Proteome-wide prediction of acetylation substrates
Bibliographic record
Abstract
Acetylation is a well-studied posttranslational modification that has been associated with a broad spectrum of biological processes, notably gene regulation. Many studies have contributed to our knowledge of the enzymology underlying acetylation, including efforts to understand the molecular mechanism of substrate recognition by several acetyltransferases, but traditional experiments to determine intrinsic features of substrate site specificity have proven challenging. Here, we combine experimental methods with clustering analysis of protein sequences to predict protein acetylation based on the sequence characteristics of acetylated lysines within histones with our unique prediction tool PredMod. We define a local amino acid sequence composition that represents potential acetylation sites by implementing a clustering analysis of histone and nonhistone sequences. We show that this sequence composition has predictive power on 2 independent experimental datasets of acetylation marks. Finally, we detect acetylation for selected putative substrates using mass spectrometry, and report several nonhistone acetylated substrates in budding yeast. Our approach, combined with more traditional experimental methods, may be useful for identifying acetylated substrates proteome-wide.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".