Nylon Analogue Substrates Allow for Continuous Quantification of Polyamidase Activity in Nylon-Degrading Enzymes
Bibliographic record
Abstract
Nylon-hydrolyzing enzymes have been of increased interest recently in the context of bioremediation. The aminohexanoate oligomer hydrolases (NylCs) are thus far the most promising biocatalysts identified to this end. Protein engineering has been used to increase the thermal stability of these enzymes, but relatively little work has been done to improve their catalytic activity, due in part to a lack of high-throughput assays. Herein we report the design, synthesis, and enzymatic hydrolysis of polyamide analogue substrates mimicking various nylon architectures. We observed hydrolysis of diamide analogues 2, 4, and 5 in a continuous and quantitative manner via a light-scattering assay, which is amenable to a high-throughput screen in 96-well plates. The reaction products were characterized by liquid chromatography-coupled mass spectrometry, revealing insight into the structural elements required for recognition of substrates by NylC enzymes. The assay may be performed in minutes at elevated temperatures, allowing for efficient screening of thermostable nylonase enzymes. The activity of the NylC enzymes towards substrate 2 correlated to their corresponding enzymatic hydrolysis of Nylon-6 film, indicating that these substrates are surrogates for bulk nylon hydrolysis. Finally, we demonstrate the applicability of this assay to cell lysate, further enabling protein engineering efforts.
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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) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".