0
Article ? AI-assigned paper type based on the abstract. Classification may not be perfect — flag errors using the feedback button. Tier 2 ? Original research — experimental, observational, or case-control study. Direct primary evidence. Environmental Sources Remediation Sign in to save

Computational Redesign of a PETase for Plastic Biodegradation under Ambient Condition by the GRAPE Strategy

ACS Catalysis 2021 545 citations ? Citation count from OpenAlex, updated daily. May differ slightly from the publisher's own count. Score: 55 ? 0–100 AI score estimating relevance to the microplastics field. Papers below 30 are filtered from public browse.
Yinglu Cui, Ruchira Mitra, Yinglu Cui, Yinglu Cui, Chunli Li, Yanchun Chen, Yanchun Chen, Xinyue Liu, Yanchun Chen, Xinyue Liu, Hua Xiang, Haiyan Liu, Saijun Dong, Saijun Dong, Jing Han, Saijun Dong, Saijun Dong, Yue Tian, Yue Tian, Yuxin Qiao, Yuxin Qiao, Yuxin Qiao, Yuxin Qiao, Yong Liang, Ruchira Mitra, Ruchira Mitra, Jing Han, Jing Han, Chunli Li, Chunli Li, Chunli Li, Yong Liang, Chunli Li, Xu Han, Xu Han, Weidong Liu, Quan Chen, Quan Chen, Wenbin Du, Wangqing Wei, Wangqing Wei, Xin Wang, Shuang‐Yan Tang, Hua Xiang, Wenbin Du, Shuang‐Yan Tang, Haiyan Liu, Bian Wu Hua Xiang, Haiyan Liu, Yong Liang, K. N. Houk, Bian Wu

Summary

Researchers developed a computational protein engineering strategy called GRAPE to redesign a PET-degrading enzyme from Ideonella sakaiensis. The resulting DuraPETase variant showed a 31-degree-Celsius increase in thermal stability and over 300-fold improved degradation of PET films at mild temperatures, achieving complete biodegradation of 2 g/L microplastics into water-soluble products under ambient conditions.

Polymers

Nature has provided a fantastic array of enzymes that are responsible for essential biochemical functions but not usually suitable for technological applications. Not content with the natural repertoire, protein engineering holds promise to extend the applications of improved enzymes with tailored properties. However, engineering of robust proteins remains a difficult task since the positive mutation library may not cooperate to reach the target function in most cases owing to the ubiquity of epistatic effects. The main demand lies in identifying an efficient path of accumulated mutations. Herein, we devised a computational strategy (greedy accumulated strategy for protein engineering, GRAPE) to improve the robustness of a PETase from Ideonella sakaiensis. A systematic clustering analysis combined with greedy accumulation of beneficial mutations in a computationally derived library enabled the redesign of a variant, DuraPETase, which exhibits an apparent melting temperature that is drastically elevated by 31 °C and a strikingly enhanced degradation toward semicrystalline poly(ethylene terephthalate) (PET) films (30%) at mild temperatures (over 300-fold). Complete biodegradation of 2 g/L microplastics to water-soluble products under mild conditions is also achieved, opening up opportunities to steer the biological degradation of uncollectable PET waste and further conversion of the resulting monomers to high-value molecules. The crystal structure revealed the individual mutation match with the design model. Concurrently, synergistic effects are captured, while epistatic interactions are alleviated during the accumulation process. We anticipate that our design strategy will provide a broadly applicable strategy for global optimization of enzyme performance.

Sign in to start a discussion.

Share this paper