Reggiano, Gabriella / Lugmayr, Wolfgang / Farrell, Daniel / Marlovits, Thomas C. / DiMaio, Frank. Residue-level error detection in cryoelectron microscopy models. 2023. Structure, 31, 1-10
Building accurate protein models into moderate resolution (3–5A) cryoelectron microscopy (cryo-EM) maps is challenging and error prone. We have developed MEDIC (Model Error Detection in Cryo-EM), a robust statistical model that identifies local backbone errors in protein structures built into cryo-EM maps by combining local fit-to-density with deep-learning-derived structural information. MEDIC is validated on a set of 28 structures that were subsequently solved to higher resolutions, where we identify the differences between lowand high-resolution structures with 68% precision and 60% recall. We additionally use this model to fix over 100 errors in 12 deposited structures and to identify errors in 4 refined AlphaFold predictions with 80% precision and 60% recall. Asmodelers more frequently use deep learning predictions as a starting point for refinement and rebuilding, MEDIC’s ability to handle errors in structures derived from hand-building and machine learning methods makes it a powerful tool for structural biologists.