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Fuzzy K-means–based outlier detection in plastic-degradation-related protein sequences using PSI-BLAST, Jaccard similarity, and OMA features
Original title: Fuzzy K-means–based outlier detection in plastic-degradation-related protein sequences using PSI-BLAST, Jaccard similarity, and OMA features
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Scientists developed a smarter computer method to scan proteins and spot unusual ones that might be especially good at breaking down plastic waste. This matters because finding new plastic-eating proteins could help us develop better tools to clean up plastic pollution, including the microplastics that end up in our water, food, and eventually our bodies. While this study doesn't test the proteins directly on human health, it's an early step toward biological solutions that could reduce our long-term exposure to plastic waste in the environment.
Plastic pollution is a severe environmental hazard due to the persistence of synthetic polymers, thus requiring the development of sophisticated techniques. This study proposes a fuzzy k-means-based framework integrating PSI-BLAST alignment features, Jaccard motif similarity, and OMA evolutionary scores to identify functional outliers within protein sequences associated with plastic degradation. Three variations of the system were evaluated: (i) PSI-BLAST alone could identify 200 outliers, (ii) PSI-BLAST + Jaccard similarity reduced the number of outliers to 162, and (iii) PSI-BLAST + Jaccard + OMA further reduced the number of outliers to 151, achieving a 24.5% improvement in outlier detection. The approach shows that proteins with weak similarity to known degraders, suggesting candidates for novel catalytic functions. A knowledge graph constructed from the clustering results visualises connectivity patterns and isolates weakly linked outliers. These results highlight that combining evolutionary, structural, and functional metrics improves the precision of detecting non-canonical sequences relevant to plastic degradation pathways.
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