The relationship between enzyme structure, thermostability, catalytic efficiency of threepoint-mutations in theβ-glucosidase B enzyme

Student Name: 
Darren Wang
UCD Department: 
Department of Chemistry
UCD Mentor: 
Justin B. Siegel

The ability to accurately view a protein in 3 dimensions is very new. Although the images are accurate, the data the models give are faulty due to lack of background data. With a massive database, scientists can compile an algorithm that will greatly help future enzyme engineers. In the future, they no longer need to test every mutant they find interesting in a wet lab, scientists will only need a laptop and a few minutes instead of a weeklong process. So far, the Siegel lab has collected data for 129 mutants of beta-glucosidase enzyme, also known as BglB. For this research, 6 original enzymes are designed using the current computational model, also known as the Rosetta Model, to mutate BglB. Other mutants of the BglB enzyme were grown simultaneously. Lastly, a positive control was used as a benchmark. After the cells were induced with the desired DNA, assays were executed to calculate the thermostability and the Michaelis-Menten constants (kcat, KM, and kcat/KM). Although the research has no short-term results, the small amount of data we can provide is still valuable for the development of a better predictive algorithm. As seen from the data we collected, we can see a general trend of lowering of catalytical efficiencies and a rise in thermostability for the three specific mutants.