STRAND: Structure Refinement of RNA-Protein Complexes via Diffusion

RNA-protein interactions play crucial roles in cellular processes, from gene regulation to viral replication. While recent advances in structure prediction have revolutionized our ability to model macromolecular complexes, achieving accurate predictions of RNA-protein binding poses remains challenging. In this work, we present STRAND, a diffusion-based model for monomeric RNA-protein complex refinement that builds upon the success of DiffDock-PP in protein-protein docking. Unlike traditional docking, we develop STRAND as a modular extension to existing RNA-Protein complex prediction tools to improve their backbone predictions. We study the effect of different transformations by training models to learn either translation, rotation, torsion, or combinations of these during the diffusion process and initialize the backward process with a complex prediction at test time. Our experiments with AlphaFold 3 and ProRNA3D-single reveal that STRAND can improve the backbones of a large fraction of RNA-protein complex predictions.