NMR-based functional profiling of RASopathies and oncogenic RAS mutations
Bibliographic record
Abstract
Defects in the RAS small G protein or its associated network of regulatory proteins that disrupt GTPase cycling are a major cause of cancer and developmental RASopathy disorders. Lack of robust functional assays has been a major hurdle in RAS pathway-targeted drug development. We used NMR to obtain detailed mechanistic data on RAS cycling defects conferred by oncogenic mutations, or full-length RASopathy-derived regulatory proteins. By monitoring the conformation of wild-type and oncogenic RAS in real-time, we show that opposing properties integrate with regulators to hyperactivate oncogenic RAS mutants. Q61L and G13D exhibited rapid nucleotide exchange and an unexpected susceptibility to GAP-mediated hydrolysis, in direct contrast with G12V, indicating different approaches must be taken to inhibit these oncoproteins. An NMR methodology was established to directly monitor RAS cycling by intact, multidomain proteins encoded by RASopathy genes in mammalian cell extracts. By measuring GAP activity from tumor cells, we demonstrate how loss of neurofibromatosis type 1 (NF1) increases RAS-GTP levels in NF1-derived cells. We further applied this methodology to profile Noonan Syndrome (NS)-derived SOS1 mutants. Combining NMR with cell-based assays allowed us to differentiate defects in catalysis, allosteric regulation, and membrane targeting of individual mutants, while revealing a membrane-dependent compensatory effect that attenuates dramatic increases in RAS activation shown by Y337C, L550P, and I252T. Our NMR method presents a precise and robust measure of RAS activity, providing mechanistic insights that facilitate discovery of therapeutics targeted against the RAS signaling network.
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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".