European Research Council (starting grant no. 716058)
European Union
Swiss National Science Foundation
NCCR transcure 185544
Switzerland
Swiss National Science Foundation
310030_188744
Switzerland
Swiss National Science Foundation
NCCR Molecular Systems Engineering
Switzerland
Swiss National Science Foundation
NCCR Chemical Biology
Switzerland
Citation
Journal: Nature / Year: 2023 Title: De novo design of protein interactions with learned surface fingerprints. Authors: Pablo Gainza / Sarah Wehrle / Alexandra Van Hall-Beauvais / Anthony Marchand / Andreas Scheck / Zander Harteveld / Stephen Buckley / Dongchun Ni / Shuguang Tan / Freyr Sverrisson / Casper ...Authors: Pablo Gainza / Sarah Wehrle / Alexandra Van 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 / Bruno E Correia / Abstract: Physical interactions between proteins are essential for most biological processes governing life. However, the molecular determinants of such interactions have been challenging to understand, even ...Physical interactions between proteins are essential for most biological processes governing life. 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. 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. 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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