RNA sequence and structure designed together with new generative model
La-Ribo: RNA Co-Design via Geometry-Latent Flow Matching
Artificial Intelligence
Summary
RNA molecules work by their sequence and shape cooperating closely, but designing both at once is tough. The authors developed La-Ribo, a tool that generates RNA sequences and their 3D structures simultaneously using a special network that understands geometry. They trained it on a large dataset combining experimental and predicted RNA structures, improving the quality of designed RNA molecules compared to older methods. This tool also works for designing sequences based on a fixed structural scaffold, making RNA design more flexible.
What this means in practice
- •For synthetic biologists: Design RNA molecules with both sequence and structure optimized simultaneously, improving lab experiments and novel RNA engineering projects.
- •For biotech software developers: Create tools that generate RNA sequences conditioned on structural scaffolds without retraining, speeding up RNA therapeutics design workflows.
Authors
Runze Ma, Will Hua, Shuangjia Zheng
Abstract
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.