Generation And Statistical Modeling Of Active Protein Chimeras: A Sequence Based Approach

<p>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.</p>

Name thesis:degree_name

Doctor of Philosophy (PhD)

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Biological Science

Year

2013

Author dc:creator

• Fico, Nicholas

Contributors dc:contributor

• Alan M. Friedman
• Daisuke Kihara
• Cynthia Stauffacher
• Clinton Chapple

• chimeric proteins
• linear regression
• logistic regression
• phenylpropanoid
• p450
• ras proteins
• Molecular Biology
• Statistics and Probability

Repository record dc:identifier

https://docs.lib.purdue.edu/open_access_dissertations/144

OAI identifier oai:identifier

oai:docs.lib.purdue.edu:open_access_dissertations-1071