*WINNER* Computational Design of Novel Inhibitors of Dihydrofolate Reductase in Three Bacterial Species
Bibliographic record
Abstract
This project aims to design high affinity small molecule inhibitors of bacterial dihydrofolate reductase (DHFR) for the purpose of obtaining broad-spectrum antibiotics against multiple bacteria, including Bacillus anthracis (anthrax), Staphylococcus aureus, and Mycobacterium tuberculosis. Inhibitors were designed using MOE 2020 (Chemical Computing, Ltd., Montreal, Quebec, Canada) based on a previous ZINC Database search to target the active site of DHFR based on computational analysis of the energetic frustration and evolutionary importance of amino acid residues present. This analysis was conducted using the Protein Frustratometer (http://frustratometer.qb.fcen.uba.ar/; EMBNet Aargentina, Buenos Aires, Argentina) and Evolutionary Trace (http://lichtargelab.org/software/ETserver; Baylor College of Medicine, Baylor University, Houston, Texas USA). Evolutionary trace and frustration define the active site, by determining binding sites, and areas of the molecule in high energetic states, respectively. Designed inhibitors were docked into each protein using the Docking module of MOE 2020, and the binding residues were then compared to the areas of evolutionary trace and frustration to help determine if the molecules had favorable binding scores. 189 small molecules were designed to interact with these amino acid functional groups based on complementary, non-covalent functional group interactions. The ligand interactions for the top compounds in each bacteria were examined and these compounds were examined according to Lipinski’s Rule of Five, which helps to determine potential druggability. One compound was found to have favorable bonding across all three bacterial DHFR, and fourteen compounds were recognized as having favorable bonding across two bacterial DHFR.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".