Department of Biological Sciences, Purdue University, West Lafayette, IN, USA.
Department of Computer Science, Purdue University, West Lafayette, IN, USA.
Nat Methods. 2022 Sep;19(9):1116-1125. doi: 10.1038/s41592-022-01574-4. Epub 2022 Aug 11.
An increasing number of protein structures are being determined by cryogenic electron microscopy (cryo-EM). Although the resolution of determined cryo-EM density maps is improving in general, there are still many cases where amino acids of a protein are assigned with different levels of confidence. Here we developed a method that identifies potential misassignment of residues in the map, including residue shifts along an otherwise correct main-chain trace. The score, named DAQ, computes the likelihood that the local density corresponds to different amino acids, atoms, and secondary structures, estimated via deep learning, and assesses the consistency of the amino acid assignment in the protein structure model with that likelihood. When DAQ was applied to different versions of model structures in the Protein Data Bank that were derived from the same density maps, a clear improvement in the DAQ score was observed in the newer versions of the models. DAQ also found potential misassignment errors in a substantial number of deposited protein structure models built into cryo-EM maps.
越来越多的蛋白质结构通过低温电子显微镜（cryo-EM）来确定。虽然确定的 cryo-EM 密度图的分辨率总体上在提高，但仍有许多情况下，蛋白质的氨基酸被赋予不同程度的置信度。在这里，我们开发了一种方法，可以识别图谱中潜在的残基误分配，包括沿正确主链轨迹的残基移动。该名为 DAQ 的评分通过深度学习计算局部密度对应不同氨基酸、原子和二级结构的可能性，并评估蛋白质结构模型中氨基酸分配与该可能性的一致性。当 DAQ 应用于源自同一密度图的蛋白质数据库中不同版本的模型结构时，观察到模型的新版本中 DAQ 评分有明显提高。DAQ 还在大量存储的蛋白质结构模型中发现了潜在的误分配错误，这些模型是根据 cryo-EM 图谱构建的。
