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Cited by 236

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One-shot design of functional protein binders with BindCraft

Open access
Pacesa, Martin, Nickel, Lennart, Schellhaas, Christian, Schmidt, Joseph, Pyatova, Ekaterina, Kissling, Lucas, Barendse, Patrick, Choudhury, Jagrity, Kapoor, Srajan, Alcaraz-Serna, Ana, Cho, Yehlin, Ghamary, Kourosh H., Vinué, Laura, Yachnin, Brahm J., Wollacott, Andrew M., Buckley, Stephen, Westphal, Adrie H., Lindhoud, Simon, Georgeon, Sandrine, Goverde, Casper A., Hatzopoulos, Georgios N., Gönczy, Pierre, Muller, Yannick D., Schwank, Gerald, Swarts, Daan C., Vecchio, Alex J., Schneider, Bernard L., Ovchinnikov, Sergey, Correia, Bruno E.·2025·Nature
DOI 10.1038/s41586-025-09429-6
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Abstract

Protein–protein interactions are at the core of all key biological processes. However, the complexity of the structural features that determine protein–protein interactions makes their design challenging. Here 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 weights of AlphaFold2 (ref. 1) to generate binders with nanomolar affinity 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 the functional and therapeutic potential of designed binders by reducing IgE binding to birch allergen in patient-derived samples, modulating Cas9 gene editing activity and reducing the cytotoxicity of a foodborne bacterial enterotoxin. Last, we use cell-surface-receptor-specific binders to redirect adeno-associated virus capsids for targeted gene delivery. This work represents a significant advancement towards a ‘one design-one binder’ approach in computational design, with immense potential in therapeutics, diagnostics and biotechnology. BindCraft, an open-source, automated pipeline for de novo protein binder design with experimental success rates of 10–100%, leverages AlphaFold2 weights to generate binders with nanomolar affinity without the need for high-throughput screening.

