Many methods exist for determining protein structures from cryogenic electron microscopy maps, but this remains challenging for RNA structures. Here we developed EMRNA, a method for accurate, automated determination of full-length all-atom RNA structures from cryogenic electron microscopy maps. EMRNA integrates deep learning-based detection of nucleotides, three-dimensional backbone tracing and scoring with consideration of sequence and secondary structure information, and full-atom construction of the RNA structure. We validated EMRNA on 140 diverse RNA maps ranging from 37 to 423 nt at 2.0–6.0 Å resolutions, and compared EMRNA with auto-DRRAFTER, phenix.map_to_model and CryoREAD on a set of 71 cases. EMRNA achieves a median accuracy of 2.36 Å root mean square deviation and 0.86 TM-score for full-length RNA structures, compared with 6.66 Å and 0.58 for auto-DRRAFTER. EMRNA also obtains a high residue coverage and sequence match of 93.30% and 95.30% in the built models, compared with 58.20% and 42.20% for phenix.map_to_model and 56.45% and 52.3% for CryoREAD. EMRNA is fast and can build an RNA structure of 100 nt within 3 min.

存在许多从低温电子显微镜图确定蛋白质结构的方法，但这对于 RNA 结构仍然具有挑战性。在这里，我们开发了 ERNA，这是一种从低温电子显微镜图谱中准确、自动测定全长全原子 RNA 结构的方法。ERNA集成了基于深度学习的核苷酸检测、考虑序列和二级结构信息的三维骨干追踪和评分以及RNA结构的全原子构建。我们在分辨率为 2.0-6.0 Å 的 140 个不同 RNA 图谱（范围从 37 到 423 nt）上验证了 EMRNA，并在 71 个病例上将 EMRNA 与 auto-DRRAFTER、phenix.map_to_model 和 CryoREAD 进行了比较。EMRNA 对全长 RNA 结构的平均准确度为 2.36 Å 均方根偏差和 0.86 TM 得分，而 auto-DRRAFTER 的平均准确度为 6.66 Å 和 0.58。EMRNA 在构建的模型中还获得了 93.30% 和 95.30% 的高残基覆盖率和序列匹配度，而 phenix.map_to_model 为 58.20% 和 42.20%，CryoREAD 为 56.45% 和 52.3%。ERNA速度快，可在3分钟内构建100 nt的RNA结构。