Enzymes are molecules that exist in Nature and that catalyze chemical reactions. They are highly specific to bind to their substrates and very efficient in their activities. They are perfect candidates for applications in which chemical reactions are needed and many of them are already used in different industries, from pharmaceuticals and diagnostics to food and cloth industries. Enzymes most of the time cannot be directly used in a different context as the natural one, or the desired outcome of the chemical reaction is not exactly the same as the natural one. Therefore, there is a large interest in engineering enzymes to adapt them to work in different conditions, to increase their efficiency, change their substrates or remove secondary activities.
Directed evolution has been proved to be an effective method for enzyme engineering: it has successfully increased the efficiency of some enzymes, adapted others to work in different conditions, and even changed their substrates. The process mimics natural evolution: it generates a set of diverse variants of the gene codifying for the enzyme and subjects this set to functional screening or selection in order to extract the best performing variants. These two steps are applied iteratively, leading to optimized variants of the enzyme. While it is an effective strategy, the underlying of the process remains unknown and the overall protocol is time consuming. Previous work has focused on increasing diversity and smarter strategies for selection and screening. In this project I focus on how the sequence space is explored during directed evolution experiments. I use an experimental platform that tests millions of variants of an enzyme simultaneously, and I incorporate next generation DNA sequencers to the overall protocol. This permits to have an insight on the effect of DNA mutations in the activity of the enzyme and it will aid to focus experimental efforts in those which have a stronger impact.
