Structure-based design of small molecule inhibitors of the cagT4SS ATPase Cagα of <i>Helicobacter pylori</i>
Bibliographic record
Abstract
We here describe the structure-based design of small molecule inhibitors of the type IV secretion system of Helicobacter pylori. The secretion system is encoded by the cag pathogenicity island, and we chose Cagα, a hexameric ATPase and member of the family of VirB11-like proteins, as target for inhibitor design. We first solved the crystal structure of Cagα in a complex with the previously identified small molecule inhibitor 1G2. The molecule binds at the interface between two Cagα subunits and mutagenesis of the binding site identified Cagα residues F39 and R73 as critical for 1G2 binding. Based on the inhibitor binding site we synthesized 98 small molecule derivates of 1G2 to improve binding of the inhibitor. We used the production of interleukin-8 of gastric cancer cells during H. pylori infection to screen the potency of inhibitors and we identified five molecules (1G2_1313, 1G2_1338, 1G2_2886, 1G2_2889, and 1G2_2902) that have similar or higher potency than 1G2. Differential scanning fluorimetry suggested that these five molecules bind Cagα, and enzyme assays demonstrated that some are more potent ATPase inhibitors than 1G2. Finally, scanning electron microscopy revealed that 1G2 and its derivatives inhibit the assembly of T4SS-determined extracellular pili suggesting a mechanism for their anti-virulence effect.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
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".