Haslam Devin, Zeng Tao, Li Rongjian, He Jing
Department of Computer Science, Old Dominion University, Norfolk, VA, 23529.
Department of Computer Science, Washington State University, Pullman, WA 99164.
ACM BCB. 2018 Aug;2018:628-632. doi: 10.1145/3233547.3233704.
Cryo-electron microscopy (cryo-EM) is an emerging biophysical technique for structural determination of protein complexes. However, accurate detection of secondary structures is still challenging when cryo-EM density maps are at medium resolutions (5-10 Å). Most of existing methods are image processing methods that do not fully utilize available images in the cryo-EM database. In this paper, we present a deep learning approach to segment secondary structure elements as helices and β-sheets from medium-resolution density maps. The proposed 3D convolutional neural network is shown to detect secondary structure locations with an F1 score between 0.79 and 0.88 for six simulated test cases. The architecture was also applied to an experimentally-derived cryo-EM density map with good accuracy.
冷冻电子显微镜（cryo-EM）是一种用于确定蛋白质复合物结构的新兴生物物理技术。然而，当中等分辨率（5-10埃）的冷冻电镜密度图用于二级结构的准确检测时，仍然具有挑战性。现有的大多数方法都是图像处理方法，没有充分利用冷冻电镜数据库中的可用图像。在本文中，我们提出了一种深度学习方法，用于从中等分辨率的密度图中分割出作为螺旋和β折叠的二级结构元件。对于六个模拟测试案例，所提出的3D卷积神经网络能够以0.79至0.88之间的F1分数检测二级结构位置。该架构应用于实验获得的冷冻电镜密度图时也具有良好的准确性。
