Encoding RNA Topology into Synthetic Alignments for 3D Structure Prediction

Modern 3D structure predictors rely on multiple sequence alignments (MSA) to expose evolutionary covariation, but for RNA those alignments are often missing, shallow, or unreliable. To overcome this limitation, we invert the co-evolutionary structure inference pipeline and encode a predicted 2D topology into synthetic alignment-form covariation, without requiring database search nor alignment steps. We introduce RNAformer, a transformer-based RNA secondary-structure predictor that supplies the topology, and a Synthetic Co-evolution Engine (SCE) that encodes it into synthetic homologous sequences (SHS) by co-mutating paired positions. Comprehensive experiments and systematic interventions across multiple structure predictors show that the MSA input acts as a topology channel that allows predictable steering of 3D structure prediction. Across single-chain RNAs and single-chain RNA-protein complexes, RNAformer-seeded SHS approach the performance of natural MSAs, with regime-dependent gains. Topology, not alignment depth, drives the response. SHS are interpretable, fast, and scalable, and represent a first step toward an alignment-free alternative to natural MSA.