Metagenomic exploration and computational prediction of novel enzymes for polyethylene terephthalate degradation
As a global environmental challenge, plastic pollution raises serious ecological and health concerns owing to the excessive accumulation of plastic waste, which disrupts ecosystems, harms wildlife, and threatens human health. Polyethylene terephthalate (PET), one of the most commonly used plastics,...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-01-01
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| Series: | Ecotoxicology and Environmental Safety |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S0147651324017160 |
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| author | Donya Afshar Jahanshahi Mohammad Reza Rezaei Barzani Mohammad Bahram Shohreh Ariaeenejad Kaveh Kavousi |
| author_facet | Donya Afshar Jahanshahi Mohammad Reza Rezaei Barzani Mohammad Bahram Shohreh Ariaeenejad Kaveh Kavousi |
| author_sort | Donya Afshar Jahanshahi |
| collection | DOAJ |
| description | As a global environmental challenge, plastic pollution raises serious ecological and health concerns owing to the excessive accumulation of plastic waste, which disrupts ecosystems, harms wildlife, and threatens human health. Polyethylene terephthalate (PET), one of the most commonly used plastics, has contributed significantly to this growing crisis. This study offers a solution for plastic pollution by identifying novel PET-degrading enzymes. Using a combined approach of computational analysis and metagenomic workflow, we identified a diverse array of genes and enzymes linked to plastic degradation. Our study identified 1305,282 unmapped genes, 36,000 CAZymes, and 317 plastizymes in the soil samples were heavily contaminated with plastic. We extended our approach by training machine learning models to discover candidate PET-degrading enzymes. To overcome the scarcity of known PET-degrading enzymes, we used a Generative Adversarial Network (GAN) model for dataset augmentation and a pretrained deep Evolutionary Scale Language Model (ESM) to generate sequence embeddings for classification. Finally, 21 novel PET-degrading enzymes were identified. These enzymes were further validated through active site analysis, amino acid composition analysis, and 3D structure comparison. Additionally, we isolated bacterial strains from contaminated soils and extracted plastizymes to demonstrate their potential for environmental remediation. This study highlights the importance of biotechnological solutions for plastic pollution, emphasizing scalable, cost-effective processes and the integration of computational and metagenomic methods. |
| format | Article |
| id | doaj-art-978e4054e06f4692a1f2294a2496cf51 |
| institution | Kabale University |
| issn | 0147-6513 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ecotoxicology and Environmental Safety |
| spelling | doaj-art-978e4054e06f4692a1f2294a2496cf512025-01-23T05:25:56ZengElsevierEcotoxicology and Environmental Safety0147-65132025-01-01289117640Metagenomic exploration and computational prediction of novel enzymes for polyethylene terephthalate degradationDonya Afshar Jahanshahi0Mohammad Reza Rezaei Barzani1Mohammad Bahram2Shohreh Ariaeenejad3Kaveh Kavousi4Department of Bioinformatics, Kish International Campus University of Tehran, Kish, Iran; Laboratory of Complex Biological Systems and Bioinformatics (CBB), Department of Bioinformatics, Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran, IranLaboratory of Complex Biological Systems and Bioinformatics (CBB), Department of Bioinformatics, Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran, IranDepartment of Ecology, Swedish University of Agricultural Sciences, Uppsala 75007, Sweden; Institute of Ecology and Earth Sciences, University of Tartu, 40 Lai St., 51005, Tartu, Estonia; Department of Agroecology, Aarhus University, Forsøgsvej 1 4200, Slagelse, DenmarkDepartment of Systems and Synthetic Biology, Agricultural Biotechnology Research Institute of Iran (ABRII), Agricultural Research Education and Extension Organization (AREEO), Karaj, Iran; Correspondence to: Laboratory of Complex Biological Systems and Bioinformatics (CBB), Department of Bioinformatics, Institute of Biochemistry and Biophysics (IBB), University of Tehran, P.O. Box: 13145-1384, 1417614411, Iran.Laboratory of Complex Biological Systems and Bioinformatics (CBB), Department of Bioinformatics, Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran, Iran; Correspondence to: Laboratory of Complex Biological Systems and Bioinformatics (CBB), Department of Bioinformatics, Institute of Biochemistry and Biophysics (IBB), University of Tehran, P.O. Box: 13145-1384, 1417614411, Iran.As a global environmental challenge, plastic pollution raises serious ecological and health concerns owing to the excessive accumulation of plastic waste, which disrupts ecosystems, harms wildlife, and threatens human health. Polyethylene terephthalate (PET), one of the most commonly used plastics, has contributed significantly to this growing crisis. This study offers a solution for plastic pollution by identifying novel PET-degrading enzymes. Using a combined approach of computational analysis and metagenomic workflow, we identified a diverse array of genes and enzymes linked to plastic degradation. Our study identified 1305,282 unmapped genes, 36,000 CAZymes, and 317 plastizymes in the soil samples were heavily contaminated with plastic. We extended our approach by training machine learning models to discover candidate PET-degrading enzymes. To overcome the scarcity of known PET-degrading enzymes, we used a Generative Adversarial Network (GAN) model for dataset augmentation and a pretrained deep Evolutionary Scale Language Model (ESM) to generate sequence embeddings for classification. Finally, 21 novel PET-degrading enzymes were identified. These enzymes were further validated through active site analysis, amino acid composition analysis, and 3D structure comparison. Additionally, we isolated bacterial strains from contaminated soils and extracted plastizymes to demonstrate their potential for environmental remediation. This study highlights the importance of biotechnological solutions for plastic pollution, emphasizing scalable, cost-effective processes and the integration of computational and metagenomic methods.http://www.sciencedirect.com/science/article/pii/S0147651324017160Plastic-contaminated soilMetagenomicsPlastizymesPET degradationmachine learningHigh-throughput screening |
| spellingShingle | Donya Afshar Jahanshahi Mohammad Reza Rezaei Barzani Mohammad Bahram Shohreh Ariaeenejad Kaveh Kavousi Metagenomic exploration and computational prediction of novel enzymes for polyethylene terephthalate degradation Ecotoxicology and Environmental Safety Plastic-contaminated soil Metagenomics Plastizymes PET degradation machine learning High-throughput screening |
| title | Metagenomic exploration and computational prediction of novel enzymes for polyethylene terephthalate degradation |
| title_full | Metagenomic exploration and computational prediction of novel enzymes for polyethylene terephthalate degradation |
| title_fullStr | Metagenomic exploration and computational prediction of novel enzymes for polyethylene terephthalate degradation |
| title_full_unstemmed | Metagenomic exploration and computational prediction of novel enzymes for polyethylene terephthalate degradation |
| title_short | Metagenomic exploration and computational prediction of novel enzymes for polyethylene terephthalate degradation |
| title_sort | metagenomic exploration and computational prediction of novel enzymes for polyethylene terephthalate degradation |
| topic | Plastic-contaminated soil Metagenomics Plastizymes PET degradation machine learning High-throughput screening |
| url | http://www.sciencedirect.com/science/article/pii/S0147651324017160 |
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