How Are Substrate Binding and Catalysis Affected by Mutating Glu127 and Arg161 in Prolyl-4-hydroxylase? A QM/MM and MD Study
Bibliographic record
Abstract
Prolyl-4-hydroxylase is a vital enzyme for human physiology involved in the biosynthesis of 4-hydroxyproline, an essential component for collagen formation. The enzyme performs a unique stereo- and regioselective hydroxylation at the C4 position of proline despite the fact that the C5 hydrogen atom should be thermodynamically easier to abstract. To gain insight into the mechanism and find the origin of this regioselectivity, we have done a quantum mechanics/molecular mechanics (QM/MM) study on wildtype and mutant structures. In a previous study [Timmins, Saint-André and de Visser, J. Am. Chem. Soc. 2017] we identified several active site residues critical for substrate binding and positioning. In particular, the Glu127 and Arg161 were shown to form multiple hydrogen bonding and ion-dipole interactions with substrate and could thereby affect the regio- and stereoselectivity of the reaction. In this work, we decided to test that hypothesis and report a QM/MM and molecular dynamics (MD) study on prolyl-4-hydroxylase and several active site mutants where Glu127 or Arg161 are mutated for Asp, Gln or Lys. Thus, the R161D and R161Q mutants give very high barriers for hydrogen atom abstraction from any proline C–H bond and therefore will be inactive. The R161K mutant, by contrast, sees the regio- and stereoselectivity of the reaction change but still is expected to hydroxylate proline at room temperature. By contrast, the Glu127 mutants E127D and E127Q show possible changes in regioselectivity with the former being more probable to react compared to the latter.
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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) | 0.001 | 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".