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Cited by 236

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De novo design of protein interactions with learned surface fingerprints

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Gainza, Pablo, Wehrle, Sarah, Van Hall-Beauvais, Alexandra, Marchand, Anthony, Scheck, Andreas, Harteveld, Zander, Buckley, Stephen, Ni, Dongchun, Tan, Shuguang, Sverrisson, Freyr, Goverde, Casper, Turelli, Priscilla, Raclot, Charlène, Teslenko, Alexandra, Pacesa, Martin, Rosset, Stéphane, Georgeon, Sandrine, Marsden, Jane, Petruzzella, Aaron, Liu, Kefang, Xu, Zepeng, Chai, Yan, Han, Pu, Gao, George F., Oricchio, Elisa, Fierz, Beat, Trono, Didier, Stahlberg, Henning, Bronstein, Michael, Correia, Bruno E.·2023·Nature
DOI 10.1038/s41586-023-05993-x
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Abstract

Physical interactions between proteins are essential for most biological processes governing life 1. However, the molecular determinants of such interactions have been challenging to understand, even as genomic, proteomic and structural data increase. This knowledge gap has been a major obstacle for the comprehensive understanding of cellular protein–protein interaction networks and for the de novo design of protein binders that are crucial for synthetic biology and translational applications 2–9. Here we use a geometric deep-learning framework operating on protein surfaces that generates fingerprints to describe geometric and chemical features that are critical to drive protein–protein interactions 10. We hypothesized that these fingerprints capture the key aspects of molecular recognition that represent a new paradigm in the computational design of novel protein interactions. As a proof of principle, we computationally designed several de novo protein binders to engage four protein targets: SARS-CoV-2 spike, PD-1, PD-L1 and CTLA-4. Several designs were experimentally optimized, whereas others were generated purely in silico, reaching nanomolar affinity with structural and mutational characterization showing highly accurate predictions. Overall, our surface-centric approach captures the physical and chemical determinants of molecular recognition, enabling an approach for the de novo design of protein interactions and, more broadly, of artificial proteins with function.

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Metadata

Authors
Gainza, P., Wehrle, S., Van Hall-Beauvais, A., Marchand, A., Scheck, A., Harteveld, Z., Buckley, S., Ni, D., Tan, S., Sverrisson, F., Goverde, C., Turelli, P., Raclot, C., Teslenko, A., Pacesa, M., Rosset, S., Georgeon, S., Marsden, J., Petruzzella, A., Liu, K., Xu, Z., Chai, Y., Han, P., Gao, G. F., Oricchio, E., Fierz, B., Trono, D., Stahlberg, H., Bronstein, M., & Correia, B. E.
Venue
Nature
Publisher
Springer Science and Business Media LLC
Published
2023-04-26
Volume
617
Issue
7959
Pages
176-184
ISSN
  • 0028-0836 (ISSN-L)
  • 1476-4687
DOI
10.1038/s41586-023-05993-x
OpenAlex
W4367049161
Cited by
236
References
19
FWCI
17.63
Type
journal article
Language
en
Open access
hybrid
Licence

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CoChat. (2026). Citation check: De novo design of protein interactions with learned surface fingerprints. https://app.cochat.ai/papers/10.1038/s41586-023-05993-x
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Gainza, P., Wehrle, S., Van Hall-Beauvais, A., Marchand, A., Scheck, A., Harteveld, Z., Buckley, S., Ni, D., Tan, S., Sverrisson, F., Goverde, C., Turelli, P., Raclot, C., Teslenko, A., Pacesa, M., Rosset, S., Georgeon, S., Marsden, J., Petruzzella, A., . . . Correia, B. E. (2023). De novo design of protein interactions with learned surface fingerprints. Nature. https://doi.org/10.1038/s41586-023-05993-x

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