HiGenIF: Hierarchical Structural Context Modeling for Protein Inverse Folding

Protein inverse folding aims to design amino-acid sequences compatible with a target backbone. Protein function emerges from coordinated structural environments spanning local, regional, and protein-wide scales. However, regional relationships often remain implicit in residue-level representations, leaving the connection between structural organization and position-specific biochemical preferences insufficiently explicit. We propose HiGenIF, which explicitly organizes protein-wide, regional, and residue-level structural context to predict position-specific amino-acid preferences. The resulting complete sequence draft and continuous structural states jointly condition ESM2-650M, which incorporates structural information at an intermediate layer and predicts residual corrections to the initial logits. On the CATH 4.2 topology split, HiGenIF achieves 59.37% amino-acid recovery and 3.40 perplexity across 1,120 test chains, establishing the highest recovery within our focused comparison of inverse-folding methods that incorporate pretrained sequence knowledge. Controlled experiments further demonstrate the effectiveness of explicit structural-region modeling and pretrained sequence refinement.