美国普渡大学Daisuke Kihara课题组的最新研究利用冷冻电镜(cryo-EM)图完成了对蛋白质模型中残基局部质量估计。相关论文于2022年8月11日发表于国际学术期刊《自然-方法学》杂志上。

研究人员了研发了一种方法，可识别冷冻电镜图中氨基酸残基的潜在错误分配，包括沿其他正确主链轨迹的残基移位。该算法名为DAQ，可计算局部密度对应于不同氨基酸、原子和二级结构的可能性，通过深度学习估计，并评估蛋白质结构模型中氨基酸分配与该算法评估的一致性。当DAQ应用于蛋白质数据库中源自相同密度图的不同模型结构时，在较新版本的模型中观察到DAQ得分明显提高。DAQ还可以在冷冻电镜图的大量沉积蛋白质结构模型中发现潜在的错误分配。

据介绍，越来越多的蛋白质结构可通过冷冻电镜解析。尽管冷冻电镜密度图分辨率总体上有所提高，但仍存在许多情况，蛋白质中氨基酸具有不同的置信度。

附：英文原文

Title: Residue-wise local quality estimation for protein models from cryo-EM maps

Author: Terashi, Genki, Wang, Xiao, Maddhuri Venkata Subramaniya, Sai Raghavendra, Tesmer, John J. G., Kihara, Daisuke

Issue&Volume: 2022-08-11

Abstract: 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.

DOI: 10.1038/s41592-022-01574-4

Source: https://www.nature.com/articles/s41592-022-01574-4