美国普渡大学Daisuke Kihara研究团队利用深度学习技术，开发出能够在中等分辨率冷冻电镜图谱中检测蛋白质二级结构的方法。该研究于2019年9月发表于国际一流学术期刊《自然—方法学》上。

研究人员报道了一种叫做Emap2sec的计算方法，它在分辨率为5到10埃的EM图中识别蛋白质的二级结构（α-螺旋、β-折叠和其他结构）。Emap2sec使用三维深度卷积神经网络为EM映射中的每个网格点分配二级结构。研究人员在分辨率为6.0和10.0埃的34个结构模拟的EM图上测试了Emap2sec，并在5.0到9.5埃分辨率下通过实验确定的43个图上测试了Emap2sec。Emap2sec能够在许多测试的图谱中清楚地识别二级结构，并且表现出比现有方法更好的性能。

据了解，尽管现在通过冷冻电镜（cryo-EM）可以常规地报道以近原子分辨率的确定结构，但是仍以中等分辨率确定许多密度图，并且从这些图提取结构信息仍然是一个挑战。

附：英文原文

Title: Protein secondary structure detection in intermediate-resolution cryo-EM maps using deep learning

Author: Sai Raghavendra Maddhuri Venkata Subramaniya, Genki Terashi, Daisuke Kihara

Issue&Volume: Volume 16 Issue 9

Abstract: Although structures determined at near-atomic resolution are now routinely reported by cryo-electron microscopy (cryo-EM), many density maps are determined at an intermediate resolution, and extracting structure information from these maps is still a challenge. We report a computational method, Emap2sec, that identifies the secondary structures of proteins (α-helices, β-sheets and other structures) in EM maps at resolutions of between 5 and 10 Å. Emap2sec uses a three-dimensional deep convolutional neural network to assign secondary structure to each grid point in an EM map. We tested Emap2sec on EM maps simulated from 34 structures at resolutions of 6.0 and 10.0 Å, as well as on 43 maps determined experimentally at resolutions of between 5.0 and 9.5 Å. Emap2sec was able to clearly identify the secondary structures in many maps tested, and showed substantially better performance than existing methods.

DOI: 10.1038/s41592-019-0500-1

Source:https://www.nature.com/articles/s41592-019-0500-1