FBindGen: Function-Conditioned Protein Binder Sequence Generation with Target-Aware Pair Modeling

Existing protein binder design methods primarily focus on structural compatibility with a specified target, while direct control over binder function remains less explored. We introduce FBindGen, a diffusion protein language model that generates binder sequences from a target protein and functional annotations at the residue level. FBindGen combines functional conditioning with target-aware pair modeling by allowing sequence and pair representations to update each other throughout denoising. To evaluate how well generated binders follow the specified function, we introduce EnzBind, a benchmark of protein complexes involving enzymes with functional annotations and substrate information. On EnzBind and DB5.5, FBindGen shows improved agreement between generated sequences and the specified functions while maintaining competitive predicted interface confidence. On EnzBind, it also achieves the highest predicted catalytic efficiency among the evaluated methods. Evaluation on Heterodimer99 further shows that the learned pair representations capture interchain contact information. Together, these results show that FBindGen provides functional control over binder sequence generation while preserving compatibility with the target.