Learning from 3D biological macromolecules with artificial intelligence technologies has been an emerging area. Computational protein design, known as the inverse of protein structure prediction, aims to generate protein sequences that will fold into the defined structure. Analogous to protein design, RNA design is also an important topic in synthetic biology, which aims to generate RNA sequences by given structures. However, existing RNA design methods mainly focus on the secondary structure, ignoring the informative tertiary structure, which is commonly used in protein design. To explore the complex coupling between RNA sequence and 3D structure, we introduce an RNA tertiary structure modeling method to efficiently capture useful information from the 3D structure of RNA. For a fair comparison, we collect abundant RNA data and split the data according to tertiary structures. With the standard dataset, we conduct a benchmark by employing structure-based protein design approaches with our RNA tertiary structure modeling method. We believe our work will stimulate the future development of tertiary structure-based RNA design and bridge the gap between the RNA 3D structures and sequences.

翻译：利用人工智能技术从三维生物大分子中学习已成为新兴领域。计算蛋白质设计（即蛋白质结构预测的逆过程）旨在生成能够折叠成特定结构的蛋白质序列。与蛋白质设计类似，RNA设计也是合成生物学中的重要课题，其目标是依据给定结构生成RNA序列。然而，现有RNA设计方法主要聚焦于二级结构，忽略了在蛋白质设计中广泛使用的信息丰富的三级结构。为探索RNA序列与三维结构间的复杂耦合关系，我们引入了一种RNA三级结构建模方法，用以高效捕获RNA三维结构中的有用信息。为确保公平比较，我们收集了大量RNA数据，并依据三级结构进行数据划分。基于标准数据集，我们采用基于结构的蛋白质设计方法，结合我们的RNA三级结构建模技术开展了基准测试。我们相信，这项研究将推动基于三级结构的RNA设计的未来发展，弥合RNA三维结构与序列之间的鸿沟。