Control of Enzymatic Activity and Stability

ABSTRACT

The convergence of computational design and synthetic biology has unlocked new possibilities for creating functional protein systems with sophisticated regulatory controls. However, the development of next-generation biocatalysts requires the seamless integration of novel regulatory mechanisms, enhanced structural stability, and accelerated experimental pipelines. This dissertation describes the engineering of biological logic gates based on phosphorylation-responsive split-TEV protease switches, the enhancement of enzyme thermostability through the incorporation of cysteine-crosslinking unnatural amino acids (UAAs), and the implementation of an ultra-high-throughput protein production platform. The introductory chapter establishes the current demand for enzymes with tailorable activity and improved stability. It reviews the limitations of traditional engineering approaches and highlights the urgent need for high-throughput production and screening workflows to keep pace with contemporary AI-based protein design tools. iii The second chapter details the construction of proteolytic-based biological logic gates. By developing a phosphorylation-based split-TEV switch characterized by a high dynamic range and minimal background activity, and integrating it with a rapamycin-inducible system, we engineered novel AND and OR gates. These molecular circuits are capable of processing complex biochemical signals, specifically detecting phosphorylation states and the presence or absence of small-molecule inputs. The third chapter focuses on utilizing cysteine-crosslinking UAAs to enhance the structural stability of yeast cytosine deaminase (yCD). We employed the state-of-the-art deep learning tool ProteinMPNN to identify optimal positions for UAA-mediated stapling of the protein backbone. By crosslinking the termini of the enzyme, we successfully reduced the conformational entropy of the folded state, leading to significantly higher resistance to thermal unfolding. Furthermore, we utilized ProteinMPNN to optimize the residues surrounding these staples and throughout the protein scaffold, achieving a critical balance between enhanced thermostability and the preservation of catalytic activity. To address the bottleneck of experimental validation, the final portion of this work presents a high-throughput protein production pipeline. This system enables the expression and purification of dozens of proteins directly from gene fragments within a 24- to 48-hour window, providing a robust framework for the rapid characterization of de novo designed proteins. Ultimately, this dissertation provides a multifaceted approach to protein engineering that bridges the gap between sophisticated computational design and rapid experimental execution. By integrating inducible molecular switches, AI-driven stability enhancements, and accelerated production workflows, this work establishes a robust framework for developing the next generation of functional, stable, and highly regulated biocatalysts.