Rustamov, K. R.; Baev, A. Y.

De novo protein binder design has been revolutionized by deep learning methods, yet controlling binder topology remains a challenge. We introduce a fold-conditioned AlphaFold2-Multimer hallucination framework - FoldCraft - guided by a contact map similarity loss, enabling precise generation of binders with user-defined structural folds. This single loss function enforces fold-specific geometry while implicitly optimizing AlphaFold confidence metrics. We demonstrate the method\'s versatility by designing binders with six distinct topologies. Compared to RFdiffusion, FoldCraft yields higher structural and binding confidence. Applied to VHH nanobody design against four therapeutically relevant targets, our method outperforms RFAntibody in AlphaFold3-based evaluations. FoldCraft offers a general, efficient strategy for structure-guided binder design, expanding the accessible fold space for protein engineering and enabling robust nanobody generation with improved success rates.