Deep learning molecular interaction motifs from Receptor structure alone

Interactions of proteins with other molecules are often mediated by a set of critical binding motifs on their surfaces. The majority of traditional binder design relied on motifs borrowed from known binder molecules, which highly restricted its applicability to novel targets or to new binding sites. In this work, we present a deep learning network MotifGen that predicts potential binder motifs directly from receptor structures without any further supporting information. MotifGen generates motif profiles at the receptor surface for 14 types of functional groups or 6 chemical interaction classes. These profiles are not only highly human interpretable, but also provide pre-trained embedding inputs for versatile few-shot binder design applications. We demonstrate MotifGens effectiveness through its application to peptide binder design and small-molecule binding site prediction, where it either surpassed existing methods or added significant value when integrated together. We expect our motif-centric binder design strategy can facilitate discovering novel binders for challenging receptor targets.