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Has AlphaFold3 achieved success for RNA?
Acta Crystallographica Section D ( IF 2.6 ) Pub Date : 2025-02-01 , DOI: 10.1107/s2059798325000592
Clément Bernard,Guillaume Postic,Sahar Ghannay,Fariza Tahi

Predicting the 3D structure of RNA is a significant challenge despite ongoing advancements in the field. Although AlphaFold has successfully addressed this problem for proteins, RNA structure prediction raises difficulties due to the fundamental differences between proteins and RNA, which hinder its direct adaptation. The latest release of AlphaFold, AlphaFold3, has broadened its scope to include multiple different molecules such as DNA, ligands and RNA. While the AlphaFold3 article discussed the results for the last CASP-RNA data set, the scope of its performance and the limitations for RNA are unclear. In this article, we provide a comprehensive analysis of the performance of AlphaFold3 in the prediction of 3D structures of RNA. Through an extensive benchmark over five different test sets, we discuss the performance and limitations of AlphaFold3. We also compare its performance with ten existing state-of-the-art ab initio, template-based and deep-learning approaches. Our results are freely available on the EvryRNA platform at https://evryrna.ibisc.univ-evry.fr/evryrna/alphafold3/.

中文翻译:


AlphaFold3 在 RNA 方面取得了成功吗?



尽管该领域不断进步,但预测 RNA 的 3D 结构是一项重大挑战。尽管 AlphaFold 已经成功地解决了蛋白质的这个问题,但由于蛋白质和 RNA 之间的根本差异阻碍了其直接适应,因此 RNA 结构预测增加了难度。最新版本的 AlphaFold AlphaFold3 扩大了其范围,包括 DNA、配体和 RNA 等多种不同分子。虽然 AlphaFold3 文章讨论了最后一个 CASP-RNA 数据集的结果,但其性能范围和 RNA 的局限性尚不清楚。在本文中,我们对 AlphaFold3 在预测 RNA 的 3D 结构中的性能进行了全面分析。通过对五个不同测试集的广泛基准测试,我们讨论了 AlphaFold3 的性能和局限性。我们还将其性能与现有的 10 种最先进的 ab initio、基于模板和深度学习的方法进行了比较。我们的结果可在 https://evryrna.ibisc.univ-evry.fr/evryrna/alphafold3/ 的 EvryRNA 平台上免费获得。
更新日期:2025-01-29
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