Publication

Targeting protein-ligand neosurfaces using a generalizable deep learning approach

2024

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.

Official source

https://infoscience.epfl.ch/entities/publication/4cefe87c-0035-43bd-9419-454d8dc2e808

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2024

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/4cefe87c-0035-43bd-9419-454d8dc2e808

About this result

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Targeting protein-ligand neosurfaces using a generalizable deep learning approach

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