On the road to structure-based development of anti-virulence therapeutics targeting the type III secretion system injectisome
Bibliographic record
Abstract
an 'anti-virulence strategy' is a promising avenue to pursue as an alternative to the more commonly used bactericidal therapeutics, which have a high propensity for resulting resistance development and often more broad killing profile, including unwanted side effects in eliminating favourable members of the microbiome. Building on more than a decade of crystallographic work of truncated or isolated forms of the more than two dozen components of the secretion apparatus, recent advances in the field of single-particle cryo-electron microscopy have allowed for the elucidation of atomic resolution structures for many of the type III secretion system components in their assembled, oligomerized state including the needle complex, export apparatus and ATPase. Cryo-electron tomography studies have also advanced our understanding of the direct pathogen-host interaction between the type III secretion system translocon and host cell membrane. These new structural works that further our understanding of the myriad of protein-protein interactions that promote injectisome function will be highlighted in this review, with a focus on those that yield promise for future anti-virulence drug discovery and design. Recently developed inhibitors, including both synthetic, natural product and peptide inhibitors, as well as promising new developments of immunotherapeutics will be discussed. As our understanding of this intricate molecular machinery advances, the development of anti-virulence inhibitors can be enhanced through structure-guided drug design.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".