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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 by the GRAPE strategy

2019 28 citations ? Citation count from OpenAlex, updated daily. May differ slightly from the publisher's own count. Score: 30 ? 0–100 AI score estimating relevance to the microplastics field. Papers below 30 are filtered from public browse.
Yinglu Cui, Ruchira Mitra, Yinglu Cui, Chunli Li, Yinglu Cui, Yanchun Chen, Yanchun Chen, Yanchun Chen, Xinyue Liu, Hua Xiang, Yanchun Chen, Xinyue Liu, Yanchun Chen, Saijun Dong, Haiyan Liu, Jing Han, Saijun Dong, Saijun Dong, Saijun Dong, Yue Tian, Yue Tian, Yuxin Qiao, Yuxin Qiao, Yuxin Qiao, Yuxin Qiao, Ruchira Mitra, Ruchira Mitra, Jing Han, Jing Han, Chunli Li, Chunli Li, Chunli Li, Chunli Li, Xu Han, Xu Han, Weidong Liu, Quan Chen, Quan Chen, Wenbin Du, Shuang‐Yan Tang, Wenbin Du, Hua Xiang, Haiyan Liu, Shuang‐Yan Tang, Hua Xiang, Bian Wu Haiyan Liu, Bian Wu

Summary

Researchers engineered a more stable version of the enzyme PETase, which breaks down PET plastic, using a computational protein design strategy. The improved enzyme could enable more efficient industrial biodegradation of PET plastic waste, including microplastics.

Polymers

Abstract The excessive use of plastics has been accompanied by severe ecologically damaging effects. The recent discovery of a PETase from Ideonella sakaiensis that decomposes poly(ethylene terephthalate) (PET) under mild conditions provides an attractive avenue for the biodegradation of plastics. However, the inherent instability of the enzyme limits its practical utilization. Here, we devised a novel computational strategy (greedy accumulated strategy for protein engineering, GRAPE). A systematic clustering analysis combined with greedy accumulation of beneficial mutations in a computationally derived library enabled the design of a variant, DuraPETase, which exhibits an apparent melting temperature that is drastically elevated by 31 °C and strikingly enhanced degradation performance toward semicrystalline PET films (23%) at mild temperatures (over two orders of magnitude improvement). The mechanism underlying the robust promotion of enzyme performance has been demonstrated via a crystal structure and molecular dynamics simulations. This work shows the capabilities of computational enzyme design to circumvent antagonistic epistatic effects and provides a valuable tool for further understanding and advancing polyester hydrolysis in the natural environment.

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