The Effect of Insertions and Deletions on Wirings in Protein-Protein Interaction Networks: A Large-Scale Study
Bibliographic record
Abstract
Although insertions and deletions (indels) are a common type of sequence variation, their origin and their functional consequences have not yet been fully understood. It has been known that indels preferably occur in the loop regions of the affected proteins. Moreover, it has recently been demonstrated that indels are significantly more strongly correlated with functional changes than substitutions. In sum, there is substantial evidence that indels, not substitutions, are the predominant evolutionary factor when it comes to structural changes in proteins. As a consequence it comes natural to hypothesize that sizable indels can modify protein interaction interfaces, causing a gain or loss of protein-protein interactions, thereby significantly rewiring the interaction networks. In this paper, we have analyzed this relationship in a large-scale study. We have computed all paralogous protein pairs in Saccharomyces cerevisiae (Yeast) and Drosophila melanogaster (Fruit Fly), and sorted the respective alignments according to whether they contained indels of significant lengths as per a pair Hidden Markov Model (HMM)-based framework of a recent study. We subsequently computed well known centrality measures for proteins that participated in indel alignments (indel proteins) and those that did not. We found that indel proteins indeed showed greater variation in terms of these measures. This demonstrates that indels have a significant influence when it comes to rewiring of the interaction networks due to evolution, which confirms our hypothesis. In general, this study may yield relevant insights into the functional interplay of proteins and the evolutionary dynamics behind it.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 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".