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Title Generation And Statistical Modeling Of Active Protein Chimeras: A Sequence Based Approach
Publication Date
Degree PhD
Discipline/Department Biological Science
Degree Level doctoral
University/Publisher Purdue University
Abstract Generation of active protein chimeras is a valuable tool to probe the functional space of proteins. Statistical modeling is the next logical step, allowing us to build a model of gene fragment replaceability between species. In this thesis I begin to develop the statistical tools that are needed to systematically describe combinatorial protein libraries. I present three sets of diverse chimeric protein libraries developed using sequence information. The statistical model of the human N-Ras and human K-Ras-4B genes reveal a set previously unidetifed surface residues on the N-Ras G-Domain that may be involved in cellular localization. Statistical modeling of a library of chimeric proteins between A. thaliana cinnamate 4-hydroxylase (AtC4H) and S. moellendorffii cinnamate 4-hydroxylase (SmC4H) reveal a possible stabilizing effect of the N-terminal amino acids from SmC4H and, irreplaceable catalytic domains between AtC4H and SmC4H. I also show gene fragment replaceability on a small scale between functionally divergent AtC4H and A. thaliana ferulate 5-hyrdoxylase proteins. Finally, I show that commonly occurring residue pairs in the sequence record are effective covariates when modeling activity in the AtC4H-SmC4H chimeric library.
Subjects/Keywords chimeric proteins; linear regression; logistic regression; phenylpropanoid; p450; ras proteins; Molecular Biology; Statistics and Probability
Contributors Alan M. Friedman; Daisuke Kihara; Alan M. Friedman; Cynthia Stauffacher; Clinton Chapple
Country of Publication us
Record ID oai:docs.lib.purdue.edu:open_access_dissertations-1071
Repository purdue-diss
Date Retrieved
Date Indexed 2019-10-07
Created Date 2013-10-01 07:00:00

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