Molecular Docking and Simulation Study to Identify Novel Potent Inhibitors Against FmtA in Staphylococcus Aureus
Bibliographic record
Abstract
Staphylococcus aureus is a gram-positive bacterium causing pneumonia, endocarditis and sepsis. Screening for Staphylococcus aureus has resulted in the identification of auxillary genes like fmtA, llm, and other factors. FmtA possess novel hydrolytic activity toward the ester bond between D-Ala and the backbone of teichoic acids (TAs). TAs are polyol-phosphate polymers found in the Staphylococcus aureus cell wall that play important roles in antibiotic resistance and pathogenesis. Two of the PRPs conserved motifs, namely SXXK and Y(S)XN, are involved in the hydrolysis by FmtA, but its catalytic mechanism remains elusive. In the present study, we determined the crystal structure and identified the potent inhibitors that can effectively bind to FmtA. We screened active compounds and the binding affinity was confirmed using molecular docking study. The enzyme-ligand complex which showed higher binding affinity than substrate was employed for subsequent analysis. Molecular dynamics studies were utilized to validate the dynamics and stability of top scoring protein-inhibitor complex using GROMACS 5.1.4. Molecular dynamics revealed a similar pattern of deviation, fluctuation, and compactness in the protein-inhibitor complex as compared to the protein-substrate complex. Further, in-vitro binding assays will be performed to check the inhibition of novel esterase for the inhibition of Staphylococcus aureus.
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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) | 0.002 | 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".