A Dual-Model Machine Learning Framework for Interpretable Design and Ensemble Prediction of C‑Amidated Antimicrobial Peptides

Antimicrobial peptides (AMPs) offer promising alternatives to conventional antibiotics, yet most predictive models fail to account for chemical modifications that influence real-world efficacy. Among these, C-terminal amidation is a widely adopted and effective strategy that improves structural stability, membrane interaction, and protease resistance. In this study, we established an integrated framework for the design and prediction of C-terminal amidated AMPs targeting Escherichia coli. Our approach combined a design-oriented model based on an interpretable Explainable Boosting Machine (EBM), which extracts actionable sequence-level design rules, together with a reliable deployment model, built on a fine-tuned ESM2 deep learning architecture. The resulting tool, CAmidPred, enables both predictive classification and amino acid pattern analysis with outputs examined in relation to published alanine-scanning experiments. Using these models, we identified a pardaxin variant with improved activity against E. coli, demonstrating the practical utility of the dual-model framework in targeted AMP design.