Multivalent assembly of KRAS with the RAS-binding and cysteine-rich domains of CRAF on the membrane
Bibliographic record
Abstract
Membrane anchoring of farnesylated KRAS is critical for activation of RAF kinases, yet our understanding of how these proteins interact on the membrane is limited to isolated domains. The RAS-binding domain (RBD) and cysteine-rich domain (CRD) of RAF engage KRAS and the plasma membrane, unleashing the kinase domain from autoinhibition. Due to experimental challenges, structural insight into this tripartite KRAS:RBD-CRD:membrane complex has relied on molecular dynamics simulations. Here, we report NMR studies of the KRAS:CRAF RBD-CRD complex. We found that the nucleotide-dependent KRAS-RBD interaction results in transient electrostatic interactions between KRAS and CRD, and we mapped the membrane interfaces of the CRD, RBD-CRD, and the KRAS:RBD-CRD complex. RBD-CRD exhibits dynamic interactions with the membrane through the canonical CRD lipid-binding site (CRD β7-8), as well as an alternative interface comprising β6 and the C terminus of CRD and β2 of RBD. Upon complex formation with KRAS, two distinct states were observed by NMR: State A was stabilized by membrane association of CRD β7-8 and KRAS α4-α5 while state B involved the C terminus of CRD, β3-5 of RBD, and part of KRAS α5. Notably, α4-α5, which has been proposed to mediate KRAS dimerization, is accessible only in state B. A cancer-associated mutation on the state B membrane interface of CRAF RBD (E125K) stabilized state B and enhanced kinase activity and cellular MAPK signaling. These studies revealed a dynamic picture of the assembly of the KRAS-CRAF complex via multivalent and dynamic interactions between KRAS, CRAF RBD-CRD, and the membrane.
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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".