Identification and validation of potent inhibitor of <i>Escherichia coli</i> DHFR from MMV pathogen box
Bibliographic record
Abstract
The present study is conducted to find the solution of rising antimicrobial resistance (AMR) in Escherichia coli which is a pathogen responsible for fatal systemic infections in human and animals. The enzyme dihydrofolate reductase (DHFR) is found in all organisms. In this study DHFR of E. coli (ec-DHFR) and human DHFR (h-DHFR) is targeted by novel chemical entities (NCE) from the Pathogen box of Medicines for Malaria Venture, Switzerland (MMV) using molecular modelling. The in-silico studies were further validated by in-vitro assays. The virtual screening of 400 MMV compounds was conducted using PyRx standard tool followed by manual docking of selected compounds by Autodock vina and Ligplot program. The in-silico studies showed good binding energy and strong hydrogen bond in docking of MMV675968 with ec-DHFR and no hydrogen bond with h-DHFR. This was further validated by the Molecular dynamic studies that revealed high binding free energy in ec-DHFR and in-vitro assays that produced good synergy in combination study of MMV675968 with last line (meropenem) and last resort (colistin) antibiotics. The extensive MD simulation and energetic analysis thus concludes that MMV675968 targets ec-DHFR. The combination studies were conducted with MMV675968 and FDA approved drugs against a panel of multidrug resistant Escherichia coli isolates. The synergistic results obtained in combination studies concluded that in-vitro data is consistent with in-silico data and that MMV675968 is a potential lead for future process of antimicrobial drug development against the multidrug resistance E. coli.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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".