Cheminformatics-Driven Discovery of Selective, Nanomolar Inhibitors for Staphylococcal Pyruvate Kinase
Bibliographic record
Abstract
We have recently mapped the protein interaction network of methicillin-resistant Staphylococcus aureus (MRSA), which revealed its scale-free organization with characteristic presence of highly connected hub proteins that are critical for bacterial survival. Here we report the discovery of inhibitors that are highly potent against one such hub target, staphylococcal pyruvate kinase (PK). Importantly, the developed compounds demonstrate complete selectivity for the bacterial enzyme compared to all human orthologues. The lead 91nM inhibitor IS-130 has been identified through ligand-based cheminformatic exploration of a chemical space around micromolar hits initially generated by experimental screening. The following crystallographic study resulted in identification of a tetrameric MRSA PK structure where IS-130 is bound to the interface between the protein's subunits. This newly described binding pocket is not present in otherwise highly similar human orthologues and can be effectively utilized for selective inhibition of bacterial PK. The following synthetic modifications of IS-130, guided by structure-based molecular modeling, resulted in the development of MRSA PK inhibitors with much improved antimicrobial properties. Considering a notable lack of recent reports on novel antibacterial targets and cognate antibacterial compounds, this study provides a valuable perspective on the development of a new generation of antimicrobials. Equally noteworthy, the results of the current work highlight the importance of rigorous cheminformatics-based exploration of the results of high-throughput experiments.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".