Enzyme engineering is a powerful technology now widely used in laboratories around the world. The goal is to obtain improved proteins that will serve as better biocatalysts, biosensors or as a tool to understand protein evolution, which is at the core of many societal problem, such as antibiotic resistance. Enzyme engineering entails the initial generation of libraries of mutants that will then be screened for improved properties (for example: better selectivity or specificity towards a substrate of interest, improved stability, etc). Obtaining good quality libraries is becoming routine thanks to the availability of molecular biology tools and computational simulations. The bottleneck of the experiment remains at the screening level. The bigger the generated library, the more stringent the requirement for the high-throughput screen. 
In our work we tackle both aspects: library generation and screening throughput by using the enzyme Candida antarctica lipase A (Cal-A) as a model. Cal-A has the potential to be applied in the food industry for the hydrolysis of saturated short-chain fatty acids (C4 and C6) vs. saturated C10-C16 fatty acids - a very useful tool for the dairy industry. Upon generation of several libraries of mutants using a novel Golden Gate-based strategy, we developed two screening methods (in vivo and in vitro) that give us the required high-throughput capacity. The two screening methods, one carried out on agar plates and the other in liquid, are completely automated and they are both optimized for our Biomek NX^P system.