Building accurate protein models into moderate resolution (3–5 Å) 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 low- and 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. As modelers 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.

将准确的蛋白质模型构建到中等分辨率 (3–5 Å) 冷冻电子显微镜 (cryo-EM) 图谱中具有挑战性且容易出错。我们开发了 MEDIC（冷冻电镜模型错误检测），这是一种强大的统计模型，通过将局部拟合密度与深度学习衍生的结构信息相结合，识别冷冻电镜图谱中内置的蛋白质结构中的局部骨干错误。 MEDIC 在一组 28 个结构上进行了验证，这些结构随后被解决为更高分辨率，我们以 68% 的精度和 60% 的召回率识别低分辨率和高分辨率结构之间的差异。我们还使用该模型修复了 12 个沉积结构中的 100 多个错误，并以 80% 的精度和 60% 的召回率识别 4 个改进的 AlphaFold 预测中的错误。随着建模者更频繁地使用深度学习预测作为细化和重建的起点，MEDIC 处理源自手工构建和机器学习方法的结构错误的能力使其成为结构生物学家的强大工具。