Back

Improvement of thermostability and activity of PET-degrading enzyme Cut190 towards a detailed understanding and application of the enzymatic reaction mechanism

Numoto, N.; Kamiya, N.; Oda, M.

2023-02-27 biochemistry
10.1101/2023.02.26.529345 bioRxiv
Show abstract

Enzymes capable of hydrolyzing polyethylene terephthalate (PET) and other plastics are attractive catalysts for application to the recycling of plastic waste due to their generally low environmental impact. Cut190 is a cutinase from a thermophilic actinomycete and shows PET-degrading activity and high thermal stability. We developed a series of Cut190 mutants exhibiting further improvements in thermal stability and activity, and showed that the unique stabilization and activation mechanism was dependent on Ca2+ ions. Two of these mutants, Cut190** and Cut190*SS, differed from the previous mutant Cut190* by deletion of the three C-terminal residues and introduction of five substitutions, including two cysteines forming a disulfide-bond, respectively. These mutants exhibit higher thermal stability and activity, which are often mutually exclusive characteristics. Crystallographic studies of these mutants and their inactivated derivatives demonstrated that they could have a novel ejecting form that would be responsible for releasing products. We also determined the crystal structures of ligand-bound complexes, which revealed the molecular mechanisms of the aromatic-ring recognition and the tetrahedral intermediate during the substrate cleaving, although the ligands had no aromatic ring but a cyclic group. This structural information provides insights into the mechanism of the Ca2+ -dependent PET-cleaving activity of Cut190 and provides a useful basis for further mutant design and computational studies.

Matching journals

The top 4 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.