Cao Hong, He Jiahua, Li Tao, Huang Sheng-You
School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, P. R. China.
J Chem Inf Model. 2025 Feb 10;65(3):1641-1652. doi: 10.1021/acs.jcim.4c01971. Epub 2025 Jan 21.
With the resolution revolution of cryo-electron microscopy (cryo-EM) and the rapid development of image processing technology, cryo-EM has become an indispensable experimental method for determining the three-dimensional structures of biological macromolecules. However, structural modeling from cryo-EM maps remains a difficult task for intermediate-resolution maps. In such cases, detection of protein secondary structures and nucleic acid locations in an EM map is of great value for model building of the map. Meeting the need, we present a deep learning-based method for detecting protein secondary structures and nucleic acid locations in cryo-EM density maps, named EMInfo. EMInfo was extensively evaluated on two protein-nucleic acid complex test sets including intermediate-resolution experimental maps and high-resolution experimental maps and compared them with two state-of-the-art methods including Emap2sec+ and Haruspex. It is shown that EMInfo can accurately predict different structural categories in an EM map. EMInfo is freely available at http://huanglab.phys.hust.edu.cn/EMInfo/.
随着冷冻电子显微镜（cryo-EM）的分辨率革命以及图像处理技术的快速发展，cryo-EM已成为确定生物大分子三维结构不可或缺的实验方法。然而，对于中等分辨率的图谱，从cryo-EM图谱进行结构建模仍然是一项艰巨的任务。在这种情况下，在电子显微镜图谱中检测蛋白质二级结构和核酸位置对于该图谱的模型构建具有重要价值。为满足这一需求，我们提出了一种基于深度学习的方法，用于在冷冻电子显微镜密度图中检测蛋白质二级结构和核酸位置，名为EMInfo。EMInfo在两个蛋白质-核酸复合物测试集上进行了广泛评估，包括中等分辨率实验图谱和高分辨率实验图谱，并将其与两种最先进的方法Emap2sec+和Haruspex进行了比较。结果表明，EMInfo可以准确预测电子显微镜图谱中的不同结构类别。EMInfo可在http://huanglab.phys.hust.edu.cn/EMInfo/免费获取。
