Identifying the protein–peptide binding residues is fundamentally important to understanding the mechanisms of protein functions and drug discovery. Although several computational methods have been developed, they highly rely on third-party tools or information for feature design, easily resulting in low computational efficacy and suffering from low predictive performance. We describe how to use an end-to-end computational method PepBCL that is free with feature design for high-throughput prediction of protein–peptide binding sites. PepBCL outperforms the state-of-the-art methods under benchmarking comparison and achieves more robust performance based on protein sequences only. We can automatically extract and learn high-latent representations of protein sequences relevant to protein structure and functions by the introduction of a well pretrained protein large language model. We overview our method and discuss how to run the supported codes to reproduce our predictor.