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  1. Cao, L. et al. Design of protein-binding proteins from the target structure alone. Nature 605, 551–560 (2022). · DOI · Paper page
  2. Gainza, P. et al. De novo design of protein interactions with learned surface fingerprints. Nature 617, 176–184 (2023). · DOI · Paper page
  3. Bennett, N. R. et al. Improving de novo protein binder design with deep learning. Nat. Commun. 14, 2625 (2023). · DOI · Paper page
  4. Frank, C. et al. Scalable protein design using optimization in a relaxed sequence space. Science 386, 439–445 (2024). · DOI
  5. Goverde, C. A. et al. Computational design of soluble and functional membrane protein analogues. Nature 631, 449–458 (2024). · DOI
  6. Wicky, B. I. M. et al. Hallucinating symmetric protein assemblies. Science 0, eadd1964 (2022).
  7. Bushuiev, A. et al. Learning to design protein-protein interactions with enhanced generalization. in Twelfth Intl Conf. Learning Representations (ICLR, Vienna, 2024).
  8. Walter, M. R. The role of structure in the biology of interferon signaling. Front. Immunol. 11, 606489 (2020). · DOI
  9. Hermiston, M. L., Xu, Z. & Weiss, A. CD45: a critical regulator of signaling thresholds in immune cells. Annu. Rev. Immunol. 21, 107–137 (2003). · DOI · Paper page
  10. Vecchio, A. J., Rathnayake, S. S. & Stroud, R. M. Structural basis for Clostridium perfringens enterotoxin targeting of claudins at tight junctions in mammalian gut. Proc. Natl Acad. Sci. USA 118, e2024651118 (2021). · DOI
  11. Gönczy, P. & Hatzopoulos, G. N. Centriole assembly at a glance. J. Cell Sci. 132, jcs228833 (2019). · DOI
  12. Hatzopoulos, G. N. et al. Tuning SAS-6 architecture with monobodies impairs distinct steps of centriole assembly. Nat. Commun. 12, 3805 (2021). · DOI
  13. Bousquet, J. et al. Allergic rhinitis. Nat. Rev. Dis. Prim. 6, 95 (2020). · DOI · Paper page
  14. Dall’Antonia, F., Pavkov-Keller, T., Zangger, K. & Keller, W. Structure of allergens and structure based epitope predictions. Methods 66, 3–21 (2014). · DOI
  15. Ipsen, H. & Løwenstein, H. Isolation and immunochemical characterization of the major allergen of birch pollen (Betula verrucosa). J. Allergy Clin. Immunol. 72, 150–159 (1983). · DOI · Paper page
  16. Tai, H.-Y. et al. The different modes of binding of the dust mite allergens, Der f 7 and Der p 7, on a monoclonal antibody WH9 contribute to the differential reactivity. J. Microbiol. Immunol. Infect. 51, 478–484 (2018). · DOI
  17. Pang, S. L. et al. Crystal structure and epitope analysis of house dust mite allergen Der f 21. Sci. Rep. 9, 4933 (2019). · DOI
  18. Kofler, S. et al. Crystallographically mapped ligand binding differs in high and low IgE binding isoforms of birch pollen allergen Bet v 1. J. Mol. Biol. 422, 109–123 (2012). · DOI
  19. Atanasio, A. et al. Targeting immunodominant Bet v 1 epitopes with monoclonal antibodies prevents the birch allergic response. J. Allergy Clin. Immunol. 149, 200–211 (2022). · DOI
  20. Wiegand, T., Karambelkar, S., Bondy-Denomy, J. & Wiedenheft, B. Structures and strategies of anti-CRISPR-mediated immune suppression. Annu. Rev. Microbiol. 74, 21–37 (2020). · DOI
  21. Jiang, F., Zhou, K., Ma, L., Gressel, S. & Doudna, J. A. A Cas9–guide RNA complex preorganized for target DNA recognition. Science 348, 1477–1481 (2015). · DOI · Paper page
  22. Shibata, M. et al. Real-space and real-time dynamics of CRISPR-Cas9 visualized by high-speed atomic force microscopy. Nat. Commun. 8, 1430 (2017). · DOI · Paper page
  23. Dong, D. et al. Structural basis of CRISPR–SpyCas9 inhibition by an anti-CRISPR protein. Nature 546, 436–439 (2017). · DOI · Paper page
  24. Zhu, Y. et al. Diverse mechanisms of CRISPR-Cas9 inhibition by Type IIC anti-CRISPR proteins. Mol. Cell 74, 296–309 (2019). · DOI
  25. Liu, L., Yin, M., Wang, M. & Wang, Y. Phage AcrIIA2 DNA mimicry: structural basis of the CRISPR and Anti-CRISPR arms race. Mol. Cell 73, 611–620 (2019). · DOI
  26. Kuzmenko, A. et al. DNA targeting and interference by a bacterial Argonaute nuclease. Nature 587, 632–637 (2020). · DOI
  27. Hegge, J. W. et al. DNA-guided DNA cleavage at moderate temperatures by Clostridium butyricum Argonaute. Nucleic Acids Res. 47, 5809–5821 (2019). · DOI · Paper page
  28. Münch, R. C. et al. Displaying high-affinity ligands on adeno-associated viral vectors enables tumor cell-specific and safe gene transfer. Mol. Ther. 21, 109–118 (2013). · DOI · Paper page
  29. Zambaldi, V. et al. De novo design of high-affinity protein binders with AlphaProteo. Preprint at https://arxiv.org/abs/2409.08022 (2024). · arXiv
  30. Cotet, T.-S. et al. Crowdsourced protein design: lessons from the adaptyv EGFR binder competition. Preprint at bioRxiv https://doi.org/10.1101/2025.04.17.648362 (2025). · DOI
  31. Berger, S. et al. Preclinical proof of principle for orally delivered Th17 antagonist miniproteins. Cell 187, 4305–4317 (2024). · DOI
  32. Goudy, O. J., Nallathambi, A., Kinjo, T., Randolph, N. Z. & Kuhlman, B. In silico evolution of autoinhibitory domains for a PD-L1 antagonist using deep learning models. Proc. Natl Acad. Sci. USA 120, e2307371120 (2023). · DOI
  33. Baryshev, A. et al. Massively parallel measurement of protein–protein interactions by sequencing using MP3-seq. Nat. Chem. Biol. 20, 1514–1523 (2024). · DOI
  34. Leman, J. K. et al. Macromolecular modeling and design in Rosetta: recent methods and frameworks. Nat. Methods 17, 665–680 (2020). · DOI · Paper page
  35. Coventry, B. & Baker, D. Protein sequence optimization with a pairwise decomposable penalty for buried unsatisfied hydrogen bonds. PLoS Comput. Biol. 17, e1008061 (2021). · DOI
  36. Roney, J. P. & Ovchinnikov, S. State-of-the-art estimation of protein model accuracy using AlphaFold. Phys. Rev. Lett. 129, 238101 (2022). · DOI · Paper page
  37. Pacesa, M. et al. R-loop formation and conformational activation mechanisms of Cas9. Nature 609, 191–196 (2022). · DOI · Paper page
  38. Vonrhein, C. et al. Data processing and analysis with the autoPROC toolbox. Acta Crystallogr. Sect. D 67, 293–302 (2011). · DOI · Paper page
  39. Liebschner, D. et al. Macromolecular structure determination using X-rays, neutrons and electrons: recent developments in Phenix. Acta Crystallogr. Sect. D 75, 861–877 (2019). · DOI · Paper page
  40. Emsley, P., Lohkamp, B., Scott, W. G. & Cowtan, K. Features and development of Coot. Acta Crystallogr. Sect. D 66, 486–501 (2010). · DOI · Paper page
  41. Williams, C. J. et al. MolProbity: more and better reference data for improved all‐atom structure validation. Protein Sci. 27, 293–315 (2018). · DOI · Paper page
  42. Pettersen, E. F. et al. UCSF ChimeraX: structure visualization for researchers, educators, and developers. Protein Sci. 30, 70–82 (2021). · DOI · Paper page
  43. Punjani, A., Rubinstein, J. L., Fleet, D. J. & Brubaker, M. A. cryoSPARC: algorithms for rapid unsupervised cryo-EM structure determination. Nat. Methods 14, 290–296 (2017). · DOI · Paper page
  44. Punjani, A., Zhang, H. & Fleet, D. J. Non-uniform refinement: adaptive regularization improves single-particle cryo-EM reconstruction. Nat. Methods 17, 1214–1221 (2020). · DOI · Paper page
  45. Marquart, K. F. et al. Predicting base editing outcomes with an attention-based deep learning algorithm trained on high-throughput target library screens. Nat. Commun. 12, 5114 (2021). · DOI · Paper page
  46. Clement, K. et al. CRISPResso2 provides accurate and rapid genome editing sequence analysis. Nat. Biotechnol. 37, 224–226 (2019). · DOI · Paper page
  47. Gaudry, J.-P., Aebi, A., Valdés, P. & Schneider, B. L. Production and purification of adeno-associated viral vectors (AAVs) using orbitally shaken HEK293 cells. Methods Mol. Biol. 2810, 55–74 (2024). · DOI

