Unlocking the Potential for Microplastic Degradation: In Silico Mutagenesis and Molecular Dynamics Simulations of PET-Petase and MHET-Mhetase Interactions by Reverse Screening

Abstract

Plastic pollution remains a growing global concern, requiring efficient and sustainable solutions. Enzymes such as PETase and MHETase from Ideonella sakaiensis offer natural mechanisms for plastic degradation but need further enhancement for large-scale applications. This study aimed to improve the structural stability and substrate-binding performance of PETase and MHETase by engineering their homologous variants through in silico methods. Homologous proteins were identified via multiple sequence alignment, and functionally relevant residues near the active site were selected for site-saturation mutagenesis. The resulting variants were evaluated through molecular docking to assess binding energy and interaction profiles, while molecular dynamics (MD) simulations examined their behavior over time using RMSD, and RMSF. Among the MHETase variants, GLY103ALA (–9.5 ± 0.2 kcal/mol), PRO44PHE (–8.4 ± 0.1 kcal/mol), and GLY165HIS (–8.2 ± 0.1 kcal/mol) showed significantly enhanced ligand-binding affinity compared to wild type (–6.3 ± 0.1 kcal/mol). These variants also showed improved dynamic stability, as indicated by reduced ligand RMSD values (GLY103ALA: 0.22 ± 0.01 nm; PRO44PHE: 0.24 ± 0.01 nm; GLY165HIS: 0.25 ± 0.01 nm) compared to the wild type (0.36 ± 0.02 nm), along with stable backbone RMSF values (~0.10 ± 0.01 nm) relative to the wild type (0.18 ± 0.02 nm). For PETase, the GLY35GLN variant demonstrated the most favorable performance, with a lower binding energy (–6.0 ± 0.1 kcal/mol) compared to the wild type (–5.2 ± 0.1 kcal/mol). It also exhibited the lowest ligand RMSD (0.28 ± 0.02 nm) compared to wild type (0.41 ± 0.03 nm), and showed reduced structural fluctuation in RMSF (0.20 ± 0.01 nm) relative to wild type (0.26 ± 0.02 nm), indicating enhanced dynamic stability. These results suggest that even minor mutations can significantly improve enzymatic efficiency in microplastic (MP) degradation. Overall, the results show that in silico mutagenesis and simulation can help find enzyme variants with better binding and stability, making them good candidates for future lab testing and possible use in breaking down microplastic waste.