Computational RNA design tasks are often posed as inverse problems, where sequences are designed based on adopting a single desired secondary structure without considering 3D geometry and conformational diversity. We introduce gRNAde, a geometric RNA design pipeline operating on 3D RNA backbones to design sequences that explicitly account for structure and dynamics. Under the hood, gRNAde is a multi-state Graph Neural Network that generates candidate RNA sequences conditioned on one or more 3D backbone structures where the identities of the bases are unknown. On a single-state fixed backbone re-design benchmark of 14 RNA structures from the PDB identified by Das et al. [2010], gRNAde obtains higher native sequence recovery rates (56% on average) compared to Rosetta (45% on average), taking under a second to produce designs compared to the reported hours for Rosetta. We further demonstrate the utility of gRNAde on a new benchmark of multi-state design for structurally flexible RNAs, as well as zero-shot ranking of mutational fitness landscapes in a retrospective analysis of a recent RNA polymerase ribozyme structure. Open source code: https://github.com/chaitjo/geometric-rna-design

翻译：计算RNA设计任务通常被表述为逆问题，即基于单个目标二级结构设计序列，而无需考虑3D几何构象和构象多样性。我们提出gRNAde，这是一个基于3D RNA骨架的几何RNA设计流程，能够显式考虑结构和动力学来设计序列。在底层，gRNAde是一个多态图神经网络，可在碱基身份未知的一个或多个3D骨架结构条件下生成候选RNA序列。在Das等人[2010]从PDB中鉴定的14个RNA结构的单态固定骨架重设计基准测试中，gRNAde的天然序列恢复率（平均56%）高于Rosetta（平均45%），且仅需不到一秒即可完成设计，而Rosetta则需要数小时。我们进一步在结构柔性RNA的多态设计新基准测试中，以及通过近期RNA聚合酶核酶结构的回顾性分析中的突变适应度景观零样本排序中，验证了gRNAde的实用性。开源代码：https://github.com/chaitjo/geometric-rna-design