Mirror‐Image Random Nonstandard Peptides Integrated Discovery (MI‐RaPID) Technology Yields Highly Stable and Selective Macrocyclic Peptide Inhibitors for Matrix Metallopeptidase 7
Bibliographic record
Abstract
Abstract Matrix metallopeptidase 7 (MMP7) plays a crucial role in cancer metastasis and progression, making it an attractive target for therapeutic development. However, the development of selective MMP7 inhibitors is challenging due to the conservation of active sites across various matrix metalloproteinases (MMPs). Here, we have developed mirror‐image random nonstandard peptides integrated discovery (MI‐RaPID) technology to discover innate protease‐resistant macrocyclic peptides that specifically bind to and inhibit human MMP7. One identified macrocyclic peptide against D ‐MMP7, termed D20 , was synthesized in its mirror‐image form, D’20 , consisting of 12 D ‐amino acids, one cyclic β‐amino acid, and a thioether bond. Notably, it potently inhibited MMP7 with an IC 50 value of 90 nM, and showed excellent selectivity over other MMPs with similar substrate specificity. Moreover, D’20 inhibited the migration of pancreatic cell line CFPAC‐1, but had no effect on the cell proliferation and viability. D’20 exhibited excellent stability in human serum, as well as in simulated gastric and intestinal fluids. This study highlights that MI‐RaPID technology can serve as a powerful tool to develop in vivo stable macrocyclic peptides for therapeutic applications.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".