Analysis of <i>Coxiela burnetti</i> dihydrofolate reductase via <i>in silico</i> docking with inhibitors and molecular dynamics simulation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it