An ensemble-based model comprising deep learning for predicting peptide-binding residues in proteins.

Protein-peptide interactions are fundamental to numerous cellular processes and are linked to diseases like cancer when disrupted. Understanding these interactions is critical for both functional genomics and drug discovery. Despite growing availability of protein-peptide complexes, experimental methods to study them remain resource-intensive and costly. While computational approaches offer a complementary solution, their predictive accuracy is often inadequate. To overcome these limitations, we present PepENS, an ensemble model combining deep learning and traditional machine learning techniques that integrates both structural and sequence-based features from primary protein sequences. By leveraging half-sphere exposure, position-specific scoring matrices from multiple-sequence alignments, and embeddings from a pre-trained protein language model, PepENS demonstrates superior performance compared to the state-of-the-art methods. The proposed model demonstrated strong performance, achieving a precision of 0.596 and an AUC of 0.860 on the Dataset 1 test set. On the Dataset 2 test set, it attained a precision of 0.539 and an AUC of 0.846. Notably, these results reflect improvements over state-of-the-art methods in terms of precision and AUC by 2.8% and 0.5%, respectively, on Dataset 1, and by 2.3% and 2.4%, respectively, on Dataset 2. The PepENS software and associated datasets are available at https://doi.org/10.6084/m9.figshare.28490012.v2.