STACKION: Ion Channel-Modulating Peptides Identification Using Stacking-Based Ensemble Machine Learning

Ion channel-modulating peptides play a crucial role in various physiological processes, making their identification a significant area of research. In this study, we present STACKION, a novel stacking-based ensemble machine-learning approach for the identification of ion channel-modulating peptides. Five feature extraction methods, including amino acid composition (AAC), pseudo-amino acid composition (PAAC), dipeptide composition (DPC), tripeptide composition (TPC), and composition-transition-distribution (CTDC), were employed to extract discriminative features from peptide sequences. Additionally, eight machine learning algorithms were applied to build predictive models. Through extensive experiments and evaluation, we demonstrate that our proposed method, STACKION, consistently outperformed the other feature extraction methods in predicting sodium (Na
⁺
) and calcium (Ca
⁺
) ion channel-modulating peptides with the DPC feature extraction method. Moreover, when combined with the PAAC feature extraction method, STACKION demonstrated excellent predictive accuracy in identifying potassium (K
⁺
) ion channel-modulating peptides. These findings highlight the effectiveness of STACKION in peptide identification, with potential implications for understanding peptide functionality and the development of therapeutic agents targeting ion channels.