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Atomistic-Level Insights into the Role of Mutations in the Engineering of PET Hydrolases: A Systematic Review

Karaoli A, Tzoupis H, Papavasileiou KD, Papadiamantis AG, Mintis DG, Kiranoudis CT, Lynch I, Melagraki G et al.

International journal of molecular sciences, 2025

2 citations

Abstract

Plastic pollution is a growing global challenge, and traditional plastic waste management methods are proving inadequate in tackling the issue. Enzymatic biodegradation has emerged as a promising alternative or addition to plastic waste management due to its environmentally friendly profile. Polyethylene terephthalate (PET) is among the most widely used polymers in packaging, and recent research has identified several PET-degrading enzymes, such as TfCut2, IsPETase, and LCC, as promising candidates for biodegradation applications at the industrial level. This has led to extensive efforts to improve their catalytic efficiency, with targeted mutagenesis being the preferred method employed for their modification. To this end, molecular dynamics (MD) simulations coupled with experimental validation have provided critical atomistic-level insights into the effect of mutations on enzymatic function. The present systematic review examines the role of mutations in determining enzymatic activity and thermostability, analyzing their structural and mechanistic contributions across 20 studies. The integration of MD simulations and experimental findings allows elucidation of the mechanistic details governing polymer degradation, as well as identification of key residue and enzyme hotspots that enhance catalytic performance. The review further highlights the role of MD simulations as powerful tools in providing valuable insights to guide targeted mutations for enzyme efficiency…

How strong is the evidence?

Evidence Hub reads this as a systematic review, done in computer models (in silico), not in people. Its evidence level is 5 of 5 (Very low · case / narrative / animal / in vitro / in silico). Case reports, narrative reviews, and animal, cell or computer-model studies give the weakest evidence for human health.

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