Abstract
Accumulating evidence has shown that protein-peptide interactions (PPepIs) are critical for understanding biological processes and developing peptide-based therapeutics. While deep learning-based protein-protein interaction (PPI) prediction showed promise, it suffers from poor generalization and overfitting problems. This study addresses these challenges by focusing training on short proteins containing much less redundant noninteracting sequence. To avoid artificial PPI, only the experimentally validated PPI pairs from STRING database were used to construct the PPI training dataset. We integrated protein sequence and structure information and presented a multilevel deep learning framework. Training on short-protein datasets yielded higher accuracy and computational efficiency compared with training on long-protein datasets. Moreover, we applied the model to delineate human protein and SARS-CoV-2 virus PPI networks. Notably, we screened PPepIs of current drug peptides with human proteins and SARS-CoV-2 viral proteins, identifying numerous potential side effect or new therapeutic targets. Together, our retrained model could be extensively applied to delineate PPepI network, contribute to peptide drug target identification and side effect analysis, and also provide ample resource for viral infection investigations.