Isonicotinic Acid Hydrazide Conversion to Isonicotinyl-NAD by Catalase-peroxidases
Bibliographic record
Abstract
Activation of the pro-drug isoniazid (INH) as an anti-tubercular drug in Mycobacterium tuberculosis involves its conversion to isonicotinyl-NAD, a reaction that requires the catalase-peroxidase KatG. This report shows that the reaction proceeds in the absence of KatG at a slow rate in a mixture of INH, NAD(+), Mn(2+), and O(2), and that the inclusion of KatG increases the rate by >7 times. Superoxide, generated by either Mn(2+)- or KatG-catalyzed reduction of O(2), is an essential intermediate in the reaction. Elimination of the peroxidatic process by mutation slows the rate of reaction by 60% revealing that the peroxidatic process enhances, but is not essential for isonicotinyl-NAD formation. The isonicotinyl-NAD(*+) radical is identified as a reaction intermediate, and its reduction by superoxide is proposed. Binding sites for INH and its co-substrate, NAD(+), are identified for the first time in crystal complexes of Burkholderia pseudomallei catalase-peroxidase with INH and NAD(+) grown by co-crystallization. The best defined INH binding sites were identified, one in each subunit, on the opposite side of the protein from the entrance to the heme cavity in a funnel-shaped channel. The NAD(+) binding site is approximately 20 A from the entrance to the heme cavity and involves interactions primarily with the AMP portion of the molecule in agreement with the NMR saturation transfer difference results.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".