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Nucleic acids encode the information required to create life, and polymerases are the gatekeepers charged with maintaining the storage and flow of this genetic information. Synthetic biologists utilize this universal property to modify organisms and other systems to create unique traits or improve the function of others. One of the

Nucleic acids encode the information required to create life, and polymerases are the gatekeepers charged with maintaining the storage and flow of this genetic information. Synthetic biologists utilize this universal property to modify organisms and other systems to create unique traits or improve the function of others. One of the many realms in synthetic biology involves the study of biopolymers that do not exist naturally, which is known as xenobiology. Although life depends on two biopolymers for genetic storage, it may be possible that alternative molecules (xenonucleic acids – XNAs), could be used in their place in either a living or non-living system. However, implementation of an XNA based system requires the development of polymerases that can encode and decode information stored in these artificial polymers. A strategy called directed evolution is used to modify or alter the function of a protein of interest, but identifying mutations that can modify polymerase function is made problematic by their size and overall complexity. To reduce the amount of sequence space that needs to be samples when attempting to identify polymerase variants, we can try to make informed decisions about which amino acid residues may have functional roles in catalysis. An analysis of Family B polymerases has shown that residues which are involved in substrate specificity are often highly conserved both at the sequence and structure level. In order to validate the hypothesis that a strong correlation exists between structural conservation and catalytic activity, we have selected and mutated residues in the 9°N polymerase using a loss of function mutagenesis strategy based on a computational analysis of several homologues from a diverse range of taxa. Improvement of these models will hopefully lead to quicker identification of loci which are ideal engineering targets.
ContributorsHaeberle, Tyler Matthew (Author) / Chaput, John (Thesis director) / Chen, Julian (Committee member) / Larsen, Andrew (Committee member) / Barrett, The Honors College (Contributor) / Department of Chemistry and Biochemistry (Contributor) / School of Life Sciences (Contributor)
Created2015-05