Targeted Genome Mining Facilitates the Discovery of a Promiscuous, Hyperthermostable Amidase from <i>Thermovenabulum Gondwanense</i> with Notable Nylon‐Degrading Capacity
Bibliographic record
Abstract
Abstract Plastics are ubiquitous in our ecosystems, and microplastic accumulation in the environment is an emerging global health concern. Since available recycling technologies are not economically competitive with primary plastic production, global use is expected to reach 1231 megatons by 2060, with 493 megatons leeching into the environment each year. To identify new nylon‐recycling biotechnologies, targeted genome mining was used to identify thermostable enzymes capable of degrading polyamides. Here, we describe the characterization of a novel protein sourced from Thermovenabulum gondwanense : TvgC. TvgC is extremely stable, exhibiting a melting temperature of 93 °C and no detectable losses in hydrolytic activity after one week at 60 °C. While nylonases primarily process nylon‐6, TvgC catalysed the degradation of both nylon‐6 and nylon‐6,6 films, which are considerably more difficult to degrade. Finally, conversion experiments demonstrate that TvgC achieves a 1.2 wt % conversion of nylon‐6 film, comparable to that of the most highly engineered nylonases. This novel hyperthermostable protein represents an excellent starting point for future engineering of increasingly efficient nylonases.
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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".