ArticleMethods in molecular biology (Clifton, N.J.)2025
Prediction of Protein-Peptide Binding Sites Using PepBCL.
Article in Methods in molecular biology (Clifton, N.J.), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- Peptide-based drug design using generative AI.Chemical communications (Cambridge, England) · 2026Review
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Authors and funding
3 authors.
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Abstract
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.
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Registered trials
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