Towards the Bioremediation of Nylon Waste Materials: Genome Mining Leads to the Identification of a Thermostable Laurolactamase from Thermopolyspora flexuosa
Bibliographic record
Abstract
The accumulation of plastic waste presents an ongoing environmental and human health crisis. With current recycling technologies recovering only ~9% of plastics globally, there is an urgent need for innovative and sustainable solutions. While enzymatic strategies for polyethylene terephthalate (PET) degradation have made significant progress, analogous approaches for other plastics, such as nylon, remain underdeveloped. In particular, the environmental persistence of cyclic nylon oligomers has received limited attention, with only a single distinct enzyme (NylA) reported decades ago and exhibiting poor catalytic performance. To address this critical gap, a genome mining approach was used to identify novel amidases with enhanced activity and thermal stability. Herein, we report the discovery and characterization of a thermostable lactam hydrolase from the bacterium Thermopolyspora flexuosa, representing the first thermostable NylA orthologue, with a melting temperature of 72 ± 0.3 °C. Biochemical analyses reveal that this enzyme hydrolyzes a broad range of lactams, including cyclic nylon byproducts, with particularly high specificity and turnover for laurolactam. An analysis of substrate scope trends was performed to understand the molecular features governing enzyme-substrate compatibility. Structural modeling and mutational analysis elucidated key substrate-binding feature, shedding light on the preferential activity of NylAs towards laurolactam over the cyclic nylon substrate and providing a mechanistic foundation for downstream enzyme engineering efforts. This thermostable NylA variant will serve as the ideal starting point for the development of robust enzymes capable of mitigating recalcitrant nylon waste, advancing the field of biocatalysis toward sustainable plastic remediation technologies.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".