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Article ? AI-assigned paper type based on the abstract. Classification may not be perfect — flag errors using the feedback button. Tier 2 ? Original research — experimental, observational, or case-control study. Direct primary evidence. Nanoplastics Remediation Sign in to save

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 2023 9 citations ? Citation count from OpenAlex, updated daily. May differ slightly from the publisher's own count. Score: 40 ? 0–100 AI score estimating relevance to the microplastics field. Papers below 30 are filtered from public browse.
Christian Ebere Enyoh Christian Ebere Enyoh Christian Ebere Enyoh Christian Ebere Enyoh Christian Ebere Enyoh Christian Ebere Enyoh Christian Ebere Enyoh Christian Ebere Enyoh Christian Ebere Enyoh Christian Ebere Enyoh Christian Ebere Enyoh Chidi Edbert Duru, Chidi Edbert Duru, Chidi Edbert Duru, Christian Ebere Enyoh Christian Ebere Enyoh Christian Ebere Enyoh Christian Ebere Enyoh Christian Ebere Enyoh Christian Ebere Enyoh Christian Ebere Enyoh Christian Ebere Enyoh Christian Ebere Enyoh Christian Ebere Enyoh Christian Ebere Enyoh Christian Ebere Enyoh Christian Ebere Enyoh Christian Ebere Enyoh Christian Ebere Enyoh Christian Ebere Enyoh Christian Ebere Enyoh Christian Ebere Enyoh Christian Ebere Enyoh Christian Ebere Enyoh Christian Ebere Enyoh Christian Ebere Enyoh Christian Ebere Enyoh Chidi Edbert Duru, Christian Ebere Enyoh Christian Ebere Enyoh Christian Ebere Enyoh Christian Ebere Enyoh Christian Ebere Enyoh Christian Ebere Enyoh Christian Ebere Enyoh Christian Ebere Enyoh Christian Ebere Enyoh Christian Ebere Enyoh Christian Ebere Enyoh Christian Ebere Enyoh Chidi Edbert Duru, Chidi Edbert Duru, Chidi Edbert Duru, Ijeoma Akunna Duru, Ijeoma Akunna Duru, Christian Ebere Enyoh Christian Ebere Enyoh Ijeoma Akunna Duru, Ijeoma Akunna Duru, Ijeoma Akunna Duru, Christian Ebere Enyoh Ijeoma Akunna Duru, Christian Ebere Enyoh Christian Ebere Enyoh Margaret Chinyelu Enedoh, Margaret Chinyelu Enedoh, Christian Ebere Enyoh Christian Ebere Enyoh Christian Ebere Enyoh Christian Ebere Enyoh Christian Ebere Enyoh Christian Ebere Enyoh Christian Ebere Enyoh Christian Ebere Enyoh Christian Ebere Enyoh Christian Ebere Enyoh Christian Ebere Enyoh Christian Ebere Enyoh Christian Ebere Enyoh

Summary

Researchers used computational molecular docking and machine learning to show that the enzyme PHL7 degrades PET nanoplastics most efficiently at shorter chain lengths, with binding affinity becoming unfavorable once PET oligomer chains exceed six repeat units in length.

Polymers

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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