PepNN: a deep attention model for the identification of peptide binding sites

Abstract

AbstractProtein-peptide interactions play a fundamental role in facilitating many cellular processes, but remain underexplored experimentally and difficult to model computationally. Here, we introduce PepNN-Struct and PepNN-Seq, structure and sequence-based approaches for the prediction of peptide binding sites on a protein given the sequence of a peptide ligand. The models make use of a novel reciprocal attention module that is able to better reflect biochemical realities of peptides undergoing conformational changes upon binding. To compensate for the scarcity of peptide-protein complex structural information, we make use of available protein-protein complex and protein sequence information through a series of transfer learning steps. PepNN-Struct achieves state-of-the-art performance on the task of identifying peptide binding sites, with a ROC AUC of 0.893 and an MCC of 0.483 on an independent test set. Beyond prediction of binding sites on proteins with a known peptide ligand, we also show that the developed models make reasonable agnostic predictions, allowing for the identification of novel peptide binding proteins.