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First, we start by adding the enzyme sequence to the pipeline, from which we aim to generate a very high quality 3D structure. Afterwards, we try to understand the enzyme’s “non optimal” properties, like “why does it have a low thermostability?” or “why does it have a low activity?”.

Once we have a clear view of the system, we detect the enzyme positions that could be improved (aka hotspots). Not only do we perform mutations in the enzyme’s active site, but also far away from it. We’re actually one of the few companies that take distal mutations into account!

After detecting the hotspots, we create the mutant library: for each position inside the enzyme, we select the amino acids we want to mutate until we end up with millions of “variants” or “mutants”.

After detecting the hotspots, we create the mutant library: for each position inside the enzyme, we select the amino acids we want to mutate until we end up with millions of “variants” or “mutants”.

Then, we model the structures to evaluate the mutants and select the best candidates, those that have the improvement that the client wants.

Then, we model the structures to evaluate the mutants and select the best candidates, those that have the improvement that the client wants.

In the end, we keep less than 100 mutants. Compare this to the thousands or even millions of mutants you could have by doing traditional Directed Evolution, and it’s clear why this is a great time & resource-saving method for enzyme engineering!