Computational Biology of Ras Proteins

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Title: Computational Biology of Ras Proteins
Author: Dellinger, Andrew Everette
Advisors: William R. Atchley, Committee Chair
Carla Mattos, Committee Member
Jeffrey Thorne, Committee Member
Jon Doyle, Committee Member
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.
Date: 2008-04-07
Degree: PhD
Discipline: Bioinformatics

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