An optimized method using peptide arrays for the identification of in vitro substrates of lysine methyltransferase enzymes
Bibliographic record
Abstract
While a number of post-translational modifications (PTM), such as phosphorylation and ubiquitination, have been extensively studied, lysine methylation is emerging as an important PTM with implications in a growing number of diverse cellular processes. To date, there are approximately 5000 identified methylation sites on non-histone proteins, and as the methyllysine proteome expands it becomes important to identify the lysine methyltransferase enzymes responsible for each methylation event. The use of peptide SPOT methylation assay has proven to be a useful in the identification and validation of novel substrates for lysine methyltransferase enzymes as it uses a weak beta emitter coupled with fluorography to detect methylation events. The method described in this paper provides improvements to the typical protocol for this assay, as a highly sensitive tritium assay can be developed with less radioactivity than previously described. This protocol provides an inexpensive alternative to weak beta signal enhancer sprays and washes for use in lysine methylation peptide SPOT arrays, and a simple open-source method for array quantification.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.005 |
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".