Analysis of <i>Coxiela burnetti</i> dihydrofolate reductase via <i>in silico</i> docking with inhibitors and molecular dynamics simulation
Bibliographic record
Abstract
Coxiella burnetii is a gram-negative bacterium able to infect several eukaryotic cells, mainly monocytes and macrophages. It is found widely in nature with ticks, birds, and mammals as major hosts. C. burnetii is also the biological warfare agent that causes Q fever, a disease that has no vaccine or proven chemotherapy available. Considering the current geopolitical context, this fact reinforces the need for discovering new treatments and molecular targets for drug design against C. burnetii. Among the main molecular targets against bacterial diseases reported, the enzyme dihydrofolate reductase (DHFR) has been investigated for several infectious diseases. In the present work, we applied molecular modeling techniques to evaluate the interactions of known DHFR inhibitors in the active sites of human and C. burnetii DHFR (HssDHFR and CbDHFR) in order to investigate their potential as selective inhibitors of CbDHFR. Results showed that most of the ligands studied compete for the binding site of the substrate more effectively than the reference drug trimethoprim. Also the most promising compounds were proposed as leads for the drug design of potential CbDHFR inhibitors.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".