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Integrating Computational Tools and Machine Learning for Microbial Enzyme Engineering in Persistent Pollutant Management: A Systematic Review from Detection to Recycling
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Scientists are using AI tools (like AlphaFold, the program that predicts protein shapes) to design custom microbial enzymes that can break down stubborn pollutants like PFAS ("forever chemicals"), microplastics, and other toxic industrial chemicals that build up in our environment and bodies. This paper reviews existing research on these computational methods rather than presenting new lab results, but it matters because faster, smarter enzyme design could eventually lead to real-world tools for cleaning up the pollutants linked to health problems like hormone disruption and cancer risk.
This systematic review aims to evaluate and integrate computational tools—including metagenomics, AI-based structure prediction (AlphaFold), molecular docking, molecular dynamics simulations, and machine learning—for engineering microbial enzymes to detect, degrade, and recycle persistent organic pollutants (POPs) such as PFAS, microplastics, PAHs, and PCBs. The review follows PRISMA 2020 guidelines and will synthesize evidence from studies published between 2018 and 2026
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