A novel C-terminal degron identified in bacterial aldehyde decarbonylases using directed evolution
Bibliographic record
Abstract
Abstract Background Aldehyde decarbonylases (ADs), which convert acyl aldehydes into alkanes, supply promising solution for producing alkanes from renewable feedstock. However the instability of ADs impedes their further application. Therefore, the current study aimed to investigate the degradation mechanism of ADs and engineer it towards high stability. Results Here, we describe the discovery of a degradation tag (degron) in the AD from marine cyanobacterium Prochlorococcus marinus using error-prone PCR-based directed evolution system. Bioinformatic analysis revealed that this C-terminal degron is common in bacterial ADs and identified a conserved C-terminal motif, RMSAYGLAAA, representing the AD degron (ADcon). Furthermore, we demonstrated that the ATP-dependent proteases ClpAP and Lon are involved in the degradation of AD-tagged proteins in E. coli , thereby limiting alkane production. Deletion or modification of the degron motif increased alkane production in vivo. Conclusion This work revealed the presence of a novel degron in bacterial ADs responsible for its instability. The in vivo experiments proved eliminating or modifying the degron could stabilize AD, thereby producing higher titers of alkanes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".