A multi-objective comprehensive framework for predicting protein-peptide interactions and binding residues

The identification of protein-peptide interacting pairs and their corresponding binding residues is fundamentally crucial and can greatly facilitate the peptide therapeutics designing and understanding the mechanisms of protein functions. Recently, several computational approaches have been proposed to solve the protein-peptide interaction prediction problem. However, most existing prediction methods cannot directly predict the protein-peptide interacting pairs as well as the binding residues from protein and peptide sequences simultaneously. Here, we developed a Comprehensive Protein-Peptide Interaction prediction Framework (CPPIF), to predict both binary protein-peptide interaction and their binding residues. We also constructed a benchmark dataset containing more than 8,900 protein-peptide interacting pairs with non-covalent interactions and their corresponding binding residues to systematically evaluate the performances of existing models. Comprehensive evaluation on the benchmark datasets demonstrated that CPPIF can successfully predict the non-covalent protein-peptide interactions that cannot be effectively captured by previous prediction methods. Moreover, CPPIF outperformed other state-of-the-art methods in predicting binding residues in the peptides and achieved good performance in the identification of important binding residues in the proteins.