Department of Computer Science, Purdue University, West Lafayette, IN, USA.
Department of Biological Sciences, Purdue University, West Lafayette, IN, USA.
Nat Commun. 2021 Apr 16;12(1):2302. doi: 10.1038/s41467-021-22577-3.
An increasing number of density maps of macromolecular structures, including proteins and DNA/RNA complexes, have been determined by cryo-electron microscopy (cryo-EM). Although lately maps at a near-atomic resolution are routinely reported, there are still substantial fractions of maps determined at intermediate or low resolutions, where extracting structure information is not trivial. Here, we report a new computational method, Emap2sec+, which identifies DNA or RNA as well as the secondary structures of proteins in cryo-EM maps of 5 to 10 Å resolution. Emap2sec+ employs the deep Residual convolutional neural network. Emap2sec+ assigns structural labels with associated probabilities at each voxel in a cryo-EM map, which will help structure modeling in an EM map. Emap2sec+ showed stable and high assignment accuracy for nucleotides in low resolution maps and improved performance for protein secondary structure assignments than its earlier version when tested on simulated and experimental maps.
越来越多的大分子结构（包括蛋白质和 DNA/RNA 复合物）的密度图已经通过冷冻电子显微镜（cryo-EM）确定。尽管最近经常报道接近原子分辨率的图谱，但仍有相当一部分图谱是在中等或低分辨率下确定的，在这些分辨率下提取结构信息并不简单。在这里，我们报告了一种新的计算方法 Emap2sec+，它可以识别 DNA 或 RNA 以及蛋白质在 5 至 10Å 分辨率的 cryo-EM 图谱中的二级结构。Emap2sec+ 使用深度残差卷积神经网络。Emap2sec+ 在 cryo-EM 图谱的每个体素上分配结构标签及其相关概率，这将有助于在 EM 图谱中进行结构建模。Emap2sec+ 在低分辨率图谱中对核苷酸的稳定和高分配准确率以及在模拟和实验图谱上对蛋白质二级结构分配的改进性能均优于其早期版本。
