Screening a library of potential competitive inhibitors against bacterial threonyl-tRNA synthetase: DFT calculations
Bibliographic record
Abstract
Due to the growing interest in directing aminoacyl-tRNA synthetases for antimicrobial therapies, evaluating the binding proficiency of potential inhibitors against this target holds significant importance. In this work, we proposed potential ligands that could properly bind to the crucial Zn(II) cofactor located in the active site of Threonyl-tRNA synthetases (ThrRS), potentially functioning as competitive inhibitors. Initially, detailed DFT quantum chemical study was conducted to examine the binding ability of threonine against unnatural amino acids to cofactor Zn(II). Then, the binding energy value for each suggested ligand has been determined and compared to the value determined for the native substrate, threonine. Our screening investigation showed that the native threonine should coordinate in a bidentate fashion to this Zn(II) which lead to the highest (binding energy) BE Thereby, the synthetic site of ThrRS rejects unnatural amino acids that cannot afford this type of coordination to Zn(II) ion which has been supported by our calculations. Moreover, based on their binding to the Zn(II) and the obtained BE values compared to the cognate threonine, many potent ligands have been suggested. Importantly, ligands with deprotonated warheads showed the highest binding ability amongst a list of potential hits. Further investigation on the selected ligands using molecular docking and QM/MM calculations confirmed our findings of the suggested ligands being able to bind efficiently in the active site of ThrRS. The suggested hits from this study should be valuable in paving routs for developing candidates as competitive inhibitors against the bacterial ThrRS.Communicated by Ramaswamy H. Sarma.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".