2024Li EMRNA

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Revision as of 06:26, 5 August 2024 by WikiSysop (talk | contribs) (Created page with "== Citation == Li, Tao / He, Jiahua / Cao, Hong / Zhang, Yi / Chen, Ji / Xiao, Yi / Huang, Sheng-You. All-atom RNA structure determination from cryo-EM maps. 2024. Nature Biotechnology, p. 1-9 == Abstract == 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 cryo...")
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Citation

Li, Tao / He, Jiahua / Cao, Hong / Zhang, Yi / Chen, Ji / Xiao, Yi / Huang, Sheng-You. All-atom RNA structure determination from cryo-EM maps. 2024. Nature Biotechnology, p. 1-9

Abstract

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.

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https://www.nature.com/articles/s41587-024-02149-8

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