A Multi-Objective Comprehensive Framework for Predicting Protein-Peptide Interactions and Binding Residues

Ruheng Wang, Xuetong Yang, Chao Pang, Leyi Wei

Abstract: Identifying protein–peptide interaction pairs and their corresponding binding residues is crucial and can greatly facilitate peptide therapeutic design as well as improve our understanding of protein function mechanisms. Recently, several computational approaches have been proposed to solve the protein-peptide interaction prediction problem. However, most existing prediction methods cannot simultaneously predict protein–peptide interaction pairs and their binding residues directly from the protein and peptide sequences. 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 8900 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.

External IDs:dblp:journals/titb/WangYPW25