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.
Degree Type
Dissertation
Degree Name
Doctor of Philosophy (PhD)
Department
Biological Science
Committee Chair
Daisuke Kihara
Date of Award
Fall 2013
Recommended Citation
Fico, Nicholas, "Generation And Statistical Modeling Of Active Protein Chimeras: A Sequence Based Approach" (2013). Open Access Dissertations. 144.
https://docs.lib.purdue.edu/open_access_dissertations/144
First Advisor
Alan M. Friedman
Committee Member 1
Alan M. Friedman
Committee Member 2
Cynthia Stauffacher
Committee Member 3
Clinton Chapple