OptZyme: a computational tool for altering enzymatic specificity
Open Access
- Author:
- Grisewood, Matthew Jeffery
- Graduate Program:
- Chemical Engineering
- Degree:
- Master of Science
- Document Type:
- Master Thesis
- Date of Defense:
- April 04, 2013
- Committee Members:
- Costas D Maranas, Thesis Advisor/Co-Advisor
Michael John Janik, Thesis Advisor/Co-Advisor - Keywords:
- beta-glucuronidase
enzyme
redesign
molecular mechanics
OptZyme
transition state analogue
specificity - Abstract:
- OptZyme is a new computational procedure that is designed to improve enzymatic activity (i.e., kcat or kcat/KM) with a novel substrate. The key concept here is to use transition state analogue (TSA) compounds, which are known for many enzymatic reactions, as substitutes for the typically unknown transition state (TS) structures. Mutations that minimize the interaction energy of the enzyme with its TSA, rather than with its substrate, are identified that lower the transition state energy barrier. Using results of Escherichia coli β-glucuronidase as a benchmark, we confirm that KM correlates (R2 = 0.960) with the interaction energy between Escherichia coli β-glucuronidase and para-nitrophenyl- β D-glucuronide, kcat/KM correlates (R2 = 0.864) with the interaction energy of the transition state analogue molecule, D-glucaro-1, 5-lactone, and kcat correlates (R2 = 0.854) with a weighted combination of interaction energies with para-nitrophenyl- β, D-glucuronide and D-glucaro-1, 5-lactone. OptZyme is subsequently used to identify mutants with improved KM, kcat, and kcat/KM, respectively for the new substrate, para-nitrophenyl- β, D-galactoside. Differences between the three libraries shed light onto structural differences that underpin improved KM, kcat, or kcat/KM. Mutants predicted to enhance activity of pNP-GAL were mostly those that indirectly or directly helped create hydrogen bonds with the altered sugar ring conformation or its substituents, namely H162S, L361G, W549R, and N550S. Therefore, OptZyme is an inexpensive tool that can predict mutations for promoting catalysis of a reaction of a related but new substrate, where a natural enzyme may be unknown. Note: This manuscript is being prepared for publication.