Exploring the Reaction Mechanism of Polyethylene Terephthalate Biodegradation through QM/MM Approach

Author(s)

dos Santos, Alberto M. ; da Costa, Clauber H. S. ; Silva, Pedro H. A. ; Skaf, Munir S. ; Lameira, Jeronimo

• Document type: Journal article
• Source: Journal of Physical Chemistry B; v. 128, n. 31, p. 14-pg., 2024-07-29.

Abstract

The enzyme PETase fromIdeonella sakaiensis (IsPETase) strain 201-F6 can catalyze the hydrolysis of polyethylene terephthalate (PET), mainly converting it into mono(2-hydroxyethyl) terephthalic acid (MHET). In this study, we used quantum mechanics/molecular mechanics (QM/MM) simulations to explore the molecular details of the catalytic reaction mechanism of IsPETase in the formation of MHET. The QM region was described with AM1d/PhoT and M06-2X/6-31+G(d,p) potential. QM/MM simulations unveil the complete enzymatic PET hydrolysis mechanism and identify two possible reaction pathways for acylation and deacylation steps. The barrier obtained at M06-2X/6-31+G(d,p)/MM potential for the deacylation step corresponds to 20.4 kcal/mol, aligning with the experimental value of 18 kcal/mol. Our findings indicate that deacylation is the rate-limiting step of the process. Furthermore, per-residue interaction energy contributions revealed unfavorable contributions to the transition state of amino acids located at positions 200-230, suggesting potential sites for targeted mutations. These results can contribute to the development of more active and selective enzymes for PET depolymerization. (AU)

• FAPESP's process: 13/08293-7 - CCES - Center for Computational Engineering and Sciences
• Grantee: Munir Salomao Skaf
• Support Opportunities: Research Grants - Research, Innovation and Dissemination Centers - RIDC
• FAPESP's process: 22/04703-5 - Molecular dynamics simulations of enzymes of biotechnological interest
• Grantee: Clauber Henrique Souza da Costa
• Support Opportunities: Scholarships in Brazil - Post-Doctoral
• FAPESP's process: 22/04695-2 - Reaction mechanisms of enzymes and catalytic enhancement based on transition states and machine learning algorithms
• Grantee: Alberto Monteiro dos Santos
• Support Opportunities: Scholarships in Brazil - Post-Doctoral