Identification of an H-Ras nanocluster disrupting peptide
Bibliographic record
Abstract
Abstract The Ras-MAPK pathway is critical to regulate cell proliferation and differentiation. Its dysregulation is implicated in the onset and progression of numerous types of cancers. To be active, Ras proteins are membrane anchored and organized into nanoclusters, which realize high-fidelity signal transmission across the plasma membrane. Nanoclusters therefore represent potential drug targets. However, targetable protein components of signalling nanoclusters are poorly established. We previously proposed that the nanocluster scaffold galectin-1 (Gal1) enhances H-Ras nanoclustering by stabilizing stacked dimers of H-Ras and Raf via a direct interaction of dimeric Gal1 with the Ras binding domain (RBD) in particular of B-Raf. Here, we provide further supportive evidence for this model. We establish that the B-Raf preference emerges from divergent regions of the Raf RBDs that were proposed to interact with Gal1. We then identify the L5UR peptide, which disrupts this interaction by binding with low micromolar affinity to the B-Raf-RBD. Its 23-mer core fragment is thus sufficient to interfere with Gal1-enhanced H-Ras nanocluster, reduce MAPK-output and cell viability in HRAS -mutant cancer cell lines. Our data therefore suggest that the interface between Gal1 and the RBD of B-Raf can be targeted to disrupt Gal1-enhanced H-Ras nanoclustering. Collectively, our results support that Raf-proteins are integral components of active Ras nanoclusters.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".