Error-Controlled Discovery of Protein Interactions via Conformal Calibration

Protein language models (PLMs) are powerful predictors of protein interactions, but strong benchmark performance does not ensure reliable discoveries. We introduce PPI-Caliper, a model-agnostic framework that adapts conformal calibration to select interactions at user-specified false discovery rate (FDR) levels using held-out labels without retraining the predictor. Across five species, two PLM-based predictors, and two human training-data versions for each predictor, PPI-Caliper keeps empirical FDR at or below the target level under in-distribution calibration. Under distribution shift, transferred thresholds tend to become conservative when predictive performance improves, but can fail to control FDR when performance deteriorates. PPI-Caliper connects pre-trained interaction scores to explicit error targets and demonstrates why reliable biological discovery requires evaluating calibration alongside predictive accuracy.