发表机构
Shanghai Jiao Tong University（上海交通大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出基于几何-潜在流匹配的RNA协同生成框架La-Ribo，构建含168561个RNA结构的质控语料库，其在可设计性等指标上优于基线，还可支持骨架条件下的逆折叠。
AI 中文摘要
RNA功能源于核苷酸序列与三维结构的耦合，这推动了二者的协同设计。在有限结构监督下，协调全局折叠与核苷酸级细节仍具挑战性。我们提出La-Ribo，一种基于几何-潜在流匹配的RNA序列-结构协同生成框架。La-Ribo保留稀疏的磷酸-糖-碱基骨架，在残基级潜在变量中编码核苷酸身份与局部构象；共享流网络联合生成二者，再经RNA专用解码器重构所有重原子。为扩充监督信号，我们构建了含168561个RNA结构的质控语料库，整合实验数据与3种折叠模型的预测结果，其中包含本研究生成的10631个多序列比对（MSA）支持的结构。在不同采样预算及2种重折叠模型下，La-Ribo在可设计性与协同设计性上均优于所评估的基线模型，且该先验无需额外训练即可支持骨架条件下的逆折叠任务。
英文摘要
RNA function arises from the coupling of nucleotide sequence and three-dimensional structure, motivating their joint design. Coordinating global folding with nucleotide-level detail remains challenging under limited structural supervision. We introduce La-Ribo, a generative framework for RNA sequence-structure co-design via geometry-latent flow matching. La-Ribo retains a sparse phosphate-sugar--base scaffold and encodes nucleotide identity and local conformation in residue-wise latents. A shared flow network generates both jointly, and an RNA-specific decoder then reconstructs all heavy atoms. To expand supervision, we construct a quality-controlled corpus of 168,561 RNA structures, integrating experimental data with predictions from three folding models, including 10,631 MSA-supported structures generated in this work. La-Ribo improves designability and codesignability over the evaluated baselines across sampling budgets and two refolding models, and the same prior supports scaffold-conditioned inverse folding without additional training.
