Design of Peptoid-peptide Macrocycles to Inhibit the β-catenin TCF Interaction in Prostate Cancer
Bibliographic record
Abstract
New chemical inhibitors of protein-protein interactions are needed to propel advances in molecular pharmacology. Peptoids are peptidomimetic oligomers with the capability to inhibit protein-protein interactions by mimicking protein secondary structure motifs. Here we report the in silico design of a macrocycle primarily composed of peptoid subunits that targets the β-catenin:TCF interaction. The β-catenin:TCF interaction plays a critical role in the Wnt signaling pathway which is over-activated in multiple cancers, including prostate cancer. Using the Rosetta suite of protein design algorithms, we evaluate how different macrocycle structures can bind a pocket on β-catenin that associates with TCF. The in silico designed macrocycles are screened in vitro using luciferase reporters to identify promising compounds. The most active macrocycle inhibits both Wnt and AR-signaling in prostate cancer cell lines, and markedly diminishes their proliferation. In vivo potential is demonstrated through a zebrafish model, in which Wnt signaling is potently inhibited.
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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".