Multi-scale perturbations of protein interactomes reveal their mechanisms of regulation, robustness and insights into genotype–phenotype maps
Bibliographic record
Abstract
Cellular architectures and signaling machineries are organized through protein-protein interactions (PPIs). High-throughput methods to study PPIs in yeast have opened a new perspective on the organization of the cell by allowing the study of whole protein interactomes. Recent investigations have moved from the description of this organization to the analysis of its dynamics by experimenting how protein interaction networks (PINs) are rewired in response to perturbations. Here we review studies that have used the budding yeast as an experimental system to explore these altered networks. Given the large space of possible PPIs and the diversity of potential genetic and environmental perturbations, high-throughput methods are an essential requirement to survey PIN perturbations on a large scale. Network perturbations are typically conceptualized as the removal of entire proteins (nodes), the modification of single PPIs (edges) or changes in growth conditions. These studies have revealed mechanisms of PPI regulation, PIN architectural organization, robustness and sensitivity to perturbations. Despite these major advances, there are still inherent limits to current technologies that lead to a trade-off between the number of perturbations and the number of PPIs that can be considered simultaneously. Nevertheless, as we exemplify here, targeted approaches combined with the existing resources remain extremely powerful to explore the inner organization of cells and their responses to perturbations.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".