Protein thermodynamics has been shown to be insightful and illuminating to the functional mechanisms behind biological function. By characterizing proteins and protein families according to their enthalpy and entropy we can make inferences to the relationship between specific residues or regions and the activity. Here I will leverage several existing and new computational methodologies on various families of biological importance. By combining molecular dynamics calculations with thermodynamics I will characterize the importance of disulfide bonds in CXCL7 proteins, which are a trademark characteristic of the Chemokine family. In addition I will use these calculations to approximate the dimerization energy. RiVax being a larger molecule is too computationally expensive to run MD simulations on the scale desired therefore I will create a new higher throughput methodology to better represent the conformational diversity of RiVax. Through better conformational sampling and quantitative stability/flexibility relationships I will elucidate why certain mutations enhance the stability of RiVax making it a more viable drug. Sleeping Beauty presents another set of challenges, as experimental data is scarce for this import link in the CRISPR technology chain. Therefore I will develop a methodology to predict thermodynamic properties of SB without the need for heat capacity data. I will accomplish this by comparing SB to its hyperactive mutation SB100 that is structurally very similar yet has difference in activity equal to 100 fold. These new methodologies will enhance the capacity of current methods and build the foundation for future students to develop further, and in time tackle problems outside the scope of current ability.