Start Date
27-4-2023 10:30 AM
Document Type
Poster
Description
Cell research tells us how the cell works to keep the body healthy and what diseases cause if they do not work correctly. Cell biological studies show cells' structure, growth, reproduction, and death. The Main idea of the research on Beta-glucosidase (BglB) mutation is how we can generate reliable data about genes and enzyme functions to improve the AI model. Computational tools to predict enzyme stability and catalytic efficiency are a growing method in protein engineering. To improve the predictive accuracy of enzyme modeling software, many data are needed to train the algorithms. The enzyme variants were modeled with FoldIT software, built using Kunkel Mutagenesis methods in Escherichia coli, and the purified proteins were tested for kinetic activity. Adding these mutations to the Design2Data (D2D) Course-based Undergraduate Research Experience database contributes to an improved understanding of the structure-function relationship of ß-glucosidase B. It expands the potential for improving the accuracy of computational modeling tools for protein design. BglB is a suitable model enzyme and easy to mutate. I have researched the BglB mutation A236C. My mutagenesis is successful. After successful mutagenesis, I used BL21 E coli bacteria to make my mutation protein. I will be purifying my mutant enzyme using immobilized metal affinity chromatography. This process isolates the BglB enzyme from all the other cell debris. After the purified enzyme, I will check the stability of my mutant enzyme and at what temperature the enzyme can work or cannot function correctly.
Image
Beta-glucosidase Mutation
Cell research tells us how the cell works to keep the body healthy and what diseases cause if they do not work correctly. Cell biological studies show cells' structure, growth, reproduction, and death. The Main idea of the research on Beta-glucosidase (BglB) mutation is how we can generate reliable data about genes and enzyme functions to improve the AI model. Computational tools to predict enzyme stability and catalytic efficiency are a growing method in protein engineering. To improve the predictive accuracy of enzyme modeling software, many data are needed to train the algorithms. The enzyme variants were modeled with FoldIT software, built using Kunkel Mutagenesis methods in Escherichia coli, and the purified proteins were tested for kinetic activity. Adding these mutations to the Design2Data (D2D) Course-based Undergraduate Research Experience database contributes to an improved understanding of the structure-function relationship of ß-glucosidase B. It expands the potential for improving the accuracy of computational modeling tools for protein design. BglB is a suitable model enzyme and easy to mutate. I have researched the BglB mutation A236C. My mutagenesis is successful. After successful mutagenesis, I used BL21 E coli bacteria to make my mutation protein. I will be purifying my mutant enzyme using immobilized metal affinity chromatography. This process isolates the BglB enzyme from all the other cell debris. After the purified enzyme, I will check the stability of my mutant enzyme and at what temperature the enzyme can work or cannot function correctly.
Comments
The faculty mentor for this project was Heather Seitz, Biology.