Comparison of sequence- and structure-based protein-protein interaction sites
Bibliographic record
Abstract
Computational protein-protein interaction (PPI) prediction is a diverse field with multiple paradigms generating insightful interaction interface information. The shortcomings of one approach are often the strength of another and establishing the agreement between methodologies is valuable for the development of novel PPI prediction techniques. This study represents the first large-scale comparison of PPI sites determined through a sequence-based method (PIPE-Sites) and a structure-based method (PiSITEs). A set of interactions (n = 3,109) amenable to analysis by both methods was examined. Interestingly, the distributions of the sizes of the predicted interaction sites have similar means and identical median values. Using the Sorensen-Dice similarity coefficient and independent randomization testing, we determined the degree of agreement of the predicted sites of interaction for both methods to be statistically significant (p <; 0.001). Finally, applying the hypergeometric test and Q-Analysis, we identified 491 interactions with significantly heightened agreement (p <; 0.002). These interactions represent a broad range of biological function including transcriptional regulation, cell proliferation, cytoskeletal dynamics, and apoptosis. These findings corroborate the joint application of these paradigms for future PPI prediction studies.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".