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Abstract
Controlling thermodynamic stability is a challenge in protein engineering. Engineering a protein by introducing mutations into its sequence to increase efficacy can destabilize the protein thermodynamically, preventing it from functioning in its intended environment. Having a rapid computational method to accurately estimate the stability of protein mutants will accelerate the design process. A minimal Distance Constraint Model (DCM) has been extensively used in previous works to predict thermodynamic stability in proteins by employing network rigidity as an underlying long-range mechanical interaction between constituent parts of a protein. This novel graph-theoretical approach also provides stability-flexibility relationships in proteins to elucidate functional mechanisms. The minimal DCM invokes microenvironment-independent parameters for energy and entropy contributions due to atomic packing, and partially models the microenvironment variation in hydrogen bond interactions. Three phenomenological parameters were employed to compensate for model oversimplifications. A new DCM is developed in this thesis with three major extensions – the rigidity model for molecular interactions is generalized, the microenvironment for all molecular interactions are quantified through local molecular volume properties, and self-consistent constraint theory is implemented. This new DCM captures cold and heat denaturation, although the free energy basin for low temperature denaturation is not accessible. It is found that the template structure used to model intramolecular interactions must deviate significantly from the input crystal structure where volume information of the native protein fold is no longer relevant. Feasibility of this new DCM is demonstrated, and the current implementation is ready to be fully parameterized over a large experimental dataset.