Prediction of Protein-Peptide Binding Residues via Pre-Trained Protein Language Model and Progressive Contrastive Representation Learning

Accurately predicting protein-peptide binding residues is paramount in elucidating protein functions, unraveling disease mechanisms, and advancing drug discovery. Most existing methods for predicting protein-peptide binding residues are either highly reliant on protein structural data or lack effective ways to extract valuable information from protein sequences. Furthermore, the issue of data imbalance significantly constrains the predictive performance of these methods. To address the above problems, we propose a PepPCR model based on the pre-trained protein language model ProtT5 and progressive contrastive representation learning. Specifically, the pre-trained model ProtT5 can automatically extract high-dimensional feature representations relevant to protein functions from protein sequences, while the progressive contrastive representation learning can optimize the feature representations learned by the ProtT5 model within imbalanced data. Experimental results show that the classification performance of the PepPCR model surpasses the existing state-of-the-art methods. In particular, compared with the sequence-based state-of-the-art method PepBCL, PepPCR improves Recall, AUC, and Matthews Correlation Coefficient (MCC) by 60.3%, 7.2%, and 7.5% respectively on the test set TE125, and improves Recall, AUC, and MCC by 60.3%, 4.6%, and 15.4% respectively on the test set TE639.