Collaborative Learning Macroscopic Binding Trends and Microscopic Residue Interactions to Predict Peptide-Protein Interactions.

Abstract

Short peptides and their structural modifications have demonstrated significant potential in the field of therapeutic drug development. During the research and development process, peptide-protein interaction plays a crucial role for screening highly effective peptides. Although traditional experimental methods can identity peptide-protein interactions, their time-consuming and resource-intensive nature make researchers develop various of computational alternatives. In addition, accurately predicting these interactions necessitates both the macroscopic molecular binding affinity and the precise interaction patterns at the microscopic residue level. Existing computational methods face limitations, as they are typically confined to modeling at a single level, resulting in restricted prediction accuracy. To address this gap, we propose MMPepPro, a dual-level biofeature collaborative interaction learning framework that integrates macro-level binding trends with micro-level residue interaction features. Trained on 19,187 peptide-protein complexes, MMPepPro combines molecular-level and amino acid-level features to achieve comprehensive modeling. Experimental validation demonstrates the model's superior performance across all evaluation metrics compared to other state-of-the-art methods in peptide-protein interaction prediction. More notably, its generalization performance across other four datasets validates the universality of this method, which will aid in the development of peptide-protein drugs.