UC Davis

Biotechnology has the potential to deliver solutions to many global problems in medicine, materials science, nutrition, agriculture, natural resource preservation, and energy. Engineered cells and enzymes can perform chemical transformations that are rare or unknown in nature, and even catalyze reactions not accessible to traditional synthetic chemistry while also operating at gentler, more environmentally friendly conditions. Decreases in the price of DNA sequencing and synthesis has led to generation of vast databases that can be screened for any imaginable function. These sequence databases are even more powerful now due to the development of software to enable rapid generation of 3D protein structures, like AlphaFold2. However, tools to predict the function of these proteins or their performance in engineered cells are not yet robust, leading to long development times and limited successful applications to date. New tools and methods must be developed for the true potential of biotechnology to be unlocked.For my thesis, I explore metabolic pathway construction and screening, protein design, and protein sequence-structure-function relationships across broad contexts with the objective of tool and knowledge development for future efforts in biotechnology. My first chapter discusses the introduction of the triazine degradation pathway, of interest for remediation of contaminated sites, into E. coli and methods for characterizing pathway flux and identifying bottlenecks to guide engineering efforts and limit accumulation of metabolic intermediates. My second chapter focuses on methods for the design of supercharged proteins, which have many interesting potential applications, and parameters that increase the likelihood of successful design of these proteins. My third chapter regards the generation and characterization of a library of single point mutations in the enzyme B-glucosidase B and use of kinetic data to predict the effects of changes in sequence on enzyme function. These seemingly disparate topics all serve to improve tools for protein screening, production, functional prediction, and application, addressing several gaps toward improved development timelines and success rates for biocatalysts.