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Integrating Genomic and Proteomic Data Using Machine Learning for Plastic Biodegradation: A Systematic Review
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This systematic review summarizes how machine learning and genomic data are being used to identify microbes and enzymes that can break down plastic waste. The research is significant for microplastic concerns because discovering more effective biological degradation pathways could provide a natural solution for reducing the microplastic pollution that accumulates in our environment and bodies.
Plastic waste remains a major environmental concern, and although microbial and enzymatic degradation offer eco-friendly solutions, their effectiveness is limited by microbial diversity and polymer complexity. This systematic review examined 15 studies (2021–2025) using machine learning models; such as Random Forest, SVM, XGBoost, and protein language models—applied to genomic and/or proteomic data to predict plastic biodegradation. Major plastics included PET, PE, PU, and PLA. Studies integrating multi-omics data consistently outperformed single-omics approaches, with up to 25% gains in prediction accuracy. Validation methods ranged from in vitro assays to in silico docking and field trials. While challenges remain in translating predictions into real-world applications, ML-guided multi-omics integration holds strong potential for accelerating the discovery of effective biological solutions to plastic pollution.
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This review explores how metagenomics, the study of genetic material from environmental samples, is helping scientists identify microorganisms that can break down plastics. The paper covers the methods used to find and characterize plastic-degrading bacteria, as well as the environmental consequences of plastic degradation including health risks from inhaling and ingesting microplastics. While biological solutions to plastic pollution show promise, the review notes that more research is needed to develop effective, scalable approaches.
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Researchers created a global map predicting marine microplastic pollution using machine learning based on over 9,400 samples and assessed the potential for biodegradation using marine metagenome data. The study found that microplastics converge in subtropical gyres and polar seas, and identified marine microbial communities with genetic potential for plastic biodegradation, suggesting nature may offer partial solutions to this pollution problem.
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Scientists used AI and genetic analysis to hunt for natural bacterial enzymes that can break down common plastics like polyethylene and polyurethane, materials linked to microplastic pollution that ends up in our food, water, and even bloodstream. By studying microbes that had been feeding on plastic for a long time, they identified promising new plastic-eating enzymes that hadn't been found before, some made by a bacterium called Rhodococcus aetherivorans. This matters because finding better plastic-degrading enzymes could eventually lead to more effective ways to clean up plastic waste, potentially reducing the microplastics
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