Enhancing the efficacy and selectivity of novel antimicrobial peptides against methicillin-resistant Staphylococcus aureus through computational and experimental approaches

(MRSA) poses a major global health threat and is recognized by the World Health Organization as a high-priority pathogen for new drug development. MRSA's ability to form biofilms further complicates treatment and enhances antibiotic resistance. Antimicrobial peptides (AMPs) present a promising alternative to conventional antibiotics, however, their discovery remains labor-intensive. This study utilized computational and experimental approaches to evaluate the physicochemical properties, anti-MRSA activity against 10 clinical isolates, bacterial selectivity, cytotoxicity, and antibiofilm effects of AMPs. Temporin-PF (TPF) peptide was identified as a leading candidate and modified to generate TPF-M1, achieving an improved anti-MRSA score of 600.0. TPF-M1 exhibited enhanced killing activity and selectivity against MRSA with low toxicity toward human cells. At 20 µM, TPF-M1 effectively reduced MRSA biofilm viability using the transferable solid-phase pin lid method and disrupted the biofilm structure. These findings underscore the potential of AI-guided AMP development for anti-MRSA therapy.