Molecular effects of cancer-associated somatic mutations on the structural and target recognition properties of Keap1
Bibliographic record
Abstract
Kelch-like ECH-associated protein 1 (Keap1) plays an important regulatory role in the nuclear factor erythroid 2-related factor 2 (Nrf2)-dependent oxidative stress response pathway. It functions as a repressor of Nrf2, a key transcription factor that initiates the expression of cytoprotective enzymes during oxidative stress to protect cells from damage caused by reactive oxygen species. Recent studies show that mutations of Keap1 can lead to aberrant activation of the antioxidant pathway, which is associated with different types of cancers. To gain a mechanistic understanding of the links between Keap1 mutations and cancer pathogenesis, we have investigated the molecular effects of a series of mutations (G333C, G350S, G364C, G379D, R413L, R415G, A427V, G430C and G476R) on the structural and target recognition properties of Keap1 by using nuclear magnetic resonance (NMR) spectroscopy, circular dichroism (CD) and isothermal titration calorimetry (ITC). Depending on their locations in the protein, these mutations are found to exert differential effects on the protein stability and target binding. Together with the proposed hinge-and-latch mechanism of Nrf2-Keap1 binding in the literature, our results provide important insight into the molecular affect of different somatic mutations on Keap1's function as an Nrf2 repressor.
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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".