Department of Computer Science, Purdue University, West Lafayette, IN 47907, United States.
Department of Biological Sciences, Purdue University, West Lafayette, IN 47907, United States.
Bioinformatics. 2023 Aug 1;39(8). doi: 10.1093/bioinformatics/btad494.
The tertiary structures of an increasing number of biological macromolecules have been determined using cryo-electron microscopy (cryo-EM). However, there are still many cases where the resolution is not high enough to model the molecular structures with standard computational tools. If the resolution obtained is near the empirical borderline (3-4.5 Å), improvement in the map quality facilitates structure modeling.
We report EM-GAN, a novel approach that modifies an input cryo-EM map to assist protein structure modeling. The method uses a 3D generative adversarial network (GAN) that has been trained on high- and low-resolution density maps to learn the density patterns, and modifies the input map to enhance its suitability for modeling. The method was tested extensively on a dataset of 65 EM maps in the resolution range of 3-6 Å and showed substantial improvements in structure modeling using popular protein structure modeling tools.
https://github.com/kiharalab/EM-GAN, Google Colab: https://tinyurl.com/3ccxpttx.
越来越多的生物大分子的三级结构已通过冷冻电子显微镜（cryo-EM）确定。然而，仍有许多分辨率不够高的情况，无法使用标准计算工具对分子结构进行建模。如果获得的分辨率接近经验临界值（3-4.5Å），则提高图谱质量有助于进行结构建模。
我们报告了 EM-GAN，这是一种改进输入冷冻电镜图谱以辅助蛋白质结构建模的新方法。该方法使用经过高分辨率和低分辨率密度图谱训练的三维生成对抗网络（GAN）来学习密度模式，并修改输入图谱以增强其建模适用性。该方法在 3-6Å 分辨率范围内的 65 个 EM 图谱数据集上进行了广泛测试，使用流行的蛋白质结构建模工具显示出对结构建模的实质性改进。
https://github.com/kiharalab/EM-GAN，Google Colab：https://tinyurl.com/3ccxpttx。
