<scp>PPISHES</scp> —an enhanced physicochemical approach for predicting protein interaction sites using graph neural networks
Bibliographic record
Abstract
Accurate prediction of protein interaction sites is crucial for understanding biological processes. Many existing methods capture structural, evolutionary, and sequence features; however, they overlook important physicochemical properties, limiting their performance. We propose Prediction of protein interaction sites based on solvent accessible surface area, hydrogen-bonding propensity, and electrostatic potential sites, an enhanced model that incorporates the three physicochemical features to improve site prediction for both obligate and non-obligate complexes. Feature ablation and other analyses identified key features to improve model performance. The model achieved up to 42.8% and 29.3% improvements in Area Under the Precision-Recall Curve for Test_315 and Test_71, respectively. The model also outperforms current state-of-the-art methods across other key metrics, such as Recall, Area Under the Curve, and Matthews Correlation Coefficient.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".