EnsemPred-ACP: Combining machine and deep learning to improve anticancer peptide prediction

Anticancer peptide (ACP) has emerged as potent therapeutic agents owing to its ability to selectively target cancer cells while minimising toxicity to healthy cells. However, the accurate computational prediction of ACP remains challenging because of the complex molecular mechanisms underlying cancer. In this study, we introduce EnsemPred-ACP, an innovative ensemble framework that combines machine learning (ML) and deep learning (DL) approaches to enhance ACP prediction. Our primary innovation is the introduction of binary profile features (BPF) to augment pre-trained protein embeddings, thereby capturing position-specific patterns crucial for ACP identification. The framework used a dual-pipeline architecture; ML models processed handcrafted sequence features and embeddings, whereas DL models handled BPF-enhanced embeddings. Upon evaluation with independent datasets, EnsemPred-ACP achieved an accuracy of 0.863, sensitivity of 0.897, and specificity of 0.830, notably outperforming existing methods. The model demonstrated a strong generalisation performance, achieving an area under the receiver operating characteristic curve of 0.93. Ablation studies on independent datasets further highlighted the substantial impact of BPF, enhancing the prediction accuracy by 2.5 % and 11.1 % when integrated with ESM2 and ProtT5 embeddings, respectively. These results demonstrate the effectiveness of our integrated approach in accurately identifying potential therapeutic peptides, thereby contributing to the advancement of peptide-based cancer therapeutics.