Engineering hydrolases by growth-based selections
Enzymes are biological catalysts: proteins capable of accelerating chemical reactions with great precision. Hydrolases are enzymes that use water to break chemical bonds. Because they are easy to produce and do not rely on cofactors, they are particularly attractive for industrial applications. For instance, some hydrolases are used in laundry detergents to remove stains. However, many hydrolases are not immediately suitable for industrial applications; they act too slowly, have a limited substrate scope, or are unstable under process conditions.
To finetune and improve biomolecules like hydrolases, researchers make use of a three-step process known as directed evolution: (1) the generation of many enzyme variants; (2) the identification of the best variants ("hits"); (3) the amplification of these hits. Since thousands to millions of variants can easily be generated in the first step, the second step is considered the limiting factor; evaluating each enzyme variant one-by-one (screening) is very time-consuming. To address this issue, this thesis focuses on the development of growth-based selections for hydrolase engineering. Such selections link an enzyme's performance to the survival of its producing host, namely bacteria. This "survival of the fittest" principle mimics natural evolution in the laboratory and offers several advantages: selections are easy to use, require no specialized equipment, and are able to evaluate entire populations all-at-once. I applied these selection methods to evolve hydrolases suitable for various applications such as amino acid synthesis, plastic degradation, and the breakdown of organofluorine compounds.