Targeting protein-ligand neosurfaces using a generalizable deep learning approach
Marchand, A.; Buckley, S.; Schneuing, A.; Pacesa, M.; Gainza, P.; Elizarova, E.; Neeser, R. M.; Lee, P.-W.; Reymond, L.; Elia, M.; Scheller, L.; Georgeon, S.; Schmidt, J.; Schwaller, P.; Maerkl, S. J.; Bronstein, M.; Correia, B. E.
10.1101/2024.03.25.585721 bioRxivShow abstract
Molecular recognition events between proteins drive biological processes in living systems. However, higher levels of mechanistic regulation have emerged, where protein-protein interactions are conditioned to small molecules. Here, we present a computational strategy for the design of proteins that target neosurfaces, i.e. surfaces arising from protein-ligand complexes. To do so, we leveraged a deep learning approach based on learned molecular surface representations and experimentally validated binders against three drug-bound protein complexes. Remarkably, surface fingerprints trained only on proteins can be applied to neosurfaces emerging from small molecules, serving as a powerful demonstration of generalizability that is uncommon in deep learning approaches. The designed chemically-induced protein interactions hold the potential to expand the sensing repertoire and the assembly of new synthetic pathways in engineered cells.
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