Identification of an H-Ras nanocluster disrupting peptide
Bibliographic record
Abstract
Hyperactive Ras signalling is found in most cancers. Ras proteins are only active in membrane nanoclusters, which are therefore potential drug targets. We previously showed that the nanocluster scaffold galectin-1 (Gal1) enhances H-Ras nanoclustering via direct interaction with the Ras binding domain (RBD) of Raf. Here, we establish that the B-Raf preference of Gal1 emerges from the divergence of the Raf RBDs at their proposed Gal1-binding interface. We then identify the L5UR peptide, which disrupts this interaction by binding with low micromolar affinity to the B- and C-Raf-RBDs. Its 23-mer core fragment is sufficient to interfere with H-Ras nanoclustering, modulate Ras-signalling and moderately reduce cell viability. These latter two phenotypic effects may also emerge from the ability of L5UR to broadly engage with several RBD- and RA-domain containing Ras interactors. The L5UR-peptide core fragment is a starting point for the development of more specific reagents against Ras-nanoclustering and -interactors.
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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".