Phosphorylation Mechanism of Phosphomevalonate Kinase: Implications for Rational Engineering of Isoprenoid Biosynthetic Pathway Enzymes
Bibliographic record
Abstract
The mevalonate pathway is of important clinical, pharmaceutical, and biotechnological relevance. However, lack of the understanding of the phosphorylation mechanism of the kinases in this pathway has limited rationally engineering the kinases in industry. Here the phosphorylation reaction mechanism of a representative kinase in the mevalonate pathway, phosphomevalonate kinase, was studied by using molecular dynamics and hybrid QM/MM methods. We find that a conserved residue (Ser106) is reorientated to anchor ATP via a stable H-bond interaction. In addition, Ser213 located on the α-helix at the catalytic site is repositioned to further approach the substrate, facilitating the proton transfer during the phosphorylation. Furthermore, we elucidate that Lys101 functions to neutralize the negative charge developed at the β-, γ-bridging oxygen atom of ATP during phosphoryl transfer. We demonstrate that the dissociative catalytic reaction occurs via a direct phosphorylation pathway. This is the first study on the phosphorylation mechanism of a mevalonate pathway kinase. The elucidation of the catalytic mechanism not only sheds light on the common catalytic mechanism of the GHMP kinase superfamily but also provides the structural basis for engineering the mevalonate pathway kinases to further exploit their applications in the production of a wide range of fine chemicals such as biofuels or pharmaceuticals.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".