Reduced False Positives in PDZ Binding Prediction Using Sequence and Structural Descriptors
Bibliographic record
Abstract
Abstract—Identifying the binding partners of proteins is a problem of fundamental importance in computational biology. The PDZ is one of the most common and well-studied protein binding domains, hence it is a perfect model system for designing protein binding predictors. The standard approach to identifying the binding partners of PDZ domains uses multiple sequence alignments to infer the set of contact residues that are used in a predictive model. We expand on the sequence alignment approach by incorporating structural information to generate descriptors of the binding site geometry. Furthermore, we generate a real-value score for binary predictions by applying a filter based on models that predict the probability distributions of contact residues at each of the canonical PDZ ligand binding positions. Under training cross validation, our model produced an order of magnitude more predictions at a false positive proportion (FPP) of 10 percent than our benchmark model chosen from the literature. Evaluated using an independent cross validation, with computationally predicted structures, our model was able to make five times as many predictions as the benchmark model, with a Matthews' correlation coefficient (MCC) of 0.33. In addition, our model achieved a false positive proportion of 0.14, while the benchmark model had a 0.25 false positive proportion.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".