DPI-score: A deep learning-based metric for assessing protein-protein interfaces in cryoEM derived assemblies

Abstract Advances in cryoEM have led to a surge in high-resolution structures; however, model building and refinement at resolutions (>=3 Å) and in regions with variable local resolution remain challenging, making validation essential. CryoEM derived assemblies often contain extensive protein–protein interfaces, however, most existing validation metrics focus on density fit or overall geometry without directly assessing interface quality. To address this, we present DPI-Score, a deep learning-based metric for assessing protein-protein interfaces in cryoEM derived complexes. The method uses only raw structural coordinates of interface atoms, without requiring engineered features, and achieves 87.53% validation accuracy. DPI-Score was applied to 29,120 interfaces from 6,011 fitted entries with resolutions worse than 3 Å in the Electron Microscopy Data Bank. Here, we show that DPI-Score provides complementary information to existing validation metrics and can identify interface errors in modelled assemblies that are not detected by density-based scores alone.