Chen Xuan, Li Xiaoya, Yang Yu
School of Life Science, Beijing Institute of Technology, Beijing 100081, PR China.
School of Life Science, Beijing Institute of Technology, Beijing 100081, PR China.
Int J Biol Macromol. 2025 Sep;321(Pt 4):146581. doi: 10.1016/j.ijbiomac.2025.146581. Epub 2025 Aug 5.
The mining of novel plastic-degrading enzymes is imperative for the development of enzymatic degradation and recycling strategies for plastic waste. Here, a cutinase-like enzyme (MhCulp3) was identified for polyester-polyurethane (PU) degradation from a compost metagenome in virtue of protein structure clustering. The recombinant MhCulp3 was expressed in Escherichia coli with the pET-28a vector, possessing optimal activity at 30 °C, pH 8.0 against p-nitrophenyl-hexanoate (pNPH, C). The results of Fourier Transform Infrared (FTIR) spectroscopy, scanning electron microscopy (SEM), and liquid chromatography-tandem mass spectrometry (HPLC-MS) demonstrated that MhCulp3 exhibited activity towards PU emulsions (Impranil®DLN-SD), PU films (PCL-MDI), and PU foams (PEGA-TDI) by cleaving ester bonds in soft segments rather than urethane bonds in hard segments. Additionally, MhCulp3 could not hydrolyze the natural substrate cutin based on the results of gas chromatography-mass spectrometry (GC-MS). Molecular docking and site-directed mutagenesis of MhCulp3 revealed the substrate binding model and catalytic mechanism. Taken together, this study substantiates the reliability of AI-assisted structure clustering strategy in the mining of plastic-degrading enzymes, and provides a novel biocatalyst for enzymatic degradation and recycling of polyester-PU waste.