Harnessing Protein Language Model for Structure-Based Discovery of Highly Efficient and Robust PET Hydrolases

Plastic waste, particularly polyethylene terephthalate (PET), poses significant environmental challenges, prompting extensive research into enzymatic biodegradation. However, existing PET hydrolases (PETases) are constrained to a narrow sequence space and exhibited limited performance for effective biodegradation. This study introduces a protein discovery pipeline, ProMine, which integrates protein language models (PLMs) with a representation tree to identify PETase based on structural similarity using sequence information. Using the crystal structure of IsPETase as a template, we employed ProMine to search for and cluster target proteins. PETase candidates were further screened using PLM-based assessments of solubility and thermostability, leading to the selection of 34 proteins for biochemical experiments. The results showed that 14 candidates exhibited PET degradation activity across a temperature range of 30-60 ℃. Notably, we identified a PET hydrolase from Kibdelosporangium banguiense (KbPETase), which has a melting temperature 32 ℃ higher than that of IsPETase and exhibits the highest PET degradation activity within 30-60 ℃ compared to other wild-type PETases. KbPETase also shows higher catalytic efficiency than FastPETase. X-ray crystallography and molecular dynamics simulations revealed that KbPETase has a conserved catalytic domain and enhanced intramolecular interactions, contributing to its improved functionality and thermostability. This work demonstrates a novel deep learning approach for discovering natural PETases with enhanced properties.