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

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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.
Funding Information
  • Biotechnology and Biological Sciences Research Council (Artificial Intelligence in the Biosciences - AIBIO-UK (22-AIBN) [BB/Y006933/1)
  • Engineering and Physical Sciences Research Council (BASE-II ExCALIBUR project (EPSRC grant number EP/X019918/1))
  • ADA Lovelace Centre