Abstract
Background/Objectives: Antimicrobial peptides (AMPs) are promising therapeutic agents due to their broad-spectrum activity against bacteria, viruses, and fungi. Unlike traditional antibiotics, AMPs target microbial membranes directly and are less likely to induce resistance. They also possess immunomodulatory and wound-healing properties. However, clinical application remains limited by factors such as salt sensitivity, low bioavailability, and poor stability. To address these challenges, researchers have turned to structural optimization strategies. Recently, artificial intelligence (AI) has facilitated peptide drug design by rapidly screening large peptide libraries. Still, AI struggles to predict how subtle sequence changes affect peptide structure and function. Traditional sequence permutation offers a complementary approach by analyzing structural and functional effects without altering amino acid composition. Methods: In this study, we applied a clockwise sequence permutation strategy to the AMP W5K/A9W, generating derivative peptides with identical molecular weight, net charge, and hydrophobicity. We aimed to investigate how lysine and tryptophan distribution affects antimicrobial activity, membrane permeability, and selectivity. We assessed the secondary structures using circular dichroism (CD) spectroscopy and evaluated in vitro antimicrobial activity, salt resistance, membrane-permeabilizing ability, hemolysis, and wound healing effects. Results: The results revealed that the sequence arrangement of key residues significantly impacts peptide bioactivity and therapeutic index. Conclusions: This study highlights the importance of sequence order in determining AMP function. It also supports integrating permutation strategies with AI-based design to enhance AMP discovery. Together, these approaches offer new opportunities to combat drug-resistant pathogens and advance next-generation anti-infective therapies.