Engineering Arabidopsis long-chain acyl-CoA synthetase 9 variants with enhanced enzyme activity
Bibliographic record
Abstract
Abstract Long-chain acyl-CoA synthetase (LACS, EC 6.2.1.3) catalyzes the ATP-dependent activation of free fatty acid to form acyl-CoA, which, in turn, serves as the major acyl donor for various lipid metabolic pathways. Increasing the size of acyl-CoA pool by enhancing LACS activity appears to be a useful approach to improve the production and modify the composition of fatty acid-derived compounds, such as triacylglycerol. In the present study, we aimed to improve the enzyme activity of Arabidopsis thaliana LACS9 (AtLACS9) by introducing random mutations into its cDNA using error-prone PCR. Two AtLACS9 variants containing multiple amino acid residue substitutions were identified with enhanced enzyme activity. To explore the effect of each amino acid residue substitution, single-site mutants were generated and the amino acid substitutions C207F and D238E were found to be primarily responsible for the increased activity of the two variants. Furthermore, evolutionary analysis revealed that the beneficial amino acid site C207 is conserved among LACS9 from plant eudicots, whereas the other beneficial amino acid site D238 might be under positive selection. Together, our results provide valuable information for the production of LACS variants for applications in the metabolic engineering of lipid biosynthesis in oleaginous organisms.
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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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".