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.