ArticleScientific reports2023
PepCNN deep learning tool for predicting peptide binding residues in proteins using sequence, structural, and language model features.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed, 34 citations in OpenAlex.
- ProVenTL: a transfer-learning framework for predicting peptide-protein interactions derived from snake venom for cancer therapeutics.Journal of computer-aided molecular design · 2026Article
- Peptide-based drug design using generative AI.Chemical communications (Cambridge, England) · 2026Review
- Bioactive peptides targeting dyslipidemia and atherosclerosis: from computational discovery and data bottlenecks to precision medicine translation.Frontiers in molecular biosciences · 2026Review
- An ensemble-based model comprising deep learning for predicting peptide-binding residues in proteins.NAR genomics and bioinformatics · 2025Article
- Deep-ProBind: binding protein prediction with transformer-based deep learning model.BMC bioinformatics · 2025Article
- Leveraging large language models for peptide antibiotic design.Cell reports. Physical science · 2025Article
- AI/ML-empowered approaches for predicting T Cell-mediated immunity and beyond.Frontiers in immunology · 2025Article
- CNN-Meth: A Tool to Accurately Predict Lysine Methylation Sites Using Evolutionary Information-Based Protein Modeling.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Recent Advances in Peptide Drug Discovery: Novel Strategies and Targeted Protein Degradation.Pharmaceutics · 2024Review
- DeepBP: Ensemble deep learning strategy for bioactive peptide prediction.BMC bioinformatics · 2024Article
- PepCA: Unveiling protein-peptide interaction sites with a multi-input neural network model.iScience · 2024Article
- GAPS: a geometric attention-based network for peptide binding site identification by the transfer learning approach.Briefings in bioinformatics · 2024Article
- LMCrot: an enhanced protein crotonylation site predictor by leveraging an interpretable window-level embedding from a transformer-based protein language model.Bioinformatics (Oxford, England) · 2024Article
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Protein-peptide interactions play a crucial role in various cellular processes and are implicated in abnormal cellular behaviors leading to diseases such as cancer. Therefore, understanding these interactions is vital for both functional genomics and drug discovery efforts. Despite a significant increase in the availability of protein-peptide complexes, experimental methods for studying these interactions remain laborious, time-consuming, and expensive. Computational methods offer a complementary approach but often fall short in terms of prediction accuracy. To address these challenges, we introduce PepCNN, a deep learning-based prediction model that incorporates structural and sequence-based information from primary protein sequences. By utilizing a combination of half-sphere exposure, position specific scoring matrices from multiple-sequence alignment tool, and embedding from a pre-trained protein language model, PepCNN outperforms state-of-the-art methods in terms of specificity, precision, and AUC. The PepCNN software and datasets are publicly available at https://github.com/abelavit/PepCNN.git .
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