SET7/9 Catalytic Mutants Reveal the Role of Active Site Water Molecules in Lysine Multiple Methylation
Bibliographic record
Abstract
SET domain lysine methyltransferases (KMTs) methylate specific lysine residues in histone and non-histone substrates. These enzymes also display product specificity by catalyzing distinct degrees of methylation of the lysine ε-amino group. To elucidate the molecular mechanism underlying this specificity, we have characterized the Y245A and Y305F mutants of the human KMT SET7/9 (also known as KMT7) that alter its product specificity from a monomethyltransferase to a di- and a trimethyltransferase, respectively. Crystal structures of these mutants in complex with peptides bearing unmodified, mono-, di-, and trimethylated lysines illustrate the roles of active site water molecules in aligning the lysine ε-amino group for methyl transfer with S-adenosylmethionine. Displacement or dissociation of these solvent molecules enlarges the diameter of the active site, accommodating the increasing size of the methylated ε-amino group during successive methyl transfer reactions. Together, these results furnish new insights into the roles of active site water molecules in modulating lysine multiple methylation by SET domain KMTs and provide the first molecular snapshots of the mono-, di-, and trimethyl transfer reactions catalyzed by these enzymes.
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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 | 0.001 |
| 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".