Uncovering Enzyme-Specific Post-Translational Modifications: An Overview of Current Methods
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it