De novo design of potent inhibitors of clostridial family toxins
Bibliographic record
Abstract
Clostridioides difficile remains a leading cause of hospital-acquired infections, with its primary virulence factor, toxin B (TcdB), responsible for severe colitis and recurrent disease. The closely related toxin, TcsL, from Paeniclostridium sordellii , causes a rarer but often fatal toxic shock syndrome, particularly in gynecological and obstetric contexts. We report the de novo design of small protein minibinders that directly neutralize TcdB and TcsL by preventing their entry into host cells. Using deep learning and Rosetta-based approaches, we generated high-affinity minibinders that protect cells from intoxication with picomolar potency and, in the case of TcsL, prolonged survival following lethal toxin challenge in mice. The designed proteins against TcdB demonstrate exceptional stability in proteolytic and acidic environments, making them well-suited for oral delivery—a valuable feature for treating C. difficile infections localized to the gastrointestinal tract. For TcsL, potent inhibitors were identified from 48 initial designs and 48 optimized designs, highlighting the potential of computational design for rapidly developing countermeasures against life-threatening bacterial toxins.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".