Widespread alteration of protein autoinhibition in human cancers
Bibliographic record
Abstract
ABSTRACT Autoinhibition is a prevalent allosteric regulatory mechanism in signaling proteins as it prevents spurious pathway activation and primes for signal propagation only under appropriate inputs. Altered functioning of inhibitory allosteric switches underlies the tumorigenic potential of numerous cancer drivers. However, whether protein autoinhibition is altered generically in cancer cells remains elusive. Here, we reveal that cancer-associated missense mutations and fusion breakpoints are found with significant enrichment within inhibitory allosteric switches across all cancer types, which in the case of the fusion breakpoints is specific to cancer and not present in other diseases. Recurrently disrupted or mutated allosteric switches identify established and new cancer drivers. Cancer-specific mutations in allosteric switches are associated with distinct changes in signaling, and suggest molecular mechanisms for altered protein regulation, which in the case of ASK1, DAPK2 and EIF4G1 were supported by biophysical simulations. Our results demonstrate that autoinhibition-modulating genetic alterations are positively selected for by cancer cells, and that their study provides valuable insights into molecular mechanisms of cancer misregulation.
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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".