Oncogenic KRAS G12D mutation promotes dimerization through a second, phosphatidylserine–dependent interface: a model for KRAS oligomerization
Bibliographic record
Abstract
KRAS forms transient dimers and higher-order multimers (nanoclusters) on the plasma membrane, which drive MAPK signaling and cell proliferation. KRAS is a frequently mutated oncogene, and while it is well known that the most prevalent mutation, G12D, impairs GTP hydrolysis, thereby increasing KRAS activation, G12D has also been shown to enhance nanoclustering. Elucidating structures of dynamic KRAS assemblies on a membrane has been challenging, thus we have refined our NMR approach that uses nanodiscs to study KRAS associated with membranes. We incorporated paramagnetic relaxation enhancement (PRE) titrations and interface mutagenesis, which revealed that, in addition to the symmetric 'α-α' dimerization interface shared with wild-type KRAS, the G12D mutant also self-associates through an asymmetric 'α-β' interface. The 'α-β' association is dependent on the presence of phosphatidylserine lipids, consistent with previous reports that this lipid promotes KRAS self-assembly on the plasma membrane in cells. Experiments using engineered mutants to spoil each interface, together with PRE probes attached to the membrane or free in solvent, suggest that dimerization through the primary 'α-α' interface releases β interfaces from the membrane promoting formation of the secondary 'α-β' interaction, potentially initiating nanoclustering. In addition, the small molecule BI-2852 binds at a β-β interface, stabilizing a new dimer configuration that outcompetes native dimerization and blocks the effector-binding site. Our data indicate that KRAS self-association involves a delicately balanced conformational equilibrium between transient states, which is sensitive to disease-associated mutation and small molecule inhibitors. The methods developed here are applicable to biologically important transient interactions involving other membrane-associated proteins.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".