Non-Canonical Amino Acid Mutagenesis of Position B28 In Insulin with Proline Analogs

Abstract

Insulin is a protein hormone that is crucial for maintaining the concentration of blood glucose in vivo and is used clinically as a drug for the treatment of diabetes. Chapter I provides for an overview and background on the state of the art in insulin treatment of diabetes and the many attempts, over 95 years, to improve the pharmaceutically relevant properties of insulin and improve our understanding of the model globular protein. Chapter II demonstrates the incorporation of hydroxyproline analogs into insulin and shares the discovery of insulin with enhanced stability and an accelerated kinetic rate of dissociation. We also provide the highest resolution structure deposited in the PDB of insulin in the T2 state and the 3rd highest of any insulin to date. Chapter III extends the incorporation of proline analogs in insulin to include fluorinated insulins. We also provide, for the first time, high-resolution structures of a single globular protein systematically mutated with all possible stereoisomers of fluorination at the 4-position on a single proline residue (4S, 4R, di-substituted). Chapter IV extends the incorporation of proline analogs in insulin to include ring variant analogs. We also provide, for the first time, high-resolution structures of globular proteins containing pipecolic acid, azetidine-2-carboxylic acid and 3,4 dehydroproline in the polypeptide chain. Chapter V discusses the significance of the findings described herein and discusses future directions to undertake in further engineering insulin for improved characteristics. This thesis describes a systematic approach, akin to medicinal chemistry, of altering a particular protein side chain by atomistic changes. I hope that the breadth of different amino acids incorporated into a single globular protein combined with the structural, functional, thermodynamic and kinetic information contained within this set of mutants will provide future protein engineers, computational protein designers and proline enthusiasts with a wealth of new information to be used to improve our understanding of proteins and predictive power.