利用二级结构信息实现核酸结构精确预测的OFoldNA
Leveraging secondary-structure information for accurate nucleic acid structure prediction with OFoldNA
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中文总结 AI 辅助
本文提出全原子扩散模型OFoldNA，通过整合核酸二级结构信息提升核酸及蛋白质-核酸复合物结构预测精度，并在多个基准上取得领先性能。
中文摘要 AI 辅助
近年来，生物分子结构预测的进展使得对日益复杂的分子系统进行精确建模成为可能。然而，由于构象灵活性和高质量三维结构数据的有限可用性，核酸结构预测仍然具有挑战性。二级结构（SS）提供了一种更容易获得的结构信息层，它捕获碱基配对关系和折叠拓扑。在此，我们提出了OFoldNA，一个全原子扩散模型，将SS信息整合到核酸折叠和蛋白质-核酸共折叠中。在没有外部SS信息的情况下，OFoldNA在FoldBench上对核酸单体折叠和蛋白质-核酸共折叠均取得了领先性能，在DNA单体和涉及较长核酸链的蛋白质-DNA界面上表现尤为突出。当提供准确的碱基配对信息时，OFoldNA-SS2TS进一步提高了折叠和共折叠的准确性，而部分SS信息也带来了一致的增益。相同的辅助分支也可用于RNA SS预测，即OFoldNA-SS，在CHANRG上取得了最佳分布外性能。总之，这些结果表明，核酸SS等中间结构信息可用于改进全原子三维建模，为将互补的结构模态纳入分子结构预测和设计提供了通用方向。
英文摘要
Recent advances in biomolecular structure prediction have enabled accurate modelling of increasingly complex molecular systems. However, nucleic acid structure prediction remains challenging because of conformational flexibility and the limited availability of high-quality 3D structural data. Secondary structure (SS) provides a more readily available layer of structural information that captures base-pairing relationships and folding topology. Here we present OFoldNA, an all-atom diffusion model that incorporates SS information into nucleic acid folding and protein--nucleic acid co-folding. Without external SS information, OFoldNA achieved leading performance on FoldBench for both nucleic acid monomer folding and protein--nucleic acid co-folding, with particularly strong performance on DNA monomers and protein--DNA interfaces involving longer nucleic acid chains. When accurate base-pairing information was provided, OFoldNA-SS2TS further improved both folding and co-folding accuracy, while partial SS information also yielded consistent gains. The same auxiliary branch can also be used for RNA SS prediction as OFoldNA-SS, which achieved the best out-of-distribution performance on CHANRG. Together, these results show that intermediate structural information such as nucleic acid SS can be leveraged to improve all-atom 3D modelling, providing a general direction for incorporating complementary structural modalities into molecular structure prediction and design.
