Practical Machine Learning Framework for Designing and Predicting C-Amidated Antimicrobial Peptides

Abstract 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. To address the limitations of existing models, we developed 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, alongside with a high-accuracy 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 validated against published alanine-scanning experiments. This dual-model approach bridges computational modeling and wet-lab discovery, offering a robust and practical pipeline for the rational design of the targeted AMP design pipelines