Targeting protein-ligand neosurfaces with a generalizable deep learning tool

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. Despite recent advances, computational tools for the design of novel chemically-induced protein interactions have remained a challenging task for the field. 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 geometric deep learning approach based on learned molecular surface representations 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 assessed by mutational and structural characterization. Remarkably, surface fingerprints previously trained only on proteins can be applied to neosurfaces emerging from small molecules, serving as a powerful demonstration of generalizability that is uncommon in other deep learning approaches. We anticipate that the designed chemically-induced protein interactions hold the potential to expand the sensing repertoire and the assembly of new synthetic pathways in engineered cells for innovative drug-controlled cell-based therapies