Plant acetyl-CoA carboxylase: The homomeric form and the heteromeric form
Bibliographic record
Abstract
• Can the plant starch-lipid interplay be exploited for increasing TAG production in plants. • Plastid acetyl-CoA carboxylase's multi-subunit structure makes it a complex enzyme to study. • A membrane-protein family negatively regulates plastid acetyl-CoA carboxylase. • Plastid α-carboxyltransferase contains a highly acidic hotspot for phosphorylation. Across the domains of life, the enzyme acetyl-CoA carboxylase (ACC) converts HCO 3 − , ATP, and acetyl-CoA to malonyl-CoA, ADP, and P i . Malonyl-CoA is the building block for all de novo fatty acid biosynthesis. ACC is found in two forms, (1) as a heteromeric enzyme, and (2) as a homomeric enzyme. Whether a single polypeptide, or various subunit combinations, they all catalyze the ATP-dependent carboxylation of acetyl-CoA to form malonyl-CoA. Here, we explore five burning questions pertaining to this fascinatingly intricate and complicated molecular machine, and the prospect of increasing oil production in plant vegetative tissues through its manipulation. We ask: 1. Can we manipulate the interplay of starch-lipid biosynthesis to increase the total TAG content in the vegetative tissues of plants? 2. Why is ACC such a complex enzyme? 3. How is ACC regulated? 4. Why is the plant plastid ACC recruited to the chloroplast membrane? 5. Will structural biology provide insights into the regulation of plant ACC?
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".