Identification of novel inhibitors targeting serine acetyltransferase from <i>Neisseria gonorrhoeae</i>
Bibliographic record
Abstract
Abstract Neisseria gonorrhoeae is an obligate human pathogen and the etiological agent of the sexually transmitted infection, gonorrhoea. The rapid emergence of extensively antimicrobial-resistant strains, including those resistant to all frontline antibiotics, has led to N. gonorrhoeae being labelled a priority pathogen by the World Health Organization, highlighting the need for new antimicrobial treatments. Given its absence in humans, targeting de novo cysteine biosynthesis has been identified as a promising avenue for developing new antimicrobials against drug-resistant bacteria. The biosynthesis of cysteine is catalyzed by two enzymes; serine acetyltransferase (SAT/CysE) which catalyzes the first step and O -acetylserine sulfhydrylase (OASS/CysK) that catalyzes the second step incorporating sulfur to form L-cysteine. CysE is reported to be essential for bacterial survival in several bacterial pathogens including N. gonorrhoeae . Here, we have conducted virtual inhibitor screening of commercially available compound libraries against SAT from N. gonorrhoeae ( Ng SAT). We have identified a hit compound with an IC 50 of 13.9 µM and analyzed its interactions with the enzyme’s active site. This provides a platform for the identification and development of novel SAT inhibitors to combat drug-resistant bacterial pathogens.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".