Oncogenic and RASopathy-associated K-RAS mutations relieve membrane-dependent occlusion of the effector-binding site
Bibliographic record
Abstract
K-RAS4B (Kirsten rat sarcoma viral oncogene homolog 4B) is a prenylated, membrane-associated GTPase protein that is a critical switch for the propagation of growth factor signaling pathways to diverse effector proteins, including rapidly accelerated fibrosarcoma (RAF) kinases and RAS-related protein guanine nucleotide dissociation stimulator (RALGDS) proteins. Gain-of-function KRAS mutations occur frequently in human cancers and predict poor clinical outcome, whereas germ-line mutations are associated with developmental syndromes. However, it is not known how these mutations affect K-RAS association with biological membranes or whether this impacts signal transduction. Here, we used solution NMR studies of K-RAS4B tethered to nanodiscs to investigate lipid bilayer-anchored K-RAS4B and its interactions with effector protein RAS-binding domains (RBDs). Unexpectedly, we found that the effector-binding region of activated K-RAS4B is occluded by interaction with the membrane in one of the NMR-observable, and thus highly populated, conformational states. Binding of the RAF isoform ARAF and RALGDS RBDs induced marked reorientation of K-RAS4B from the occluded state to RBD-specific effector-bound states. Importantly, we found that two Noonan syndrome-associated mutations, K5N and D153V, which do not affect the GTPase cycle, relieve the occluded orientation by directly altering the electrostatics of two membrane interaction surfaces. Similarly, the most frequent KRAS oncogenic mutation G12D also drives K-RAS4B toward an exposed configuration. Further, the D153V and G12D mutations increase the rate of association of ARAF-RBD with lipid bilayer-tethered K-RAS4B. We revealed a mechanism of K-RAS4B autoinhibition by membrane sequestration of its effector-binding site, which can be disrupted by disease-associated mutations. Stabilizing the autoinhibitory interactions between K-RAS4B and the membrane could be an attractive target for anticancer drug discovery.
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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".