Structure-guided evolutionary analysis of protein-protein interactions and interactome network rewiring at single residue resolution in yeasts.
Bibliographic record
Abstract
Protein-protein interactions, or PPIs, are important phenomena, essential to proper protein function, and present in virtually all biological pathways of cells.Accordingly, in recent years, numerous experiments have been performed to survey all proteins that interact in a given species, as well as to uncover the molecular structure and 3D mechanisms of interactions between individual proteins.So far, this extensive work has generated large amounts of data, which now allows us to study the evolution of PPIs, a feat that was previously difficult due to a lack of highquality experimental results.An investigation into the evolution of PPIs is essential to try and uncover the evolutionary design principles behind variations in PPIs, both within and between species.Here, we take advantage of PPI datasets made recently available for two yeast species, Saccharomyces cerevisiae (S. cerevisiae), and Schizosaccharomyces pombe (S. pombe), and perform their thorough analysis using bioinformatics tools.We first design a custom script pipeline to automate the curation of high-quality protein-protein interaction data from online databases and organize this data into structural models of PPIs for the two yeast species, S. cerevisiae, and S. pombe.These structural models are subsequently used to investigate the relationship between PPI structure and PPI evolution in yeast at the single residue level.This analysis yields significant insight into the design principles and structural mechanisms governing PPI evolution in yeast, uncovering several structural properties directly correlated with the evolutionary rates of PPIs.Finally, we use structural models of S. cerevisiae and S. pombe PPIs to construct structurallyresolved interactome networks for the two yeasts and compare PPIs that are preserved and PPI that are different between the two yeast species.This analysis yields further insight into the evolutionary design principles of PPIs and the mechanisms by which interactions are preserved or 3.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".