Modulation of the 14-3-3σ/C-RAF “auto”inhibited complex by molecular glues
Bibliographic record
Abstract
Molecular glues, compounds that bind cooperatively at protein-protein interfaces are revolutionizing chemical biology and drug discovery, allowing the modulation of traditional "undruggable" targets. Here, we focus on the native protein-protein interaction (PPI) of C-RAF, a key component of the MAPK signaling pathway, with the scaffolding protein 14-3-3. Although extensive drug discovery efforts have focused on the MAPK pathway, its central role in oncology and developmental disorders (RASopathies), still requires alternative approaches, moving beyond direct kinase inhibition. Indeed, stabilization of native PPIs is a relatively unexplored territory in this pathway. The function of C-RAF is regulated on multiple levels including dimerization, phosphorylation and complex formation with the hub protein 14-3-3. 14-3-3 prevents C-RAF activation by molecular recognition and binding at the phospho-serine 259. We used a fragment-merging approach to design a molecular glue scaffold that would bind to the composite surface of the 14-3-3/C-RAF "auto"inhibited complex. The synthesized molecular glues stabilized the 14-3-3/C-RAF complex up to 300-fold in biophysical assays; their glue-based mechanism of action was confirmed with several crystal structures of ternary complexes. Selectivity among the other RAF isoforms and other RAF phosphorylation sites was evaluated with biophysical assays. The best compounds showed excellent selectivity among a broad panel of 80 14-3-3 clients. Validation in cell assays showed on-target engagement, enhanced phosphorylation levels of the C-RAF pS259 site, reduced RAF dimerization and reduced ERK phosphorylation. Overall, this approach enables chemical biology studies on a C-RAF site that is intrinsically disordered prior to 14-3-3 binding and has not been targeted previously. These molecular glues will be useful as chemical probes and starting points for further drug discovery efforts to elucidate the effect of native PPI stabilization in the MAPK pathway with applications in oncology and RASopathies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".