Development of Complementary Photo‐arginine/lysine to Promote Discovery of Arg/Lys hPTMs Interactomes
Bibliographic record
Abstract
Arginine and lysine, frequently appearing as a pair on histones, have been proven to carry diverse modifications and execute various epigenetic regulatory functions. However, the most context-specific and transient effectors of these marks, while significant, have evaded study as detection methods have thus far not reached a standard to capture these ephemeral events. Herein, a pair of complementary photo-arginine/δ-photo-lysine (R-dz/K-dz) probes is developed and involve these into histone peptide, nucleosome, and chromatin substrates to capture and explore the interactomes of Arg and Lys hPTMs. By means of these developed tools, this study identifies that H3R2me2a can recruit MutS protein homolog 6 (MSH6), otherwise repelDouble PHD fingers 2 (DPF2), Retinoblastoma binding protein 4/7 (RBBP4/7). And it is disclosed that H3R2me2a inhibits the chromatin remodeling activity of the cBAF complex by blocking the interaction between DPF2 (one component of cBAF) and the nucleosome. In addition, the novel pairs of H4K5 PTMs and respective readers are highlighted, namely H4K5me-Lethal(3)malignant brain tumor-like protein 2 (L3MBTL2), H4K5me2-L3MBTL2, and H4K5acK8ac-YEATS domain-containing protein 4 (YEATS4). These powerful tools pave the way for future investigation of related epigenetic mechanisms including but not limited to hPTMs.
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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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".