PlasticEnz Integrates Homology And Machine Learning
Krzynowek, Snoeks and Faust (published Jan. 26, 2026) present PlasticEnz, an open-source tool that combines custom HMMs, DIAMOND alignments, and ProtBERT-based machine learning to identify plastic-degrading enzymes in contigs, genomes, and metagenomes. It supports screening for 11 polymers with ML classifiers for PET and PHB achieving F1 scores above 0.7, and successfully distinguished contaminated from pristine environments in lab and field datasets.
Scoring Rationale
Peer-reviewed, practical ML–HMM integration provides usable plastizyme screening; scope limited to 11 polymers and PET/PHB classifiers.
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Sources
- Read OriginalPlasticEnz: An integrated database and screening tool combining homology and machine learning to identify plastic-degrading enzymes in meta-omics datasetsjournals.plos.org
