Molecular Evolution of Posttranslational Regulation in Intrinsically Disordered Regions
Bibliographic record
Abstract
Protein posttranslational regulation is a major facet of protein function, and efforts have been made to systematically characterize the level of control of proteins. For example, systematic determination of protein localization, phosphorylation, and interactions have allowed the examination of the regulatory network of the cell. However, the study of the evolution of this underlying regulatory network requires a higher resolution analysis of the sequences that are responsible for this level of control. The overarching goal of this thesis was to examine the role of the evolution of protein regulatory sequences as a molecular mechanism driving functional diversity. I developed computational tools and methods for the identification and characterization of these regulatory sequences, as well as experimental approaches to study the evolutionary impact of changes within these sequences.I first characterized the evolution of phosphorylation sites and used the property that they are strongly conserved relative to their flanking disordered regions as a computational means to systematically identify motifs in the budding yeast proteome. These results suggest that incorporating evolutionary conservation is sufficient for the prediction of around 30% of the known short linear motifs. Applying these computational approaches to the budding yeast proteome showed that thousands of short linear motifs exist and still remain uncharacterized.Using a relative rates test, I showed that motifs frequently change selective constraints after gene duplication and showed that these changes can alter protein regulation over evolution. Finally, I designed a high-throughput experimental pipeline to systematically, quantitatively and precisely assess the fitness consequences of rewiring a regulatory network and applied it to test whether a bi-functional protein has sub-functionalized over evolution.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".