De novo designed inhibitor confers protection against lethal toxic shock
Bibliographic record
Abstract
Abstract Paeniclostridium sordellii causes a toxic shock syndrome with a mortality rate of nearly 70%, primarily affecting postpartum and post-abortive women. This disease is driven by the production of the P. sordellii lethal toxin, TcsL, for which there are currently no effective treatments. We used a protein diffusion model, RFdiffusion, to design high affinity TcsL inhibitors. From a very small set of 48 starting designs and 48 additional sequence optimized designs, we developed a potent inhibitor with <100 pM affinity that protects mice prophylactically and therapeutically (post exposure) from lung edema and death in a stringent lethal challenge model. This inhibitor, which can be lyophilized without any loss of activity, is a promising therapeutic candidate for this rare but deadly disease, and our results highlight the ability of deep learning-based protein design to rapidly generate biologics with potential clinical utility.
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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) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".