Mechanism of the Nitric Oxide Dioxygenase Reaction of <i>Mycobacterium tuberculosis</i> Hemoglobin N
Bibliographic record
Abstract
Many globins convert • NO to innocuous NO 3 – through their nitric oxide dioxygenase (NOD) activity. Mycobacterium tuberculosis fights the oxidative and nitrosative stress imposed by its host (the toxic effects of O 2 •– and • NO species and their OONO – and • NO 2 derivatives) through the action of truncated hemoglobin N (trHbN), which catalyzes the NOD reaction with one of the highest rates among globins. The general NOD mechanism comprises the following steps: binding of O 2 to the heme, diffusion of • NO into the heme pocket and formation of peroxynitrite (OONO – ), isomerization of OONO –, and release of NO 3 – . Using quantum mechanics/molecular mechanics free-energy calculations, we show that the NOD reaction in trHbN follows a mechanism in which heme-bound OONO – undergoes homolytic cleavage to give Fe IV ═O 2 – and the • NO 2 radical but that these potentially harmful intermediates are short-lived and caged by the heme pocket residues. In particular, the simulations show that Tyr33(B10) side chain is shielded from Fe IV ═O 2 – and • NO 2 (and protected from irreversible oxidation and nitration) by forming stable hydrogen bonds with Gln58(E11) side chain and Leu54(E7) backbone. Aromatic residues Phe46(CD1), Phe32(B9), and Tyr33(B10) promote NO 3 – dissociation via C–H···O bonding and provide stabilizing interactions for the anion along its egress route.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".