Uncovering allosteric communication in cancer-related histone mutations
Bibliographic record
Abstract
Abstract Background Histone mutations have been implicated in various cancers, but their mechanistic effects on chromatin dynamics remain largely unexplored. Aim To investigate how allosteric communication influences the functional outcomes of cancer-linked histone mutations. Methods We analyzed forty cancer-associated histone missense mutations using integrated computational modeling and experimental validation to evaluate their long-range structural and functional consequences. Results The allosteric effects of histone mutations were highly position-dependent. Mutations near the N-terminal tails of histones H3 and H4 caused the most pronounced conformational perturbations. Seven histone mutations were predicted to have strong allosteric signatures potentially influencing nucleosome interactions. Experimental assays confirmed that H2BS64Y and H2BS64F substitutions impaired histone function, disrupted H2BK120 ubiquitination, and decreased genome stability. Conclusions These findings indicate that certain histone mutations may exert oncogenic potential through long-range allosteric mechanisms, underscoring the importance of allostery as a key regulatory principle in cancer epigenetics.
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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 | 0.001 |
| 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".