Structure-Guided Optimization of Selective Covalent Reversible Peptidomimetic Inhibitors Targeting TMPRSS6
Bibliographic record
Abstract
Developing selective protease inhibitors is a challenging task due to the high structural resemblance of their catalytic pockets. Here, we aimed to develop selective inhibitors targeting TMPRSS6, a protease involved in regulating iron homeostasis. By exploiting structural differences in the catalytic subpockets between TMPRSS6 and matriptase, we optimized ketobenzothiazole-based peptidomimetics using the P4-P3-P2-Arg-Kbt scaffold. We found that a combination of bulky residues at P4 and P3, along with polar amino acids at P2, enhance selectivity while preserving high potency. Notably, WGU55 showed exceptional selectivity toward TMPRSS6 over matriptase and minimal off-target inhibition of coagulation serine proteases such as Factor Xa and Thrombin, representing, to our knowledge, the most selective TMPRSS6 inhibitor identified to date. Cell-based assays confirmed the inhibitor's high potency and selectivity. These findings validate a rational design strategy for the selective inhibition of TMPRSS6, paving the way for the development of targeted therapeutics based on peptidomimetics.
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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 | 0.000 |
| 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".