The drylab team improved upon a machine learning algorithm for protein design, CbAS, by considering the probability the generated sequences will fold into a PETase-like backbone as well as validating designs using ligand docking and molecular dynamics simulations.

The wetlab team used the Rosetta energy function and the PROSS and FuncLib design strategies to generate hundreds of potential designs to be tested experimentally in iGEMTO 2021. They also simulated a bioreactor to be implemented next year.

The policy and practices team reached out to stakeholders in the plastics recycling industry and learned that textile waste is currently a undervalued problem. Working with students from Rotman, they identified bottlenecks in scaling bio-recycling solutions and calculated the necessary catalytic rate of an enzyme for a bio-recycling startup to be profitable.