DualMPNN: Harnessing Structural Alignments for High-Recovery Inverse Protein Folding

Inverse protein folding addresses the challenge of designing amino acid sequences that fold into a predetermined tertiary structure, bridging geometric and evolutionary constraints to advance protein engineering. Inspired by the pivotal role of multiple sequence alignments (MSAs) in structure prediction models like Al-phaFold, we hypothesize that structural alignments can provide an informative prior for inverse folding. In this study, we introduce DualMPNN, a dual-stream message passing neural network that leverages structurally homologous templates to guide amino acid sequence design of predefined query structures. DualMPNN processes the query and template proteins via two interactive branches, coupled through alignment-aware cross-stream attention mechanisms that enable exchange of geometric and co-evolutionary signals. Comprehensive evaluations across on CATH 4.2, TS50 and T500 benchmarks demonstrate DualMPNN achieves state-ofthe-art recovery rates of 65.51%, 70.99%, and 70.37%, significantly outperforming base model ProteinMPNN by 15.64%, 16.56%, 12.29%, respectively. Further template quality analysis and structural foldability assessment underscore the value of structural alignment priors for protein design.