Degradation of PET Nanoplastic Oligomers at the Novel PHL7 Target:Insights from Molecular Docking and Machine Learning


  • C. E. Duru Surface Chemistry and Environmental Technology (SCENT) Research Group, Department of Chemistry, Imo State University, Owerri, PMB 2000, Imo State, Nigeria.
  • C. E. Enyoh Graduate School of Science and Engineering, Saitama University, 255 Shimo-Okubo, Sakura-ku, Saitama City, Saitama 338-8570, Japan.
  • I. A. Duru Department of Chemistry, Federal University of Technology Owerri, PMB 1526, Imo State Nigeria.
  • M. C. Enedoh Department of Chemistry, Imo State University, Owerri, PMB 2000, Imo State, Nigeria.


Polyethylene terephthalate, Nanoplastic, Polyester Hydrolase Leipzig 7, binding affinity, Artificial Neutral Network


The versatility of Polyethylene terephthalate (PET) as a material with numerous applications in the food industry and its recalcitrance to chemical and microbial degradation has recently made it an environmental nuisance. In this study, we applied computational methods to ascertain the dependence of PET nanoplastic (NP) degradation on the chain length of the oligomer. The binding affinities of the NPs on the novel enzyme Polyester Hydrolase Leipzig 7 (PHL7) were used to relate their ease of degradation at the enzyme active site. The results revealed that the binding affinity of PET NPs at the enzyme target decreased from -5.2 kcal/mol to -0.8 kcal/mol, with an increase in PET chain length from 2.18 nm to 5.45 nm (2-5 PET chains). The binding affinities became positive at chain lengths 6.54 nm (6 PET chains) and above. These findings indicated that PET NP degradation at this enzyme’s active site is most efficient as chain length decreases from 5-2 units and is not likely to occur at longer PET chains. A feedforward Artificial Neutral Network (ANN) analysis predicted that the energy of the PET NPs is a very important factor in its degradation.


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How to Cite

Duru, C., Enyoh, C., Duru, I. A., & Enedoh, M. C. (2023). Degradation of PET Nanoplastic Oligomers at the Novel PHL7 Target:Insights from Molecular Docking and Machine Learning. Journal of the Nigerian Society of Physical Sciences, 5(1), 1154.



Original Research