Targeting protein–ligand neosurfaces with a generalizable deep learning tool

Anthony Marchand, Stephen Buckley, Arne Schneuing, Martin Pacesa, Maddalena Elia, Pablo Gainza, Evgenia Elizarova, Rebecca M. Neeser, Pao-Wan Lee, Luc Reymond, Yangyang Miao, Leo Scheller, Sandrine Georgeon, Joseph Schmidt, Philippe Schwaller, Sebastian J. Maerkl, Michael Bronstein and Bruno E. Correia ()
Additional contact information
Anthony Marchand: Ecole polytechnique fédérale de Lausanne
Stephen Buckley: Ecole polytechnique fédérale de Lausanne
Arne Schneuing: Ecole polytechnique fédérale de Lausanne
Martin Pacesa: Ecole polytechnique fédérale de Lausanne
Maddalena Elia: Ecole polytechnique fédérale de Lausanne
Pablo Gainza: Ecole polytechnique fédérale de Lausanne
Evgenia Elizarova: Ecole polytechnique fédérale de Lausanne
Rebecca M. Neeser: Ecole polytechnique fédérale de Lausanne
Pao-Wan Lee: Ecole polytechnique fédérale de Lausanne
Luc Reymond: Ecole polytechnique fédérale de Lausanne
Yangyang Miao: Ecole polytechnique fédérale de Lausanne
Leo Scheller: Ecole polytechnique fédérale de Lausanne
Sandrine Georgeon: Ecole polytechnique fédérale de Lausanne
Joseph Schmidt: Ecole polytechnique fédérale de Lausanne
Philippe Schwaller: Ecole polytechnique fédérale de Lausanne
Sebastian J. Maerkl: Ecole polytechnique fédérale de Lausanne
Michael Bronstein: University of Oxford
Bruno E. Correia: Ecole polytechnique fédérale de Lausanne

Nature, 2025, vol. 639, issue 8054, 522-531

Abstract: Abstract Molecular recognition events between proteins drive biological processes in living systems1. However, higher levels of mechanistic regulation have emerged, in which protein–protein interactions are conditioned to small molecules2–5. Despite recent advances, computational tools for the design of new chemically induced protein interactions have remained a challenging task for the field6,7. Here we present a computational strategy for the design of proteins that target neosurfaces, that is, surfaces arising from protein–ligand complexes. To develop this strategy, we leveraged a geometric deep learning approach based on learned molecular surface representations8,9 and experimentally validated binders against three drug-bound protein complexes: Bcl2–venetoclax, DB3–progesterone and PDF1–actinonin. All binders demonstrated high affinities and accurate specificities, as assessed by mutational and structural characterization. Remarkably, surface fingerprints previously trained only on proteins could be applied to neosurfaces induced by interactions with small molecules, providing a powerful demonstration of generalizability that is uncommon in other deep learning approaches. We anticipate that such designed chemically induced protein interactions will have the potential to expand the sensing repertoire and the assembly of new synthetic pathways in engineered cells for innovative drug-controlled cell-based therapies10.

Date: 2025
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DOI: 10.1038/s41586-024-08435-4

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