Beyond histones – the expanding roles of protein lysine methylation
Bibliographic record
Abstract
A robust signaling network is essential for cell survival. At the molecular level, this is often mediated by as many as 200 different types of post-translational modifications (PTMs) that are made to proteins. These include well-documented examples such as phosphorylation, ubiquitination, acetylation and methylation. Of these modifications, non-histone protein lysine methylation has only recently emerged as a prevalent modification occurring on numerous proteins, thus extending its role well beyond the histone code. To date, this modification has been found to regulate protein activity, protein-protein interactions and interplay with other PTMs. As a result, lysine methylation is now known to be a coordinator of protein function and is a key driver in several cellular signaling events. Recent advances in mass spectrometry have also allowed the characterization of a growing number of lysine methylation events on an increasing number of proteins. As a result, we are now beginning to recognize lysine methylation as a dynamic event that is involved in a number of biological processes, including DNA damage repair, cell growth, metabolism and signal transduction among others. In light of current research advances, the stage is now set to study the extent of lysine methylation that exists within the entire proteome, its dynamics, and its association with physiological and pathological processes.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".