Interpretable Machine Learning and Comparative Genomics Reveal Microbial Plastic-Degrading (Microbeyt) Potential

Plastic pollution poses a critical environmental threat, and microbial enzymes represent a sustainable strategy for polymer degradation. We present a computational pipeline that integrates orthogroup-based genomic analysis with machine learning and interpretable feature importance to identify microbial strains with high plastic-degrading potential. Using presence or absence matrices and SHAP-derived feature contributions to the MTP visualization, the workflow highlights conserved gene modules driving predictive classification. Application to a single genus revealed strains harboring versatile enzymatic repertoires capable of targeting diverse polymers, including polyethylene, polyethylene terephthalate, polyurethane, and polyhydroxyalkanoates. These findings provide a rational framework for prioritizing candidate strains for experimental validation and bioremediation strategies. Overall, this study demonstrates how integrating comparative genomics with interpretable machine learning can guide the systematic discovery of microbial solutions to plastic pollution.