RiboBridge designs RNA shapes to bind proteins without preset sites
RiboBridge: protein-conditioned de novo RNA binder structure design
Machine Learning
Summary
Designing RNA molecules that precisely stick to proteins is tricky because it involves creating both the shape of the RNA and how it fits onto the protein. The authors introduced RiboBridge, a method that first designs the RNA shape separately and then figures out how it attaches to proteins, instead of doing both at once. This approach helps find better-fitting RNA-protein pairs, even when the exact binding spots on the protein aren't known ahead of time. Tests show RiboBridge outperforms previous methods in predicting accurate RNA-protein complexes.
What this means in practice
- •For drug developers: Create RNA molecules that target specific proteins without predefined binding sites to design new RNA-based therapeutics.$Commercial implications: Enables design of RNA drugs targeting proteins with unknown binding sites, valuable for pharmaceutical companies developing novel treatments.
- •For biotech engineers: Design protein-binding RNA structures to engineer biomolecular systems for synthetic biology applications.
Authors
Zhou Zhang, Hanqun Cao, Minghao Sun, Odin Zhang, Yang Zhang, Pheng Ann Heng, Wujun Li, Tianfan Fu
Abstract
Designing RNA molecules that bind to target protein structures requires generating both an RNA conformation and its bound complex geometry, without assuming a predefined binding site. Existing approaches generally model these coupled processes within a unified representation, entangling RNA conformation generation with protein-relative placement. In this work, we introduce RiboBridge, a structure-first framework that separates RNA conformational generation from protein--RNA assembly through cross-frame interaction geometry. RiboBridge first proposes multiple candidate RNA-binding interfaces on the protein surface, generates canonical RNA conformations within a pretrained RNA structural latent space (RiboSphere), and then predicts protein--RNA interaction constraints to bridge the independently defined RNA and protein coordinate frames. The resulting interaction geometry is converted into a geometrically consistent protein-relative pose through rigid placement. We evaluate RiboBridge on a temporally split, sequence-filtered, and redundancy-controlled benchmark of 124 protein--RNA complexes. Under post-hoc interface-correspondence evaluation, where native information is used only after generation to select among already generated predicted-pocket candidates, RiboBridge achieves the highest fNAT and DockQ and the lowest interface and ligand RMSDs among compared methods. Component ablations and stage-wise diagnostics further characterize the contributions of RNA conformational generation, interaction prediction, and rigid placement, identifying the main sources of complex-recovery error.