MethylSight: Taking a wider view of lysine methylation through computer-aided discovery to provide insight into the human methyl-lysine proteome
Bibliographic record
Abstract
Post-translational lysine methylation has been found to play a fundamental role in the regulation of protein function and the transmission of biological signals. We present the development of a machine learning model for predicting lysine methylation sites among human proteins. The model uses fully-alignment-free features encoding sequence-based information. A total of 57 novel predicted histone methylation sites were selected for evaluation by targeted mass spectrometry, with 51 sites positively re-assigned as true methylated sites, while one site was also found to be dynamically responsive to DNA damage. To gain insight into the cellular function of the lysine methylation system, we reveal links between cellular metabolic and GTPase signal transduction, demonstrating a dynamic hypoxia-responsive methylation of the inducible nitric oxide synthase (NOS2). With the growing implication of lysine methylation in human health and disease, the development of methods that help to target its discovery will become of critical importance to understanding its biological implications.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".