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.

doi:10.1101/2024.03.25.585721

bioRxiv · 10.1101/2024.03.25.585721

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.

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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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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.. 2024-03-28. Targeting protein-ligand neosurfaces using a generalizable deep learning approach. https://doi.org/10.1101/2024.03.25.585721

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