A Molecular Dynamics and Quantum Mechanics/Molecular Mechanics Study of the Catalytic Reductase Mechanism of Methionine Sulfoxide Reductase A: Formation and Reduction of a Sulfenic Acid
Bibliographic record
Abstract
The catalytic mechanism of MsrA in Mycobacterium tuberculosis, in which S-methionine sulfoxide (Met-O) is reduced to methionine (Met), has been investigated using docking, molecular dynamics (MD) simulations, and ONIOM (quantum mechanics/molecular mechanics) methods. In addition, the roles of specific active site residues, including an aspartyl (Asp87) near the recycling cysteine, tyrosyls (Tyr44 and Tyr92), and glutamyl (Glu52), have been examined, as well as the general effects of the protein and active site on the nature and properties of mechanistic intermediates. The mechanism is initiated by the transfer of a proton from the catalytic cysteine's thiol (Cys13SH) via a bridging water to the R group carboxylate of Glu52. The now anionic sulfur of Cys13 nucleophilically attacks the substrate's sulfur with concomitant transfer of a proton from Glu52 to the sulfoxide oxygen, generating a sulfurane. The active site enhances the proton affinity of the sulfurane oxygen, which can readily accept a proton from the phenolic hydroxyls of Tyr44 or Tyr92 to give a sulfonium cation. Subsequently, Asp87 and the recycling cysteine (Cys154) can facilitate nucleophilic attack of a solvent water at the Cys13S center of the sulfonium to give a sulfenic acid (Cys13SOH) and Met. For the subsequent reduction of Cys13SOH with intramolecular disulfide bond formation, Asp87 can help facilitate nucleophilic attack of Cys154S at the sulfur of Cys13SOH by deprotonating its thiol. This reduction is found likely to occur readily upon suitable positioning of the active site hydrogen bond network and the sulfur centers of both Cys13 and Cys154. The calculated rate-limiting barrier is in good agreement with experiment.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".