{ "id": 570768, "title": "New editorial on RNA structure prediction with links to papers ", "url": "/competitions/stanford-rna-3d-folding/discussion/570768", "author_name": "KirkDCO", "post_date": "2025-03-30T16:14:50.888000", "votes": 9, "comment_count": 0, "views": 0, "content": "

Just saw this editorial this morning. The editorial doesn't offer much guidance, but the references are a good library of methods.

\n

Here are the actual references from the editorial.

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trRosettaRNA: automated prediction of RNA 3D structure with transformer network. Wang, W. et al. Nature Commun. 14, 7266 (2023).
\nAccurate RNA 3D structure prediction using a language model-based deep learning approach. Shen, T. et al. Nature Methods 21, 2287–2298 (2024).
\nDeep generative design of RNA aptamers using structural predictions. Wong, F. et al. Nature Comput. Sci. 4, 829–839 (2024).
\nDeep generative design of RNA family sequences. Sumi, S., Hamada, M. & Saito, H. Nature Methods 21, 435–443 (2024).
\nRNA language models predict mutations that improve RNA function. Shulgina, Y. et al. Nature Commun. 15, 10627 (2024).
\nATOM-1: A Foundation Model for RNA Structure and Function Built on Chemical Mapping Data. Boyd, N. et al. BioRxiv (2023)
\nRibonanza: deep learning of RNA structure through dual crowdsourcing. He, S. et al. BioRxiv (2024).

", "messages": [ { "id": 3163321, "postDate": "2025-03-30T16:14:50.887Z", "content": "

Just saw this editorial this morning. The editorial doesn't offer much guidance, but the references are a good library of methods.

\n

Here are the actual references from the editorial.

\n

trRosettaRNA: automated prediction of RNA 3D structure with transformer network. Wang, W. et al. Nature Commun. 14, 7266 (2023).
\nAccurate RNA 3D structure prediction using a language model-based deep learning approach. Shen, T. et al. Nature Methods 21, 2287–2298 (2024).
\nDeep generative design of RNA aptamers using structural predictions. Wong, F. et al. Nature Comput. Sci. 4, 829–839 (2024).
\nDeep generative design of RNA family sequences. Sumi, S., Hamada, M. & Saito, H. Nature Methods 21, 435–443 (2024).
\nRNA language models predict mutations that improve RNA function. Shulgina, Y. et al. Nature Commun. 15, 10627 (2024).
\nATOM-1: A Foundation Model for RNA Structure and Function Built on Chemical Mapping Data. Boyd, N. et al. BioRxiv (2023)
\nRibonanza: deep learning of RNA structure through dual crowdsourcing. He, S. et al. BioRxiv (2024).

", "rawMarkdown": "Just saw [this editorial](https://www.nature.com/articles/d41586-025-00920-8?utm_source=bluesky&utm_medium=organic_social&utm_content=null&utm_campaign=CONR_JRNLS_AWA1_GL_PCOM_SMEDA_NATUREPORTFOLIO) this morning. The editorial doesn't offer much guidance, but the references are a good library of methods.\n\nHere are the actual references from the editorial.\n\n[trRosettaRNA: automated prediction of RNA 3D structure with transformer network. Wang, W. et al. Nature Commun. 14, 7266 (2023).](https://doi.org/10.1038%2Fs41467-023-42528-4)\n[Accurate RNA 3D structure prediction using a language model-based deep learning approach. Shen, T. et al. Nature Methods 21, 2287–2298 (2024).](https://doi.org/10.1038%2Fs41592-024-02487-0)\n[Deep generative design of RNA aptamers using structural predictions. Wong, F. et al. Nature Comput. Sci. 4, 829–839 (2024).](https://doi.org/10.1038%2Fs43588-024-00720-6)\n[Deep generative design of RNA family sequences. Sumi, S., Hamada, M. & Saito, H. Nature Methods 21, 435–443 (2024).](https://doi.org/10.1038%2Fs41592-023-02148-8)\n[RNA language models predict mutations that improve RNA function. Shulgina, Y. et al. Nature Commun. 15, 10627 (2024).](https://doi.org/10.1038%2Fs41467-024-54812-y)\n[ATOM-1: A Foundation Model for RNA Structure and Function Built on Chemical Mapping Data. Boyd, N. et al. BioRxiv (2023)](https://doi.org/10.1101/2023.12.13.571579)\n[Ribonanza: deep learning of RNA structure through dual crowdsourcing. He, S. et al. BioRxiv (2024)](https://doi.org/10.1101/2024.02.24.581671).", "votes": 9 } ], "comments": [], "raw_markdown_by_id": { "3163321": "Just saw [this editorial](https://www.nature.com/articles/d41586-025-00920-8?utm_source=bluesky&utm_medium=organic_social&utm_content=null&utm_campaign=CONR_JRNLS_AWA1_GL_PCOM_SMEDA_NATUREPORTFOLIO) this morning. The editorial doesn't offer much guidance, but the references are a good library of methods.\n\nHere are the actual references from the editorial.\n\n[trRosettaRNA: automated prediction of RNA 3D structure with transformer network. Wang, W. et al. Nature Commun. 14, 7266 (2023).](https://doi.org/10.1038%2Fs41467-023-42528-4)\n[Accurate RNA 3D structure prediction using a language model-based deep learning approach. Shen, T. et al. Nature Methods 21, 2287–2298 (2024).](https://doi.org/10.1038%2Fs41592-024-02487-0)\n[Deep generative design of RNA aptamers using structural predictions. Wong, F. et al. Nature Comput. Sci. 4, 829–839 (2024).](https://doi.org/10.1038%2Fs43588-024-00720-6)\n[Deep generative design of RNA family sequences. Sumi, S., Hamada, M. & Saito, H. Nature Methods 21, 435–443 (2024).](https://doi.org/10.1038%2Fs41592-023-02148-8)\n[RNA language models predict mutations that improve RNA function. Shulgina, Y. et al. Nature Commun. 15, 10627 (2024).](https://doi.org/10.1038%2Fs41467-024-54812-y)\n[ATOM-1: A Foundation Model for RNA Structure and Function Built on Chemical Mapping Data. Boyd, N. et al. BioRxiv (2023)](https://doi.org/10.1101/2023.12.13.571579)\n[Ribonanza: deep learning of RNA structure through dual crowdsourcing. He, S. et al. BioRxiv (2024)](https://doi.org/10.1101/2024.02.24.581671)." } }