Deciphering Allosteric Modulation of Cancer-Associated Histone Missense Mutations
Bibliographic record
Abstract
• Examined position-specific allosteric effects for 40 cancer histone mutations. • 20% of mutations exhibited strong allosteric effects, especially those in the N-terminal regions of H3 and H4 histones. • Evidence of allosteric effects for the H2BS64Y and S64F mutations, showed that they alter H2BK120 ubiquitination levels and genome stability. Histone mutations have been implicated in various cancers, but their mechanistic effects on chromatin dynamics remain largely unexplored. In this study, we investigated allosteric modulation effects induced by 40 cancer-associated histone missense mutations. By combining computational approaches with experimental evidence, we assessed the allosteric and functional impact of these mutations. Our results reveal that the allosteric effects of histone mutations are position-specific, with mutations near the H3 and H4 histone N-terminal tails exhibiting the strongest long-range perturbations. Notably, we predicted seven mutations with significant allosteric effects, potentially altering nucleosome interactions. Experimental verification of H2BS64Y and H2BS64F mutations demonstrated that they disrupted normal histone function, altered H2BK120 ubiquitination levels and genome stability, findings suggestive of their potential carcinogenic effects. Collectively, these results show that allostery may serve as a critical mechanism underlying the oncogenic potential of some histone mutations, highlighting the need for further exploration of allosteric pathways 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 | 0.000 |
| 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".