Developing Methods to Assess Enzyme Stability in Liquid Laundry Detergents

Abstract

This thesis presents work focused on developing established protein analysis methods for use in studying enzyme inactivation in laundry detergent systems. In a multi-billion dollar per year industry, basic, labour intensive procedures still dominate commercial stability studies, with extensive storage tests and activity assays remaining the industry standard. These methods are both inefficient and provide little insight into inactivation processes, leading to a 'trial and error' approach to product development. This slows the introduction new formulations and enzyme variants to the market. Furthermore, a valuable opportunity is being missed, harnessing available resources in the detergent industry to advance both protein analysis technologies and understanding of protein denaturation processes.
Transfer from these basic, low throughput methods to those favoured by other protein-focused industries has been hindered by sample complexity and the presence of high concentrations of the surfactant, LAS. In this work, two novel approaches to enzyme analysis in LAS-rich media will be presented. The first employing an analogous surfactant, SDS, which yields similar effects on protein stability but does not affect UV detection, and the second, exploiting the irreversible nature of detergent enzyme unfolding to enable manipulation of formulations to within instrument specifications. These approaches will allow for incorporation of ultra-high throughput screening methods, such as DSF, as well as techniques which provide further insight into protein unfolding processes, such as CD, to the available suite of analytical techniques.
Thermal data arising from this work were compared with rates of degradation obtained through conventional storage tests. Empirical fittings suggest a linear relationship between Tm values and long-term storage stability, enabling the use of thermal analysis as a tool for prediction of degradation rates. Further work is required to refine these models, however, before expanding to more complex systems.