RayMorph: Learning Protein Redesign from Structural Pairs

Redesigning a protein to change its geometry while preserving its function is a central challenge in protein engineering. For instance, when a biosensor needs to be inserted into an internal loop of a target protein, its N- and C-termini need to be brought close together. In such cases, the desired change can be easier to illustrate with examples than explicit residue-level guidance. To address this challenge, we introduce RayMorph, a framework that learns protein redesign from pairs of re- lated structures exhibiting a desired property change. The paired structural data specifies the transformation by example, allowing it to be learned and applied to new structures. RayMorph jointly modifies geometry, sequence, and length while preserving the source fold. We establish correspondence between unequal-length structures through geometry-guided dynamic programming and construct bridge trajectories that combine residue additions and deletions with changes in coordi- nates and amino-acid identities. We introduce a curated AlphaFoldDB dataset of structurally matched protein pairs with varying N–C distances and terminal ac- cessibility. Among 488 proteins drawn from 50 Foldseek clusters excluded from training, 25.4% yield at least one candidate satisfying designability, source-fold similarity, and terminal-remodeling criteria after inverse folding and refolding.