Computational design of a cutinase for plastic biodegradation by mining molecular dynamics simulations trajectories

Polyethylene terephthalate (PET) has caused serious environmental concerns but could be degraded at high temperature. Previous studies show that cutinase from Thermobifida fusca KW3 (TfCut2) is capable of degrading and upcycling PET but is limited by its thermal stability. Nowadays, Popular protein stability modification methods rely mostly on the crystal structures, but ignore the fact that the actual conformation of protein is complex and constantly changing. To solve these problems, we developed a computational approach to design variants with enhanced protein thermal stability by mining Molecular Dynamics simulation trajectories using Machine Learning methods (MDL). The optimal classification accuracy and the optimal Pearson correlation coefficient of MDL model were 0.780 and 0.716, respectively. And we successfully designed variants with high DTm values using MDL method. The optimal variant S121P/D174S/D204P had the highest DTm value of 9.3 degrees C, and the PET degradation ratio increased by 46.42-fold at 70., compared with that of wild type TfCut2. These results deepen our understanding on the complex conformations of proteins and may enhance the plastic recycling and sustainability at glass transition temperature. (C) 2022 The Author(s). Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology.

Automated summary

This study developed a computational approach using machine learning methods to design variants with enhanced protein thermal stability by mining molecular dynamics simulation trajectories. The optimal variant showed a significant increase in PET degradation efficiency, which can contribute to improving plastic recycling and sustainability in high-temperature environments.