Computational RNA design has broad applications across synthetic biology and therapeutic development. Fundamental to the diverse biological functions of RNA is its conformational flexibility, enabling single sequences to adopt a variety of distinct 3D states. Currently, computational biomolecule design tasks are often posed as inverse problems, where sequences are designed based on adopting a single desired structural conformation. In this work, we propose gRNAde, a geometric RNA design pipeline that operates on sets of 3D RNA backbone structures to explicitly account for and reflect RNA conformational diversity in its designs. We demonstrate the utility of gRNAde for improving native sequence recovery over single-state approaches on a new large-scale 3D RNA design dataset, especially for multi-state and structurally diverse RNAs. Our code is available at https://github.com/chaitjo/geometric-rna-design

翻译：计算RNA设计在合成生物学和治疗开发中具有广泛的应用。RNA多样生物功能的基础在于其构象灵活性，使单一序列能够呈现多种不同的三维状态。目前，计算生物分子设计任务通常被表述为逆问题，即基于采用单一期望结构构象来设计序列。在这项工作中，我们提出了gRNAde，一种几何RNA设计流程，它作用于三维RNA骨架结构集合，以明确考虑并在其设计中反映RNA构象多样性。我们展示了gRNAde在改善现有单状态方法上的天然序列恢复效用，尤其是在一个新的大规模三维RNA设计数据集中，对于多状态和结构多样的RNA效果显著。我们的代码可在https://github.com/chaitjo/geometric-rna-design获取。