ArticleIranian biomedical journal2025
Engineering a Potent Anti-MRSA Agent: The Development and Characterization of Chimeric Endolysin ZAM-MSC.
Article in Iranian biomedical journal, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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6 authors.
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Abstract
Background: The increasing prevalence of S. aureus, especially methicillin-resistant strains, poses a major healthcare threat due to limited therapies. To address this challenge, we engineered the chimeric endolysin ZAM-MSC as a potent antibiotic alternative, using domain-fusion strategies to enhance antibacterial activity. We designed ZAM-MSC by integrating the catalytic (M23) and cell wall-binding (SH3b) domains of lysostaphin with the catalytic domain (CHAP) from endolysin SAL-1. Structural optimization was performed using AlphaFold2 prediction, AutoDock Vina docking, and GROMACS simulations to evaluate domain interactions, protein stability, and binding dynamics. Methods: The chimeric construct was cloned into pCold I, expressed in E. coli, and purified under solubility-optimized conditions. Purified ZAM-MSC, at a minimum concentration of 3 μg, reduced bacterial optical density within 15 minutes, demonstrating potent lytic activity. Thermal stability assays indicated that ZAM-MSC retained its enzymatic activity over 80-90% across 4-37 °C, with only a 10-20% decrease at 25-37 °C after 30 minutes. NaCl stability tests revealed maximal activity in the absence of NaCl, with gradual reduction in enzyme activity by increasing NaCl concentrations. Results: Cytotoxicity analysis via MTT assay on L929 fibroblast cells showed cell viabilities of ~85-90% ± 5% at the highest enzyme concentrations tested, with no detectable cytotoxic effect compared to untreated controls. Hemolysis assays confirmed nearly 100% red blood cell integrity across all tested enzyme concentrations, supporting its biocompatibility with mammalian cells. Conclusion: Our findings establish ZAM-MSC as a highly promising therapeutic candidate, combining computational precision with robust experimental validation.
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