TANGO: RNA Topology and Geometry Co-Design

Designing RNA sequences that satisfy target tertiary-structure objectives is an important problem in RNA engineering, with broad applications in synthetic biology and therapeutics. Existing $\mathrm{3D}$ RNA design methods have made substantial progress, but they largely treat the target fold as a geometry-only objective. This is incomplete for RNA, whose functional folds are specified not only by tertiary geometry but also by secondary base-pairing topology. We therefore formulate RNA inverse design as a topology-and-geometry co-design problem and propose $\texttt{TANGO}$, a cooperative multi-agent reinforcement learning framework for this task. $\texttt{TANGO}$ adopts a staged curriculum: it first learns a topology-aware policy from secondary-structure targets, then transfers this policy to tertiary-structure targets to train a general co-design model, and finally applies frontier-guided fine-tuning to refine target-specific trade-offs among secondary-structure fidelity, tertiary-structure fidelity, and sequence diversity. Experiments show that $\texttt{TANGO}$ generates diverse and novel designs, improving tertiary- and secondary-structure performance by $18.3\\%$ and $52.7\\%$, respectively, over the strongest baseline.