Effects of post-translational modifications on protein function
Bibliographic record
Abstract
Over 200 distinct types of post-translational modifications have been identified in eukaryotic proteomes, yet the functional consequences of most remain poorly characterized. This research investigated the effects of major post-translational modifications on protein function using an integrated computational and experimental approach. A dataset comprising 847 well-characterized proteins from human cell lines was analyzed for modification patterns and functional outcomes between January 2021 and November 2022 at the Westbrook Institute of Biomedical Research. Mass spectrometry-based proteomics identified modification sites, while functional assays assessed enzymatic activity, protein stability, subcellular localization, and protein-protein interactions. Phosphorylation represented the most prevalent modification at 38.7% of detected sites, followed by ubiquitination at 22.4% and acetylation at 15.3%. Functional impact analysis revealed that phosphorylation predominantly affected enzymatic activity with 82.1% of phosphorylated enzymes showing altered catalytic parameters. Ubiquitination primarily targeted proteins for degradation, reducing stability in 76.8% of modified substrates. Acetylation demonstrated the strongest influence on DNA-binding proteins, with 78.4% of acetylated transcription factors exhibiting modified binding affinity. Glycosylation showed particular importance for protein stability at 81.2% and subcellular localization at 62.4%. Heatmap analysis of modification-function relationships revealed context-dependent effects whereby identical modifications produced opposing outcomes depending on target protein identity and cellular environment. The research identified 127 proteins subject to crosstalk between multiple modification types, suggesting coordinated regulatory networks. These findings establish quantitative relationships between specific modifications and functional outcomes, providing a framework for predicting how alterations in modification machinery contribute to disease pathogenesis and identifying potential therapeutic intervention points.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".