Toward the Bioremediation of Nylon Waste Materials: Genome Mining Leads to the Identification of a Thermostable Laurolactamase From <i>Thermopolyspora flexuosa</i>
Bibliographic record
Abstract
Plastic waste accumulation presents an environmental and human health crisis. With current recycling technologies recovering only ∼9% of plastics globally, there is an urgent need for sustainable solutions. While enzymatic strategies for polyethylene terephthalate degradation have made significant progress, analogous approaches for other plastics, like nylon, remain underdeveloped. In particular, the persistence of cyclic nylon oligomers has received limited attention, with only one distinct enzyme (NylA) reported decades ago, exhibiting poor catalytic performance. To address this critical gap, using genome mining, novel amidases were identified with enhanced activity and thermal stability. Herein, we report the discovery and characterization of a lactam hydrolase from Thermopolyspora flexuosa , the first thermostable NylA orthologue ( T m = 72°C ± 0.3°C). Biochemical analyses reveal that Tfl NylA hydrolyzes a range of lactams, including cyclic nylon byproducts, with particularly high specificity and turnover for laurolactam. Substrate scope analysis and structural modeling revealed key molecular features governing enzyme‐substrate compatibility, explaining the preferential activity of Tfl NylA. Co‐incubation of Tfl NylA and TvgC with nylon film increased nylon dimer production, underscoring the potential of enzyme synergy for enhanced plastic degradation. This thermostable NylA variant provides an ideal starting point for enzyme engineering efforts to develop robust catalysts for nylon waste remediation.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".