NanoIF: Interaction-Aware Nanobody Inverse Folding

Nanobody inverse folding is limited by scarce VHH structures and the underuse of supervision provided by VHH–antigen complexes. We introduce NanoIF, an interaction-aware model for nanobody inverse folding that learns from VHH–antigen complexes during training and requires only the VHH backbone at inference. It first adapts to the VHH sequence–structure distribution and then incorporates antigen information through a dedicated antigen encoder, layer-wise cross-attention, and complex-derived geometric and contrastive supervision. On a SAbDab-nano-derived benchmark, NanoIF reaches state-of-the-art amino-acid recovery, improving paratope recovery by 3.2 points over the strongest baseline, with larger gains on CDR1 and CDR2 (+9.7 and +8.6 points). Controlled training shows that matched VHH-only continued training does not reproduce these gains, while the learned improvement is retained at antigen-free inference. These results establish complex-guided training as an effective strategy for fixed-backbone nanobody sequence design.