Computational Biology of Ras Proteins

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dc.contributor.advisor William R. Atchley, Committee Chair en_US
dc.contributor.advisor Carla Mattos, Committee Member en_US
dc.contributor.advisor Jeffrey Thorne, Committee Member en_US
dc.contributor.advisor Jon Doyle, Committee Member en_US Dellinger, Andrew Everette en_US 2010-04-02T18:41:53Z 2010-04-02T18:41:53Z 2008-04-07 en_US
dc.identifier.other etd-03272006-213442 en_US
dc.description.abstract In this research, computational biology is used to elucidate how evolutionary history has changed roles of structure and function among Ras proteins, with a focus on the Ras family. This dissertation begins with phylogenetic analyses of the Ras superfamily and Ras family. Phylogenetic trees of the Ras family were estimated using Neighbor-Joining, Weighted Neighbor-joining, Parsimony, Quartet Puzzling, Maximum Likelihood and Bayesian methods. In nearly all cases, each clade represented a subfamily. Clade members and clade divisions were consistent among all the trees, increasing the probability of a correct estimation of the evolutionary history. Further investigation into the evolution of sequence involved decomposing sequence covariation into its respective components. The roles of the functional and structural components of covariation were the focus of several multivariate analyses. Decision tree analysis, a data mining method, found that sequence divergence in critical sites of the hydrophobic core, dimerization regions and ligand binding regions were sufficient to divide Ras subfamilies. Alignments of GDP-bound and GTP-bound crystal structures revealed that only Ral and M-Ras proteins have structural variation in the effector binding switch I regions, while all Ras structures vary in the protein binding switch II region. Di-Ras2-GDP was shown to have a unique C-terminal loop which binds to the interswitch region. Last, a common factor analysis was computed. The factors contain the set of sites that both discriminate among the subfamilies and have a unique functional or structural role, such as Ral tree-determinant sites. Finally, sequence signatures were developed for each of the families of the Ras superfamily using Boltzmann-Shannon entropy. This method was compared to the PROSITE signature, profile hidden Markov model and MEME position-specific scoring matrix methods. The Entropy method identified approximately 8% fewer proteins than the best of the other methods, MEME. Comparative analyses of these sequence signatures determined which sites and amino acids played important roles in the changes in protein function and structure among Ras families. en_US
dc.rights I hereby certify that, if appropriate, I have obtained and attached hereto a written permission statement from the owner(s) of each third party copyrighted matter to be included in my thesis, dissertation, or project report, allowing distribution as specified below. I certify that the version I submitted is the same as that approved by my advisory committee. I hereby grant to NC State University or its agents the non-exclusive license to archive and make accessible, under the conditions specified below, my thesis, dissertation, or project report in whole or in part in all forms of media, now or hereafter known. I retain all other ownership rights to the copyright of the thesis, dissertation or project report. I also retain the right to use in future works (such as articles or books) all or part of this thesis, dissertation, or project report. en_US
dc.subject Structure en_US
dc.subject Function en_US
dc.subject Ras en_US
dc.subject Decomposing Covariation en_US
dc.subject Amino Acid Covariation en_US
dc.title Computational Biology of Ras Proteins en_US PhD en_US dissertation en_US Bioinformatics en_US

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