We developed a deep learning-based method, EMRNA, to automatically model RNA structures from cryo-electron microscopy maps. Evaluation of EMRNA on diverse test sets of RNA maps shows that it builds RNA models with high accuracy and efficiency.
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References
Zhang, J. et al. Advances and opportunities in RNA structure experimental determination and computational modeling. Nat. Methods 19, 1193–1207 (2022). A review that presents advances in the experimental determination and computational modeling of RNA structures.
Kappel, K. et al. Accelerated cryo-EM-guided determination of three-dimensional RNA-only structures. Nat. Methods 17, 699–707 (2020). This paper reports a method for modeling full-length RNA structures from RNA-only cryo-EM maps.
Terwilliger, T. C. et al. A fully automatic method yielding initial models from high-resolution cryo-electron microscopy maps. Nat. Methods. 15, 905–908 (2018). This paper reports a method for obtaining initial models of protein and nucleic acid structures from cryo-EM maps.
Wang, X. et al. CryoREAD: de novo structure modeling for nucleic acids in cryo-EM maps using deep learning. Nat. Methods 20, 1739–1747 (2023). This paper reports a method for modeling nucleic acid structure fragments from cryo-EM maps.
Luo, B. et al. Cryo-EM reveals dynamics of Tetrahymena group I intron self-splicing. Nat. Catal. 6, 298–309 (2023). This paper reports the structural dynamics of Tetrahymena group I intron self-splicing.
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This is a summary of: Li, T. et al. All-atom RNA structure determination from cryo-EM maps. Nat. Biotechnol. https://doi.org/10.1038/s41587-024-02149-8 (2024)
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A deep learning-based method for modeling of RNA structures from cryo-EM maps. Nat Biotechnol (2024). https://doi.org/10.1038/s41587-024-02162-x
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DOI: https://doi.org/10.1038/s41587-024-02162-x
