 De novo design of protein interactions with learned surface fingerprintsPablo Gainza,
Sarah Wehrle,
Alexandra Hall-Beauvais,
Anthony Marchand,
Andreas Scheck,
Zander Harteveld,
Stephen Buckley,
Dongchun Ni,
Shuguang Tan,
Freyr Sverrisson,
Casper Goverde,
Priscilla Turelli,
Charlène Raclot,
Alexandra Teslenko,
Martin Pacesa,
Stéphane Rosset,
Sandrine Georgeon,
Jane Marsden,
Aaron Petruzzella,
Kefang Liu,
Zepeng Xu,
Yan Chai,
Pu Han,
George F. Gao,
Elisa Oricchio,
Beat Fierz,
Didier Trono,
Henning Stahlberg,
Michael Bronstein () and
Bruno E. Correia ()
Nature, 2023, vol. 617, issue 7959, 176-184 Abstract: Abstract Physical interactions between proteins are essential for most biological processes governing life1. 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 applications2–9. 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 interactions10. 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. Date: 2023 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:nat:nature:v:617:y:2023:i:7959:d:10.1038_s41586-023-05993-x Ordering information: This journal article can be ordered from DOI: 10.1038/s41586-023-05993-x Access Statistics for this article Nature is currently edited by Magdalena Skipper More articles in Nature from Nature |
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