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Bacterial resistance highlights the need for new therapeutic targets and strategies. DNA gyrase is a promising target for antimicrobial development. Antimicrobial peptides (AMPs) can act on intracellular targets as well as membranes. Numerous candidates have been screened from natural sources and synthetic libraries. However, most require structural optimization to improve potency and pharmacokinetic properties for clinical use. This study aimed to screen and evaluate animal-derived antimicrobial peptides as potential DNA gyrase-targeting antimicrobial candidates using computational structural analysis and molecular docking. We screened 116 antimicrobial peptides identified through data mining and selected 24 sequences from the venom of snakes, bees, spiders, and scorpions. We assessed AlphaFold 3 models with PROCHECK and Ramachandran plots before molecular docking against DNA gyrase, identifying 12 promising candidates. We will refine lower-performing peptides structurally and redock them. Physicochemical and affinity analyses revealed substantial differences among the 12 candidates. Oxyopinin-4a showed the most favorable binding profile (HADDOCK score −117.6 ± 4.6; ∆G −9.38 kcal/mol; 162 contacts) and low MICs against Gram-negative bacteria (0.5–12.0 µM). Latarcin 2a and LyeTx I also showed promise, whereas Latarcin 1 had less favorable predicted binding despite strong membrane activity. LC-AMP-F1 showed favorable binding energy (∆G −6.29 kcal/mol), while AaeAP1 and Mucroporin-M1 showed limited activity against Gram-negative strains. Cm38, Hadrurin, VmCT2, and Rondonin exhibited less favorable interaction profiles. Overall, contact density alone did not ensure biological efficacy, supporting structural refinement and redocking of lower-performing candidates. Computational screening identified promising animal-derived antimicrobial peptides with potential to interact with bacterial DNA gyrase. However, predicted binding affinity did not consistently correlate with antibacterial activity, highlighting the need for structural optimization and experimental testing.
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