Major
Biology
Anticipated Graduation Year
2025
Access Type
Open Access
Abstract
Our work is part of a research project led by the Seigel lab at University of California–Davis called Design to Data. Collaborating with undergraduate labs across the country, the goal of this project is to create large datasets that will be able to train and improve the predictive abilities of artificial intelligence protein modeling software. For our project, we generated a mutant of the enzyme β-glucosidase (BglB), characterized it, and uploaded our data to the national database. As specific amino acid mutations are characterized, the database will be able to improve the understanding of mutational effects in proteins. β-glucosidase is a critical catalytic enzyme in the pathway for cellulose digestion.1 We hypothesized that our mutant enzyme, with a proline to leucine mutation at position 41, will demonstrate slightly decreased catalytic efficiency and decreased thermal stability in comparison to the wild type protein. Using Foldit, a graphic user interface, to score the protein suggests an acceptable likelihood of expression, with an increased total energy of 2.921 units, and no visible changes in the intermolecular forces. This energy increase also suggests that the protein will be slightly less stable and therefore slightly less active. In order to test our hypothesis, a kinetic assay was ran to determine the function of the enzyme post-mutation. This characterization will allow for further overall understanding of the enzyme β-glucosidase.
Community Partners
National Science Foundation, Ashley Vater and Justin Seigel Lab at UC-Davis
Faculty Mentors & Instructors
Ashley Vater and the Justin Siegel Lab at UC-Davis.
Supported By
Dr. Emma Feeney, PhD, Department of Biology
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License.
Characterization of Enzyme Function After Proline to Leucine Amino Acid Mutation at Location 41 in β-Glucosidase Enzyme
Our work is part of a research project led by the Seigel lab at University of California–Davis called Design to Data. Collaborating with undergraduate labs across the country, the goal of this project is to create large datasets that will be able to train and improve the predictive abilities of artificial intelligence protein modeling software. For our project, we generated a mutant of the enzyme β-glucosidase (BglB), characterized it, and uploaded our data to the national database. As specific amino acid mutations are characterized, the database will be able to improve the understanding of mutational effects in proteins. β-glucosidase is a critical catalytic enzyme in the pathway for cellulose digestion.1 We hypothesized that our mutant enzyme, with a proline to leucine mutation at position 41, will demonstrate slightly decreased catalytic efficiency and decreased thermal stability in comparison to the wild type protein. Using Foldit, a graphic user interface, to score the protein suggests an acceptable likelihood of expression, with an increased total energy of 2.921 units, and no visible changes in the intermolecular forces. This energy increase also suggests that the protein will be slightly less stable and therefore slightly less active. In order to test our hypothesis, a kinetic assay was ran to determine the function of the enzyme post-mutation. This characterization will allow for further overall understanding of the enzyme β-glucosidase.