Assessment of protein-protein interfaces in cryo-EM derived assemblies

Structures of macromolecular assemblies derived from cryo-EM maps often contain errors that become more abundant with decreasing resolution. Despite efforts in the cryo-EM community to develop metrics for map and atomistic model validation, thus far, no specific scoring metrics have been applied systematically to assess the interface between the assembly subunits. Here, we comprehensively assessed protein-protein interfaces in macromolecular assemblies derived by cryo-EM. To this end, we developed Protein Interface-score (PI-score), a density-independent machine learning-based metric, trained using the features of protein-protein interfaces in crystal structures. We evaluated 5873 interfaces in 1053 PDB-deposited cryo-EM models (including SARS-CoV-2 complexes), as well as the models submitted to CASP13 cryo-EM targets and the EM model challenge. We further inspected the interfaces associated with low-scores and found that some of those, especially in intermediate-to-low resolution (worse than 4 angstrom) structures, were not captured by density-based assessment scores. A combined score incorporating PI-score and fit-to-density score showed discriminatory power, allowing our method to provide a powerful complementary assessment tool for the ever-increasing number of complexes solved by cryo-EM. Here, the authors present Protein Interface-score (PI-score), a machine learning-based metric that has been trained on protein-protein interfaces' features from high-resolution crystal structures. They use the PI-score to evaluate the protein-protein interfaces in more than 1000 PDB-deposited cryo-EM structures and show that it can be used as a complementary assessment tool for cryo-EM model validation.

Automated summary

The researchers developed a machine learning-based metric called PI-score to assess protein-protein interfaces in cryo-EM structures, demonstrating its effectiveness as a complementary assessment tool for cryo-EM model validation.