Pacesa, M.; Nickel, L.; Schmidt, J.; Pyatova, E.; Schellhaas, C.; Kissling, L.; Alcaraz-Serna, A.; Cho, Y.; Ghamary, K. H.; Vinue, L.; Yachnin, B. J.; Wollacott, A. M.; Buckley, S.; Georgeon, S.; Goverde, C. A.; Hatzopoulos, G. N.; Gonczy, P.; Muller, Y. D.; Schwank, G.; Ovchinnikov, S.; Correia, B. E.

Protein-protein interactions (PPIs) are at the core of all key biological processes. However, the complexity of the structural features that determine PPIs makes their design challenging. We present BindCraft, an open-source and automated pipeline for de novo protein binder design with experimental success rates of 10-100%. BindCraft leverages the trained deep learning weights of AlphaFold2 to generate nanomolar binders without the need for high-throughput screening or experimental optimization, even in the absence of known binding sites. We successfully designed binders against a diverse set of challenging targets, including cell-surface receptors, common allergens, de novo designed proteins, and multi-domain nucleases, such as CRISPR-Cas9. We showcase their functional and therapeutic potential by demonstrating that designed binders can reduce IgE binding to birch allergen in patient-derived samples. This work represents a significant advancement towards a \"one design-one binder\" approach in computational design, with immense potential in therapeutics, diagnostics, and biotechnology.