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

Qingbin Li; Yi Zheng; Tianyuan Su; Qian Wang; Quanfeng Liang; Ziding Zhang; Qingsheng Qi; Jian Tian

doi:10.1016/j.csbj.2021.12.042

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

Comput Struct Biotechnol J. 2022 Jan 5:20:459-470. doi: 10.1016/j.csbj.2021.12.042. eCollection 2022.

Authors

Qingbin Li^{1

2

3}, Yi Zheng¹, Tianyuan Su¹, Qian Wang¹, Quanfeng Liang¹, Ziding Zhang³, Qingsheng Qi¹, Jian Tian²

Affiliations

¹ State Key Laboratory of Microbial Technology, Shandong University, Qingdao 266237, China.
² Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China.
³ State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing 100193, China.

Abstract

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 ΔT _m values using MDL method. The optimal variant S121P/D174S/D204P had the highest ΔT _m value of 9.3 °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.

Keywords: Machine Learning; Molecular Dynamics Simulations; PET; TfCut2; Thermostability.