Robust residue-level error detection in cryo-electron microscopy models

Building accurate protein models into moderate resolution (3-5[A]) cryo-electron microscopy (cryo-EM) maps is challenging and error-prone. While the majority of solved cryo-EM structures are at these resolutions, there are few model validation metrics that can precisely evaluate the local quality of atomic models built into these maps. We have developed MEDIC (Model Error Detection in Cryo-EM), a robust statistical model to identify residue-level errors in protein structures built into cryo-EM maps. Trained on a set of errors from obsoleted protein structures, our model draws off two major sources of information to predict errors: the local agreement of model and map compared to expected, and how "native-like" the neighborhood around a residue looks, as predicted by a deep learning model. MEDIC is validated on a set of 28 structures that were subsequently solved to higher-resolutions, where our model identifies the differences between low- and high-resolution structures with 68% precision and 60% recall. We additionally use this model to rebuild 12 deposited structures, fixing 2 sequence registration errors, 51 areas with improper secondary structure, 51 incorrect loops, and 16 incorrect carbonyls, showing the value of this approach to guide model building.