REFRACT: Generating RNA Conformational Ensembles with Context-Aware Flows

Generating RNA conformational ensembles that reflect their molecular environment remains challenging. We introduce $\text{REFRACT}$, an $\mathrm{SE}(3)$-equivariant framework that couples flow-based reconstruction with context-aware transport. During training, the model learns structural regularities around observed conformations by reconstructing individual experimental structures from corrupted inputs, without requiring paired alternative conformations or molecular dynamics (MD) trajectories. CrossIPA incorporates fixed protein geometry through geometric cross-attention, conditioning RNA updates on the evolving RNA–protein spatial relationships. At inference, context-aware transport uses these learned updates to guide independently corrupted RNA conformations toward ensembles compatible with the supplied protein context. On protein-context ensemble recovery and conformational transition across binding conditions, $\text{REFRACT}$ achieves average matched TM-scores of $0.669$ and $0.648$, respectively, exceeding the evaluated baselines. It also more closely matches nucleotide-level fluctuations and conformational distributions in MD reference ensembles. An ablation on the protein-bound benchmark shows that supplying protein context improves conformational recovery and reduces the proportion of generated structures with steric clashes.