Uncovering Enzyme-Specific Post-Translational Modifications: An Overview of Current Methods
Bibliographic record
Abstract
Post-translational modifications (PTMs) govern a multitude of protein functions within the cell, surpassing the basic function(s) encoded directly within the amino acid sequence. Despite the historical discovery of PTMs dating back over a century, recent technological advancements have facilitated the rapid expansion of the known PTM landscape. However, the elucidation of enzyme-substrate relationships responsible for PTMs, particularly for those less studied, remains a challenging endeavor. This review provides an extensive overview of methods employed in the discovery of enzyme-specific substrates for PTM catalysis. Beginning with traditional experimental approaches rooted in chemistry, biochemistry and cell biology, this review progresses to recently developed computational strategies tailored for identifying enzyme-substrate interactions. The analysis reflects on the remarkable progress achieved in PTM research to date, underscoring the increasing role of computational and high-throughput techniques in expediting enzyme-substrate discovery. Furthermore, it highlights the potential of artificial intelligence to revolutionize PTM research and emphasizes the importance of unbiased high-throughput analysis in advancing our understanding of PTM networks. Ultimately, the review advocates for the integration of sophisticated computational strategies with experimental techniques to unravel the complex enzyme-substrate networks governing PTM-mediated cellular 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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| 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".