mChIP-KAT-MS, a method to map protein interactions and acetylation sites for lysine acetyltransferases
Bibliographic record
Abstract
Recent global proteomic and genomic studies have determined that lysine acetylation is a highly abundant posttranslational modification. The next challenge is connecting lysine acetyltransferases (KATs) to their cellular targets. We hypothesize that proteins that physically interact with KATs may not only predict the cellular function of the KATs but may be acetylation targets. We have developed a mass spectrometry-based method that generates a KAT protein interaction network from which we simultaneously identify both in vivo acetylation sites and in vitro acetylation sites. This modified chromatin-immunopurification coupled to an in vitro KAT assay with mass spectrometry (mChIP-KAT-MS) was applied to the Saccharomyces cerevisiae KAT nucleosome acetyltransferase of histone H4 (NuA4). Using mChIP-KAT-MS, we define the NuA4 interactome and in vitro-enriched acetylome, identifying over 70 previously undescribed physical interaction partners for the complex and over 150 acetyl lysine residues, of which 108 are NuA4-specific in vitro sites. Through this method we determine NuA4 acetylation of its own subunit Epl1 is a means of self-regulation and identify a unique link between NuA4 and the spindle pole body. Our work demonstrates that this methodology may serve as a valuable tool in connecting KATs with their cellular targets.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".