Data-Driven Engineering of Therapeutic Enzymes Open Access

Muthu, Pravin J. (2015)

Permanent URL: https://etd.library.emory.edu/concern/etds/zp38wc749?locale=en
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Abstract

While advances in biotechnology have enabled enzymes to make significant contributions to industrial catalysis and synthetic biology, this dissertation focuses on an often overlooked application: enzymes as therapeutics. As pharmaceutical agents, the catalytic aspect of enzymes enables novel modes of action not possible with conventional treatments. Separately, enzyme biosensors have advantages over traditional analytical techniques, in particular: selectivity, affordability and ease-of-use. Natural enzymes are often not ideal for therapy, but enzyme design can introduce catalytic properties far beyond their native function, enabling new frontiers in medicinal and diagnostic chemistry. The first chapter describes recent efforts to adapt advances in biotechnology to therapeutic enzymes, providing context for the original work presented. The following two chapters focus on the development of a non-invasive reporter gene and use different data-driven approaches, improving function through iterative data generation. A fully realized reporter system will provide clinical data for future cell and gene therapies by monitoring transgene expression and migration. The second chapter describes the use of structural calculations to design human deoxycytidine kinase to have preferential activity for an unnatural L-ribose probe over the native D-deoxynucleosides. The third chapter continues development using an alternate statistical approach to optimize gene sequences. The resulting reporter gene candidates display exquisite in vitro performance and are currently being evaluated within cell and animal models. The fourth chapter shifts focus to therapeutic dosage monitoring (TDM) which is the clinical practice of measuring drug concentrations within a patient's bloodstream to optimize dosing regimens. Instead of a single enzyme, this data-driven algorithm deconvolutes kinetic observations from several enzymes to quantify multiple native and drug metabolites directly from complex biological samples. This modular detection strategy can readily translate to a variety of analytical applications. The fifth and final chapter provides commentary about the work presented and closes with a perspective on the applied sciences.

Table of Contents

Table of Contents

Chapter 1: Introduction 1 1.1 Overview of Therapeutic Enzymes 2 1.1.1 Pharmaceutical Proteins 4 1.1.2 Gene-Directed Enzyme Prodrug Therapy 6 1.1.3 Diagnostic Enzymes 8 1.2 Proteases 10 1.2.1 Plasminogen Activators 10 1.2.2 Thrombin 14 1.2.3 Procoagulant 15 1.2.4 Digestive Proteases 17 1.2.5 Other indications 18 1.3 Deoxynucleoside Kinases 19 1.3.1 Herpes Simplex Virus type 1 Thymidine Kinase 22 1.3.2 Drosophila melanogaster Deoxynucleoside kinase 24 1.3.3 H. sapiens Deoxycytidine Kinase 26 1.3.4 H. sapiens Thymidine Kinase Type 2 28 1.4 Cytosine Deaminases 29 1.5 Purine Nucleoside Phosphorylases 31 1.6 Nitroreductases 32 1.7 Cholinesterases 33 1.7.1 Butyrylcholinesterase 34 1.7.2 Acetylcholinesterase 35 1.7.3 Other Esterases 37 1.8 References 39 Chapter 2: Computational Design of Deoxycytidine Kinase 56 2.1 Introduction 57 2.2 Results and Discussion 59 2.3 Methods 70 2.3.1 Materials 70 2.3.2 Computer Models 71 2.3.3 Site Directed Mutagenesis 71 2.3.4 Protein Expression and Purification 72 2.3.5 Enzyme Kinetics 73 2.4 References 74 Chapter 3: Design-of-Experiments for Deoxycytidine Kinase 80 3.1 Introduction 82 3.2 Results and Discussion 85 3.2.1 Round 1 85 3.2.2 Round 2 91 3.2.3 Round 3 93 3.2.4 Experimental Deconvolution 96 3.3 Conclusion 99 3.4 Material and Methods 100 4.4.1 Library Design 100 3.4.2 Library Construction and Evaluation 101 3.4.3 Enzyme Characterization 102 3.5 References 103 Chapter 4: Multi-Biosensor Detection of Nucleoside Analogs 108 4.1 Introduction 110 4.2 Results and Discussion 112 4.2.1 Multi-biosensor Design 112 4.2.2 Data Processing 114 4.2.3 Single Component Evaluation 117 4.2.4 Binary Mixture Evaluation 119 4.2.5 Blood Plasma Quantification 122 4.2.6 Point-of-Care Detection 123 4.3 Conclusion 125 4.4 Methods 125 4.4.1 Chemicals and Reagents 125 4.4.2 Protein Expression and Purification 126 4.4.3 Enzyme Characterization 126 4.4.4 Datasets 127 4.4.5 Cell-Phone Based Detection 128 4.5. References 130 Chapter 5: Final Thoughts 136 5.1 Computational Enzyme Engineering 137 5.2 Design-of-Experiments 138 5.3 Reporter Systems 140 5.4 Enzyme Biosensors 141 5.5 General Perspective 143 5.6 References 145

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