Department of Computer Science, Purdue University, West Lafayette, IN, USA.
Department of Biological Sciences, Purdue University, West Lafayette, IN, USA.
Nat Methods. 2019 Sep;16(9):911-917. doi: 10.1038/s41592-019-0500-1. Epub 2019 Jul 29.
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.
虽然冷冻电子显微镜（cryo-EM）现在通常可以报告接近原子分辨率的结构，但许多密度图是在中等分辨率下确定的，因此从这些图中提取结构信息仍然是一个挑战。我们报告了一种计算方法，Emap2sec，它可以识别分辨率在 5 到 10Å 之间的 EM 图谱中蛋白质的二级结构（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 图谱中的每个网格点分配二级结构。我们在分辨率为 6.0 和 10.0Å 的 34 个结构的 EM 图谱以及分辨率在 5.0 和 9.5Å 之间的 43 个实验确定的图谱上测试了 Emap2sec。Emap2sec 能够清楚地识别许多经过测试的图谱中的二级结构，并且性能明显优于现有方法。
