones, indicating the validity of using the ENM to computationally predict protein dynamics. Third, we develop a regression model for the isotropic B-factor predictions by combining the protein rigid body motions with the ENM. The new model shows significant improvements in B-factor predictions. Fourth, we further examine the validity of using the ENM to study protein motions. We use the anisotropic form of ENM to predict the anisotropic temperature factors of proteins. It presents a timely and important evaluation of the model, shows the extent of its accuracy in reproducing experimental anisotropic temperature factors, and suggests ways to improve the model. Finally, we apply the ENM to study a dataset of 170 protein pairs having "open" and "closed" structures, and try to address how well a conformational change can be predicted by the ENM and how to improve the model. The results indicate that the applicability of ENM for explaining conformational changes is not limited by either the size of the studied protein or even the scale of the conformational change. Instead, it depends strongly on how collective the transition is.

Indexing (details)


Subject
Biostatistics;
Bioinformatics;
Biophysics
Classification
0308: Biostatistics
0715: Bioinformatics
0786: Biophysics
Identifier / keyword
Biological sciences; B-factor predictions; Elastic network model; Protein motions
Title
Understanding protein motions by computational modeling and statistical approaches
Author
Yang, Lei
Number of pages
116
Publication year
2008
Degree date
2008
School code
0097
Source
DAI-B 69/08, Dissertation Abstracts International
Place of publication
Ann Arbor
Country of publication
United States
ISBN
9780549688105
Advisor
Jernigan, Robert L.; Wu, Zhijun
Committee member
Dobbs, Drena; Dorman, Karin; Honavar, Vasant
University/institution
Iowa State University
Department
Biochemistry, Biophysics, and Molecular Biology
University location
United States -- Iowa
Degree
Ph.D.
Source type
Dissertations & Theses
Language
English
Document type
Dissertation/Thesis
Dissertation/thesis number
3316179
ProQuest document ID
304631992
Copyright
Database copyright ProQuest LLC; ProQuest does not claim copyright in the individual underlying works.
Document URL
http://search.proquest.com/docview/304631992
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