Mapping the free energy landscape of K-Ras4B dimerization
Bibliographic record
Abstract
KRAS-4B regulates cellular proliferation and differentiation via its GTPase activity, and it is often mutated in human tumors. Deregulation of the MAPK/ERK pathway as a result of K-Ras4B mutations leads to uncontrolled proliferation, with the dimer/multimerization of K-Ras4B on the plasma membrane believed to be the initiating event for subsequent MAPK/ERK signaling. While K-Ras4B proteins are known to cluster on the plasma membrane, whether they associate through well-defined dimerization interfaces remains an open question. Here, we present the dimerization landscape of active, GTP-bound wild-type and G12D mutant K-Ras4B using coarse-grained unbiased and enhanced sampling molecular dynamics simulations. We recover the experimentally-reported K-Ras4B interfaces, and additionally unveil rugged free energy landscapes with many -yet uncharacterized- minima that feature c-Raf-mediated dimerization interfaces. We further explore whether wild-type or G12D K-Ras4B present different dimerization states, revealing that the G12D mutant is more likely to form diverse dimers compared to WT K-Ras4B. Our work presents evidence that K-Ras4B proteins likely interact through multifaceted interfaces that may enable controlled dimerization in different conformations from a single system, efficiently promoting nanoclustering. Although many weak, non-specific interfaces are forming, the most dominant interfaces occur with nanomolar affinity, offering a structural basis for the design of ligands able to modulate K-Ras4B dimers.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".