Recent advances in screening and identification of PET-degrading enzymes
Bibliographic record
Abstract
Polyethylene terephthalate (PET) is widely used in plastic bottles, packaging, and textile fibers. However, PET is difficult to degrade in nature and rapidly accumulates into the environment, causing serious environmental pollution and threatening human health. At present, the recycling methods for PET mainly focus on physical recycling and chemical degradation, but these methods have severe limitations, resulting in the great loss of valuable materials and secondary pollution. In contrast, the biodegradation of PET is gradually attracting attention because of its environmental friendliness, high efficiency, and cost-effectiveness. Several PET-degrading enzymes (PDEs) have been previously identified, such as cutinase, IsPETase, lipase, and esterase. Thereafter, many efforts have been made to push the boundaries of evolution schemes, attempting to create stronger PDEs with improved activity and stability. Nevertheless, most of these enzymes show preferences toward low-crystallinity (<10%) PET, while in situ enzymatic degradation of high-crystallinity PET (30%–50%) remains a major challenge. Exploring and engineering PDEs that can efficiently degrade bottle-grade PET plastics has recently become a research hotspot. This review systematically introduces the current advances in PDEs and emphasizes the role of metagenomics in screening and identifying new PDEs.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".