Aegis: a transformer-based deep learning framework for the accurate identification of anticancer peptides

Anticancer peptides (ACPs) are promising therapeutic agents with selective cytotoxicity toward cancer cells and minimal toxicity toward normal cells. However, the experimental identification and characterization of ACPs are often costly, time-consuming, and inefficient. Computational approaches provide promising alternatives for the rapid and accurate prediction of ACPs.

Here, we introduce Aegis, a novel transformer-based deep learning framework designed for precise ACP identification. We systematically evaluated various machine learning and deep learning models via multiple feature extraction methods, including the composition of k-spaced amino acid pairs (CKSAAP), CTD composition (CTDC), CTD transition (CTDT), CTD distribution (CTDD), and pseudo amino acid composition (PAAC) methods. Comprehensive feature importance analyses via analysis of variance (ANOVA), ReliefF, and SHapley Additive exPlanations (SHAP) methods were performed, followed by incremental feature selection (IFS) to determine the optimal subset of discriminative features. Using the 103 optimal features identified via SHAP, Aegis achieves state-of-the-art (SOTA) performance on an independent testing dataset, outperforming existing ACP prediction models. Furthermore, compositional analysis revealed that ACP sequences are significantly enriched in positively charged and hydrophobic residues.

Overall, our study demonstrates the exceptional potential of transformer-based deep learning for ACP identification, laying a foundation for future computational screening and the clinical development of novel ACPs.