Characterization of KDM5 lysine demethylase family substrate preference and identification of novel substrates
Bibliographic record
Abstract
The KDM5/JARID1 sub-family are 2-oxoglutarate and Fe(II)-dependent lysine-specific histone demethylases that are characterized by their Jumonji catalytic domains. The KDM5 family is known to remove tri-/di-methyl modifications from lysine-4 of histone H3 (i.e. H3-K4me2/3), a mark associated with active gene expression. As a result, studies to date have revolved around the influence of KDM5 on disease through their ability to regulate H3-K4me2/3. Recent evidence demonstrates that KDM5 may influence disease beyond H3-K4 demethylation, making it critical to further investigate KDM5-mediated demethylation of non-histone proteins. To help identify potential non-histone substrates for the KDM5 family, we developed a library of 180 permutated peptide substrates, with sequences that are systematically altered from the wild-type H3-K4me3 substrate. From this library, we characterized recombinant KDM5A/B/C/D substrate preference and developed recognition motifs for each KDM5 demethylase. The recognition motifs developed were used to predict potential substrates for KDM5A/B/C/D and profiled to generate a list of high-ranking and medium/low-ranking substrates for further in vitro validation. Through this approach, we identified 66 high-ranking substrates in which KDM5 demethylases displayed significant in vitro activity towards.
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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 | 0.001 |
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".