ArticlePLoS computational biology2026
PlasticEnz: An integrated database and screening tool combining homology and machine learning to identify plastic-degrading enzymes in meta-omics datasets.
Article in PLoS computational biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
3 citing papers in PubMed.
- Functional dynamics and interactions within the bacterial community responsible for biodegradable plastic degradation during aerobic composting.Biodegradation · 2026Article
- Predicting the plastic biodegradation potential within microbial lineages and across global ecosystems.Microbial genomics · 2026Article
- Analysis of differences in gut microbiota structure and function in Bactrian camels under different breeding patterns from northern and southern Xinjiang, China.Frontiers in veterinary science · 2026Article
Corrections and comments
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Authors and funding
3 authors.
Funding
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Abstract
PlasticEnz is a new open-source tool for detecting plastic-degrading enzymes (plastizymes) in metagenomic data by combining sequence homology-based search with machine learning techniques. It integrates custom Hidden Markov Models, DIAMOND alignments, and polymer-specific classifiers trained on ProtBERT embeddings to identify candidate depolymerases from user-provided contigs, genomes, or protein sequences. PlasticEnz supports 11 plastic polymers with ML classifiers for PET and PHB, achieving F1 > 0.7 on an independent test set. Applied to plastic-exposed microcosms and field metagenomes, the tool recovered known PETases and PHBases, distinguished plastic-contaminated from pristine environments, and clustered predictions with validated reference enzymes. PlasticEnz is fast, scalable, and user-friendly, providing a robust framework for exploring microbial plastic degradation potential in complex communities.
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Registered trials
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