Dynamic conformational equilibria in the active states of KRAS and NRAS
Bibliographic record
Abstract
N relaxation dispersion NMR, hydrogen-deuterium exchange mass spectrometry (HDX-MS), and molecular dynamics simulations. In GMPPNP-bound KRAS and NRAS, we see the co-existence of two conformational states, corresponding to an "inactive" state-1 and an "active" state-2, as previously reported. The KRAS oncogenic mutations G12D, G12C and G12V only slightly affect this equilibrium towards the "inactive" state-1, with rank order wt < G12C < G12D < G12V. In contrast, the NRAS Q61R oncogenic mutation shifts the equilibrium fully towards the "active" state-2. Our molecular dynamics simulations explain this by the observation of a transient hydrogen bond between the Arg61 side chain and the Thr35 backbone carbonyl oxygen. NMR relaxation dispersion experiments with GTP-bound KRAS Q61R confirm a drastic decrease in the population of state-1, but still detect a small residual population (1.8%) of this conformer. HDX-MS indicates that higher populations of state-1 correspond to increased hydrogen-deuterium exchange rates in some regions and increased flexibility, whereas low state-1 populations are associated with KRAS rigidification. We elucidated the mechanism of action of a potent KRAS G12D inhibitor, MRTX1133. Binding of this inhibitor to the switch-2 pocket causes a complete shift of KRAS G12D towards the "inactive" conformation and prevents binding of effector RAS-binding domain (RBD) at physiological concentrations, by signaling through an allosteric 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".