Discovering Novel PETase Enzymes for Enhanced PET Plastic Degradation Using in Silico Approaches
Patil, B.; Attar, A.; Kumar, A.; Giri, S.
Show abstract
Accounting for 12% of global solid waste, Poly (ethylene terephthalate) (PET) is one of the most abundantly produced synthetic polymers. While PET offers substantial commercial benefits, its widespread use has led to disproportionate environmental hazards due to its resistance to degradation. To address this problem, several solutions have been proposed, including enzymatic degradation via PETase, MHETase, and Cutinase. Among these, PETase exhibited significant PET-degrading activity. However, the application of PETase has been hampered by its lack of robustness to pH, temperature ranges, and slow reaction rates. Hence, it has become novel enzymes that can overcome these limitations and function efficiently. In this study, we utilised an integrated in silico bioinformatics pipeline to identify and characterise novel PETase candidates from the Thermophilic actinobacteria Thermobifida cellulosilytica and Thermobifida halotolerans species. The PlasticDB database contains 228 plastic-degrading enzyme sequences. In which PETase (00188) is significantly homologous with two putative proteins, Hydrolase (ALF00495.1) and hypothetical protein (WOZ56011.1). The discrete optimized protein energy (DOPE) scores, stereochemical assessments, and homology modeling results closely mirrored our findings for both proteins, supporting their structural stability. The molecular dynamics simulations revealed that the putative ALF00495 variant exhibited more extensive and robust hydrogen-bonding networks, enhanced conformational stability, and increased structural compactness compared to the reference enzyme. The present in silico investigation underscores the potential of putative ALF00495 as a highly effective PETase biocatalyst for polyethylene terephthalate (PET) degradation. Collectively, these findings illustrate the utility of computational approaches of novel PET-degrading enzymes, thereby facilitating the development of sustainable biotechnological strategies to mitigate global plastic pollution.
Matching journals
The top 8 journals account for 50% of the predicted probability mass.