The growing significance of RNA engineering in diverse biological applications has spurred interest in developing AI methods for structure-based RNA design. While diffusion models have excelled in protein design, adapting them for RNA presents new challenges due to RNA’s conformational flexibility and the computational cost of fine-tuning large structure prediction models. To this end, we propose RNAFlow, a flow matching model for protein-conditioned RNA sequence-structure co-design. Its denoising network integrates an RNA inverse folding model and a pre-trained RosettaFold2NA network for simultaneous generation of RNA sequences and structures. The integration of inverse folding in the structure denoising process allows us to simplify training by fixing the structure prediction network. We further enhance the inverse folding model by conditioning it on inferred conformational ensembles to model dynamic RNA conformations. Evaluation on protein-conditioned RNA structure and sequence generation tasks demonstrates RNAFlow’s advantage over existing RNA design methods.

Through SuperUROP, I hope to gain experience with methods development for computational biology. My past research projects have given me a strong grasp of machine learning fundamentals, especially in the context of molecules and proteins. Building on that, I’m excited about building methods for the challenging setting of nucleic acid structure prediction. Learning from my mentors, I hope to balance thorough experimentation with creativity.