Insilico mining of metagenomic datasets for novel PET hydrolase homologs: Enhancing enzyme discovery for circular bioeconomy

Abstract

Polyethylene terephthalate (PET) pollution presents a significant environmental challenge, necessitating innovative strategies for its degradation and recycling. This study investigates PET-degrading enzymes using a Hidden Markov Model (HMM) to analyse 92 amino acid sequences from the PAZy database. The research aims to uncover key structural features and sequence conservation across enzymes from diverse environmental biomes, thus advancing the understanding of PET hydrolysis mechanisms. Structural motifs such as the nucleophilic elbow (GXNXG), catalytic triad, oxyanion hole, and aromatic clamp were identified as critical for PET degradation. Sequence conservation was observed in enzymes from marine, sediment, composting, and wastewater environments, highlighting the widespread distribution of PET-degrading activity. Physicochemical analyses revealed variations in enzyme stability, with most enzymes exhibiting low thermostability. 3D structural modeling and molecular docking identified PETase2 and PETase4 enzymes as having high binding affinities for PET. These findings enhance the molecular understanding of PET degradation and provide insights for developing biotechnological solutions to manage plastic waste. The study underscores the potential of enzymatic processes for sustainable plastic recycling and bioremediation, contributing to the broader goal of advancing the circular bioeconomy.