Residue-level error detection in cryoelectron microscopy models.
Residue-level error detection in cryoelectron microscopy models.
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DOI：
10.1016/j.str.2023.05.002
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发表时间：
2023-07-06
期刊：
影响因子：
5.7
通讯作者：
DiMaio, Frank
中科院分区：
文献类型：
--
作者：
Reggiano, Gabriella;Lugmayr, Wolfgang;Farrell, Daniel;Marlovits, Thomas C.;DiMaio, Frank
将精确的蛋白质模型构建到中等分辨率（3- 5 μ m）的冷冻电子显微镜（cryo-EM）图谱中具有挑战性且容易出错。尽管如此，很少有模型验证指标可以精确地评估这些映射中内置的原子模型的局部质量。我们开发了MEDIC（Model Error Detection in Cryo-EM），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cryo-EM图谱中构建的蛋白质结构中的局部骨架错误。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的分辨率，我们以68%的准确率和60%的召回率确定了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%的准确率和60%的召回率识别了4个改进的AlphaFold预测中的错误。随着建模人员越来越频繁地使用深度学习预测作为改进和重建的起点，MEDIC处理手工构建和机器学习方法产生的结构错误的能力使其成为结构生物学家的强大工具。
Building accurate protein models into moderate resolution (3–5Å) cryo-electron microscopy (cryo-EM) maps is challenging and error-prone. Despite this, 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 which 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 in12 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.
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影响因子：
48
作者：
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影响因子：
64.8
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影响因子：
48
作者：
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通讯作者：
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