Structure‐based virtual screening and biological evaluation of novel inhibitors of mycobacterium Z‐ring formation
Bibliographic record
Abstract
The major part of commercial prodrugs against Mycobacterium tuberculosis (Mtb) demonstrated a significant inhibitory effect on cell division and inhibition of bacterial growth in vitro. However, further implementation often failed to overcome the compensatory system of interchangeable cascades. This is the most common situation for the compounds, which hit the key enzymes activities involved in all basic stages of the cell cycle. We decided to find more compounds, which could affect a cytoskeleton complex playing important role in sensing the external signals, intracellular transport, and cell division. In general, the bacterial cytoskeleton is crucial for response to the environment and participates in cell-to-cell communication. In turn, filamentous temperature-sensitive Z (FtsZ) protein, a mycobacterial tubulin homolog, is essential for Z-ring formation and further bacteria cell division. We predicted the most preferable binding-sites and conducted a high-throughput virtual screening. Modeling results suggest that some compounds bind in a specific region on the surface Mtb FtsZ, which is absent in human, and other could hit GTPase activity of the FtsZ. Further in vitro studies confirmed that these novel molecules can efficiently bind to these pockets, demonstrating an effect on the polymerization state and kinetics mechanisms. The rescaling of the experiment on the cell line revealed that reported compounds are able to alter the polymerization level of the filamentous and, therefore, prevent mycobacteria reproduction.
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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) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".