AI-driven approaches to developing a 'nylonase' enzyme for recycling waste plastics

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Moving towards a circular economy requires rapid progress to develop energy efficient new technologies for recycling waste plastics. In this project we take a new approach to develop enzymes for nylon degradation, using a combination of computational biology and directed evolution. Using primary protein sequence data we will build and screen 3D-computational models (AlphFold2) of known and previously unexplored hydrolase enzymes for appropriate substrate binding sites and catalytic architecture. Docking of nylon oligomers with these models will allow identification of the best starting points for new enzymes that bind nylon and stabilise the transition state for amide bond cleavage. Molecular dynamics simulations will help assess substrate binding and the spacial arrangement for key interactions in the active site to facilitate efficient catalysis. The predicted enzymes will be expressed and tested on model polyamides and on post-consumer nylon to provide validation and further refinement of the models. Successful variants will identify key residues that may be further mutated to find optimal combinations of mutations to provide the required 'nylonase' activity.