Li Yichong, Pan Xiaoyong, Shen Hongbin, Yang Yang
Department of Computer Science and Engineering, Shanghai Jiao Tong University, 800 Dong Chuan Rd., Minhang District, Shanghai 200240, China.
Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, 800 Dong Chuan Rd., Minhang District, Shanghai 200240, China.
Brief Bioinform. 2025 Mar 4;26(2). doi: 10.1093/bib/bbaf106.
The rapid development of RNA vaccines and therapeutics puts forward intensive requirements on the sequence design of RNAs. RNA sequence design, or RNA inverse folding, aims to generate RNA sequences that can fold into specific target structures. To date, efficient and high-accuracy prediction models for secondary structures of RNAs have been developed. They provide a basis for computational RNA sequence design methods. Especially, reinforcement learning (RL) has emerged as a promising approach for RNA design due to its ability to learn from trial and error in generation tasks and work without ground truth data. However, existing RL methods are limited in considering complex hierarchical structures in RNA design environments. To address the above limitation, we propose DRAG, an RL method that builds design environments for target secondary structures with hierarchical division based on graph neural networks. Through extensive experiments on benchmark datasets, DRAG exhibits remarkable performance compared with current machine-learning approaches for RNA sequence design. This advantage is particularly evident in long and intricate tasks involving structures with significant depth.
RNA疫苗和治疗药物的快速发展对RNA的序列设计提出了严格要求。RNA序列设计，即RNA反向折叠，旨在生成能够折叠成特定目标结构的RNA序列。迄今为止，已经开发出了用于RNA二级结构的高效且高精度的预测模型。它们为计算RNA序列设计方法提供了基础。特别是，强化学习（RL）因其能够在生成任务中从试错中学习且无需真实数据即可工作，已成为一种有前途的RNA设计方法。然而，现有的RL方法在考虑RNA设计环境中的复杂层次结构方面存在局限性。为了解决上述局限性，我们提出了DRAG，这是一种基于图神经网络通过分层划分构建目标二级结构设计环境的RL方法。通过在基准数据集上进行的大量实验，与当前用于RNA序列设计的机器学习方法相比，DRAG表现出显著的性能。这种优势在涉及具有显著深度结构的长而复杂的任务中尤为明显。
