Date of Award

8-2014

Document Type

Thesis

Degree Name

Master of Science (MS)

College/School

College of Science and Mathematics

Department/Program

Chemistry and Biochemistry

Thesis Sponsor/Dissertation Chair/Project Chair

Nina M. Goodey

Committee Member

John Siekierka

Committee Member

David P. Rotella

Abstract

Dihydrofolate Reductase (DHFR) is an essential enzyme for most organisms, ranging from bacteria to humans. DHFR has essential functions in DNA biosynthesis and cell replication; as a result, cell growth can be inhibited by the inhibition of DHFR. Evaluating the use of well-known DHFR inhibitors is becoming essential in treating infections in the developing world as DHFR is a known target of antibacterial and antiparasitic drugs. Understanding determinants of DHFR inhibitor specificity in terms of amino acid sequence and structure will allow repurposing or designing of new compounds that selectively target DHFR from the pathogenic organism of interest over the Hs DHFR. Previously, a computational analysis was developed to predict allosteric residues involved in ligand discrimination using DHFR as a model system. The approach was based on inhibitor specificity and amino acid composition for sets of protein homolog pairs, predicting eighteen alignment positions. The residues were clustered as follows: three of the residues are found in the active site; four of the residues are proximal to the active site, four of the residues are clustered together in the adenosine binding domain and five of the residues are on the βFβG loop. Many of the predicted residues are located in allosteric region away from the active site. My role in this project was to experimentally validate these predictions using site specific mutations in B. Strearothermophilus DHFR gene as a model system. To ensure all mutants were correctly folded and active, turnover numbers (kcat) and Michaelis constants (KM) were measured for wildtype and mutants. KI values of the 12 single mutants against four DHFR inhibitors, methotrexate, trimethoprim, pyrimethamine, and raltitrexed were determined. Interestingly, comparisons of the ligand binding profiles of the mutants to those of the wildtype enzyme revealed significant changes in ligand specificity, supporting the predictions. In addition, the effects of mutations on KI values are ligand specific.

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