Characterization of Potential Plastic-Degradation Enzymes from Marine Bacteria
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Polyethylene terephthalate (PET) and polyethylene (PE) are prominent polymer materials that comprise a significant portion of commercial plastic waste. Their durability and slow degradation rate have resulted in significant accumulation of plastic on Earth. In a recent study, macrotranscriptomic profiling of a reconstituted marine bacterial community identified 10 putative enzymes capable of directly acting on PE or PET (PEases or PETases). Among these enzymes, three recombinant proteins were reported to possess PE degradation activity. To select potential plastic degrading enzyme candidates for protein engineering efforts, we expressed and purified eight out of the 10 candidates, excluding two due to poor expression and/or solubility. Notably, several candidate proteins displayed significant esterase activity on p- nitrophenyl butyrate and exhibited unexpected thermostability despite their marine origin. Additionally, we observed dose- and time-dependent hydrolytic activity on the PET trimer substrate. Structural analysis and mutagenesis of a candidate protein confirmed the presence of catalytic triad residues, classifying it as an esterase. Furthermore, we elucidated the structural importance of the two disulfide bonds. Through point mutation experiments, we observed an enhanced hydrolytic activity of a selected enzyme candidate on PET nanoparticles. Our findings challenge the classification of the enzymes directly acting on PE and highlight the significance and complexity of validating PE degradation enzymes identified through metagenomic analysis.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".