CryoREAD: de novo structure modeling for nucleic acids in cryo-EM maps using deep learning.
CryoREAD: de novo structure modeling for nucleic acids in cryo-EM maps using deep learning.
复制标题
CryoREAD：使用深度学习对冷冻电镜图谱中的核酸进行从头结构建模。
DOI：
10.1038/s41592-023-02032-5
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发表时间：
2023
期刊：
影响因子：
48
通讯作者：
Kihara,Daisuke
中科院分区：
文献类型：
--
作者：
Wang,Xiao;Terashi,Genki;Kihara,Daisuke
DNA和RNA在各种细胞过程中发挥着重要作用，它们的三维结构为理解其功能的分子机制提供了关键信息。尽管越来越多的核酸结构及其与蛋白质的复合物通过低温电子显微镜（cryo-EM）确定，但是DNA和RNA的结构建模仍然具有挑战性，特别是当在比原子水平更粗糙的分辨率下确定地图时。此外，用于核酸结构建模的计算方法相对缺乏。在这里，我们介绍了CryoREAD，一种使用深度学习的全自动从头DNA/RNA原子结构建模方法。CryoREAD使用深度学习识别cryo-EM图中的磷酸盐、糖和碱基位置，这些位置被跟踪并建模为三维结构。当在2.0至5.0 μ m分辨率下确定的cryo-EM图上进行测试时，CryoREAD建立的模型比现有方法准确得多。我们还将该方法应用于严重急性呼吸综合征冠状病毒2（SARS-CoV-2）中生物分子复合物的冷冻电镜图。
DNA and RNA play fundamental roles in various cellular processes, where their three-dimensional structures provide information critical to understanding the molecular mechanisms of their functions. Although an increasing number of nucleic acid structures and their complexes with proteins are determined by cryogenic electron microscopy (cryo-EM), structure modeling for DNA and RNA remains challenging particularly when the map is determined at a resolution coarser than atomic level. Moreover, computational methods for nucleic acid structure modeling are relatively scarce. Here, we present CryoREAD, a fully automated de novo DNA/RNA atomic structure modeling method using deep learning. CryoREAD identifies phosphate, sugar and base positions in a cryo-EM map using deep learning, which are traced and modeled into a three-dimensional structure. When tested on cryo-EM maps determined at 2.0 to 5.0 Å resolution, CryoREAD built substantially more accurate models than existing methods. We also applied the method to cryo-EM maps of biomolecular complexes in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).