NEW JERSEY NORTHERN SOS-PVCase: A Machine Learning Optimized Lignin-Peroxidase with Polyvinyl Chloride (PVC) Degrading Properties

Overview

Plastic accumulating in landfills poses a major environmental threat to wildlife ecosystems and contributes to the production of harmful greenhouse gasses. Polyvinyl chloride (PVC) accounts for roughly 12% of plastic manufactured worldwide, and under current measures, nearly 79% of post-consumer PVC ends up in landfills (Geyer et al., 2017). Enzymatic degradation of plastic polymers into reusable monomers provides a green and scalable solution to this expanding problem. However, the application of PVC degrading peroxidases in real- world environments is impaired by their lack of stability and solubility (Lu et al., 2022). Fungal lignin peroxidase (E.C 1.11.1.14), an enzyme expressed by Phanerochaete chrysosporium, has previously been identified to have PVC degradation properties, but nevertheless, is also hindered by these same constraining properties (Khatoon et al., 2018). In this study, established machine learning methods in literature were used to pinpoint successful mutations in the primary structure of the peroxidase. This novel metapredictor variant approach is far more time effective than the traditional process of synthesizing each mutation in a lab, and is much less computationally expensive than other machine learning techniques. The mutant protein (SOS-PVCase: stable, optimized, soluble) contains five amino acid substitutions (A112I, A114I, S174K, E224M, L291R) and is predicted to display superior metabolic activity in various environmental conditions compared to the wild-type fungal PVCase. This is significant, as it allows PVC recycling pathways to be drastically accelerated.

Competition history

• JSHS 2023 Category not listed

Resources

• National JSHS abstract book ↗

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