TPpred-SC: multi-functional therapeutic peptide prediction based on multi-label supervised contrastive learning

Therapeutic peptides contribute significantly to human health and have the potential for personalized medicine. The prediction for the therapeutic peptides is beneficial and emerging for the discovery of drugs. Although several computational approaches have emerged to discern the functions of therapeutic peptides, predicting multi-functional therapeutic peptide types is challenging. In this research, a novel approach termed TPpred-SC has been introduced. This method leverages a pretrained protein language model alongside multi-label supervised contrastive learning to predict multi-functional therapeutic peptides. The framework incorporates sequential semantic information directly from large-scale protein sequences in TAPE. Then, TPpred-SC exploits multi-label supervised contrastive learning to enhance the representation of peptide sequences for imbalanced multi-label therapeutic peptide prediction. The experimental findings demonstrate that TPpred-SC achieves superior performance compared to existing related methods. To serve our work more efficiently, the web server of TPpred-SC can be accessed at http://bliulab.net/TPpred-SC.