Kinetic improvement of an algal diacylglycerol acyltransferase 1 via fusion with an acyl‐CoA binding protein
Bibliographic record
Abstract
Microalgal oils in the form of triacylglycerols (TAGs) are broadly used as nutritional supplements and biofuels. Diacylglycerol acyltransferase (DGAT) catalyzes the final step of acyl-CoA-dependent biosynthesis of TAG, and is considered a key target for manipulating oil production. Although a growing number of DGAT1s have been identified and over-expressed in some algal species, the detailed structure-function relationship, as well as the improvement of DGAT1 performance via protein engineering, remain largely untapped. Here, we explored the structure-function features of the hydrophilic N-terminal domain of DGAT1 from the green microalga Chromochloris zofingiensis (CzDGAT1). The results indicated that the N-terminal domain of CzDGAT1 was less disordered than those of the higher eukaryotic enzymes and its partial truncation or complete removal could substantially decrease enzyme activity, suggesting its possible role in maintaining enzyme performance. Although the N-terminal domains of animal and plant DGAT1s were previously found to bind acyl-CoAs, replacement of CzDGAT1 N-terminus by an acyl-CoA binding protein (ACBP) could not restore enzyme activity. Interestingly, the fusion of ACBP to the N-terminus of the full-length CzDGAT1 could enhance the enzyme affinity for acyl-CoAs and augment protein accumulation levels, which ultimately drove oil accumulation in yeast cells and tobacco leaves to higher levels than the full-length CzDGAT1. Overall, our findings unravel the distinct features of the N-terminus of algal DGAT1 and provide a strategy to engineer enhanced performance in DGAT1 via protein fusion, which may open a vista in generating improved membrane-bound acyl-CoA-dependent enzymes and boosting oil biosynthesis in plants and oleaginous microorganisms.
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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 | 0.000 |
| 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) | 0.001 | 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".