Year: 2026
Type: preprint
Abstract: Designing RNA sequences that fold into predefined 3D structures is central to RNA engineering, yet it remains challenging due to the scarcity of experimentally determined structures and the complex sequence-structure relationships. Here, we present RiboDFM, a backbone-conditioned discrete flow-matching framework for RNA sequence design. RiboDFM progressively resolves fully masked sequences by integrating a geometric backbone encoder, an auxiliary base-pairing prediction head, and language model representations into a discrete flow sampler. To expand our training data, we supplemented experimental structures with a large-scale collection of trRosettaRNA2-predicted models. Across diverse benchmarks, RiboDFM achieves high sequence recovery and generates sequences with greater tertiary-structure self-consistency than existing methods. RiboDFM also performs strongly on aptamer-focused design tasks. Finally, beyond sequence generation, the nucleotide probabilities derived from RiboDFM’s flow trajectory serve as an intrinsic measure of sequence-structure compatibility. This enables reliable confidence estimation for designed sequences, as well as reference-free, zero-shot assessment of predicted structure models. Together, these results establish RiboDFM as an effective framework for structure-based RNA sequence design.
Institution: Shandong University
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W7223448713