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

Chidi Duru; Christian Enyoh; Ijeoma Akunna Duru; Margaret Chinyelu Enedoh

doi:10.46481/jnsps.2023.1154

Authors

C. E. Duru
[email protected]

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.

Keywords:

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

Abstract

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.

Dimensions

REFERENCES

J. Gigault, A. T. Halle, M. Baudrimont, P. Y. Pascal, F. Gauffre, T. L. Phi, H. El Hadri, B. Grassl & S. Reynaud, “Current opinion: What is a nanoplastic?”, Environmental Pollution 235 (2018) 235.

E. L. Ng, L. E. Huerta, S. M. Eldridge, P. Johnston, H. W. Hu, V. Geissen & D. Chen, “An overview of microplastic and nanoplastic pollution in agroecosystems”, Science of the Total Environment 627 (2018) 1377.

C. E. Enyoh, Q. Wang, T. Chowdhury, W. Wang, S. Lu, K. Xiao & M. A. H. Chowdhury, “New analytical approaches for effective quantification and identification of nanoplastics in environmental samples”, Processes 9 (2021) 2086.

Q. Wang, C. E. Enyoh, T. Chowdhury & A. H. Chowdhury, “Analytical techniques, occurrence and health effects of micro and nano plastics deposited in street dust”, International Journal of Environmental Analytical Chemistry 102 (2022) 6435.

M. R. M. Zaki & A. Z. A. Aris, “An overview of the effects of nanoplastics on marine organisms”, Science of the Total Environment 831 (2022) 154757.

L. M. Hernandez, N. Yousefi & N. Tufenkji, “Are there nanoplastics in your personal care products?”, Environmental Science and Technology Letters 4 (2017) 280.

A. Banerjee, L. O. Billey & W. L. Shelver, “Uptake and toxicity of polystyrene micro/nanoplastics in gastric cells: Effects of particle size and surface functionalization”, PLOS ONE 16 (2021) e0260803.

J. Ru, Y. Huo & Y. Yang, “Microbial degradation and valorization of plastic wastes”, Frontiers in Microbiology 11 (2020) 442.

A. Rahimi & J. M. Garc??a, “Chemical recycling of waste plastics for new materials production”, Nature Reviews Chemistry 1 (2017) 0046.

C. E. Duru, I. A. Duru & C. E. Enyoh, “In silico binding affinity analysis of microplastic compounds on PET hydrolase enzyme target of Ideonella sakaiensis”, Bulletin of the National Research Centre 45 (2021) 104.

R. Wei & W. Zimmermann, “Microbial enzymes for the recycling of recalcitrant petroleum-based plastics: how far are we?”, Microbial Biotechnology 10 (2017) 1308.

H. P. Austin, M. D. Allen, B. S. Donohoe , N. A. Rorrer, F. L. Kearns, R. L. Silveira, B. C. Pollard, G. Dominick, R. Duman, K. El Omari, V. Mykhaylyk, A. Wagner, W. E. Michener, A. Amore, M. S. Skaf, M. F. Crowley, A. W. Thorne, C. W. Johnson, H. L. Woodcock, J. E. McGeehan & G. T. Beckham, “Characterization and engineering of a plastic-degrading aromatic polyesterase”, Proceedings of the National Academy of Sciences 115 (2018) 4350.

S. Sulaiman, S. Yamato, E. Kanaya, J. J. Kim, Y. Koga, K. Takano & S. Kanaya, “Isolation of a novel cutinase homolog with polyethylene terephthalate degrading activity from leaf-branch compost by using a metagenomic approach”, Applied and Environmental Microbiology 78 (2012) 1556.

G. A. Macedo & T. F. Pio, “A rapid screening method for cutinase producing microorganisms”, Brazilian Journal of Microbiology 36 (2005) 388.

A. Bollinger, S. Thies, E. Knieps-Grünhagen, C. Gertzen, S. Kobus, A. Höppner, M. Ferrer, H. Gohlke, S. H. J. Smits & K. E. Jaeger, “A novel polyester hydrolase from the marine bacterium Pseudomonas aestusnigri- structural and functional insights”, Frontiers in Microbiology 11 (2020) 114.

R. Koshti, L. Mehta & N. Samarth, “Biological recycling of polyethylene terephthalate: a mini-review”, Journal of Polymers and the Environment 26 (2018) 3520.

C. W. Chidiebere, C. E. Duru & J. P. C. Mbagwu, “Application of computational chemistry in chemical reactivity: A review”, Journal of the Nigerian Society of Physical Sciences 3 (2021) 292.

A. K. Nelapati & K. Meena, “An approach to increase the efficiency of uricase by computational mutagenesis”, Physical Chemistry Research 11 (2023) 481.

C. Sonnendecker, J. Oeser, P. K. Richter, P. Hille, Z. Zhao, C. Fischer, H. Lippold, P. Blázquez-Sánchez, F. Engelberger, C. A. Ram??rez-Sarmiento, T. Oeser, Y. Lihanova, R. Frank, H. G. Jahnke, S. Billig, B. Abel, N. Sträter, J. Matysik & W. Zimmermann, “Low carbon footprint recycling of post-consumer PET plastic with a metagenomic polyester hydrolase”, ChemSusChem 15 (2022) e202101062.

E. F. Pettersen, T. D. Goddard, C. C. Huang, G. S. Couch, D. M. Greenblatt, E. C. Meng & T. E. Ferrin, “UCSF Chimera- a visualization system for exploratory research and analysis”, Journal of Computational Chemistry 25 (2004) 1605.

C. E. Duru, I. A. Duru & A. Bilar, “Computational investigation of sugar fermentation inhibition by bergenin at the pyruvate decarboxylate isoenzyme 1 target of Scharomyces cervisiae”, Journal of Medicinal Plants Studies 8 (2020) 21.

C. E. Duru, I. A. Duru & C. W. Chidiebere, “Virtual screening of selected natural products as humantyrosinase-related protein 1 blocker” Journal of the Nigerian Society of Physical Sciences 3 (2021) 154.

O. Trott & A. J. Olson, “AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization and multithreading”, Journal of Computational Chemistry 31 (2010) 455.

BIOVIA, Dassault Systemes, San Diego, Discovery studio modeling environment, (2020).

Y. LeCun, Y. Bengio & G. Hinton, “Deep learning”, Nature 521 (2015) 436.

C. L. Udeze, I. E. Eteng & A. E. Ibor, “Application of Machine Learning and Resampling Techniques to Credit Card Fraud Detection” Journal of the Nigerian Society of Physical Sciences 4 (2022) 769.

S. Venkatachalam, S. G. Nayak, J. V. Labde, P. R. Gharal, K. Rao & A. K. Kelkar, “Degradation and recyclability of poly (ethylene terephthalate)”, In Polyester , IntechOpen, 2012, 75-98.

G. J. Palm, L. Reisky, D. Böttcher, H. Muller, E. A. P. Michels, M. C. Walczak, L. Berndt M. S. Weiss, U. T. Bornscheuer & G. Weder, “Structure of the plastic-degrading Ideonella sakaiensis MHETase bound to a substrate”, Nature Communications 10 (2019) 1717.