A Chemical Method for Labeling Lysine Methyltransferase Substrates
Bibliographic record
Abstract
Several protein lysine methyltransferases (PKMTs) modify histones to regulate chromatin-dependent cellular processes, such as transcription, DNA replication and DNA damage repair. PKMTs are likely to have many additional substrates in addition to histones, but relatively few nonhistone substrates have been characterized, and the substrate specificity for many PKMTs has yet to be defined. Thus, new unbiased methods are needed to find PKMT substrates. Here, we describe a chemical biology approach for unbiased, proteome-wide identification of novel PKMT substrates. Our strategy makes use of an alkyne-bearing S-adenosylmethionine (SAM) analogue, which is accepted by the PKMT, SETDB1, as a cofactor, resulting in the enzymatic attachment of a terminal alkyne to its substrate. Such labeled proteins can then be treated with azide-functionalized probes to ligate affinity handles or fluorophores to the PKMT substrates. As a proof-of-concept, we have used SETDB1 to transfer the alkyne moiety from the SAM analogue onto a recombinant histone H3 substrate. We anticipate that this chemical method will find broad use in epigenetics to enable unbiased searches for new PKMT substrates by using recombinant enzymes and unnatural SAM cofactors to label and purify many substrates simultaneously from complex organelle or cell extracts.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".