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Metadata

Authors
Pacesa, M., Nickel, L., Schellhaas, C., Schmidt, J., Pyatova, E., Kissling, L., Barendse, P., Choudhury, J., Kapoor, S., Alcaraz-Serna, A., Cho, Y., Ghamary, K. H., Vinué, L., Yachnin, B. J., Wollacott, A. M., Buckley, S., Westphal, A. H., Lindhoud, S., Georgeon, S., Goverde, C. A., Hatzopoulos, G. N., Gönczy, P., Muller, Y. D., Schwank, G., Swarts, D. C., Vecchio, A. J., Schneider, B. L., Ovchinnikov, S., & Correia, B. E.
Venue
Nature
Publisher
Springer Science and Business Media LLC
Published
2025-08-27
Volume
646
Issue
8084
Pages
483-492
ISSN
  • 0028-0836 (ISSN-L)
  • 1476-4687
DOI
10.1038/s41586-025-09429-6
OpenAlex
W4413759428
Cited by
236
References
25
FWCI
60.33
Type
journal article
Language
en
Open access
hybrid
Licence

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CoChat. (2026). Citation check: One-shot design of functional protein binders with BindCraft. https://app.cochat.ai/papers/10.1038/s41586-025-09429-6
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Pacesa, M., Nickel, L., Schellhaas, C., Schmidt, J., Pyatova, E., Kissling, L., Barendse, P., Choudhury, J., Kapoor, S., Alcaraz-Serna, A., Cho, Y., Ghamary, K. H., Vinué, L., Yachnin, B. J., Wollacott, A. M., Buckley, S., Westphal, A. H., Lindhoud, S., Georgeon, S., . . . Correia, B. E. (2025). One-shot design of functional protein binders with BindCraft. Nature. https://doi.org/10.1038/s41586-025-09429-6

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