A computationally optimized peptide (KI17) derived from Talisia esculenta with potent action against multidrug-resistant pathogens

Abstract

Antimicrobial peptides (AMPs) offer a promising solution against multidrug-resistant pathogens. However, their clinical application remains challenging due to limitations, including toxicity, proteolytic instability, and limited bioavailability. Here, we present KI17, a rationally designed AMP derived from GL18, a peptide fragment from Talisia esculenta. KI17 was optimized for enhanced charge, hydrophobicity, and α-helical propensity using in silico and physicochemical strategies. Molecular dynamics simulations, circular dichroism and nuclear magnetic resonance structural analyses revealed that KI17 adopts a flexible conformation in solution, but stabilizes into an amphipathic α-helix (Val2-Arg11) in membrane-mimetic environments. KI17 displayed potent, broad-spectrum antibacterial activity, with minimum inhibitory concentration (MIC) values ranging from 8 to 16 μmol L− 1, maintained efficacy under physiological salt conditions, and exhibited synergistic effects when combined with ciprofloxacin. Bacterial killing kinetics and Sytox Green uptake assays revealed rapid bacterial killing, consistent with immediate membrane permeabilization, confirming a membranolytic mechanism of action. Notably, KI17 disrupted biofilms of Staphylococcus aureus and Acinetobacter baumannii while demonstrating a half-maximal hemolytic concentration (HC50) of 247 μmol L− 1 and a half-maximal inhibitory concentration (IC50) of 120 μmol L− 1 (erythrocyte hemolysis and BV-2 cells), as well as no adverse effects in Tenebrio molitor larvae at 10 × MIC. These findings position the KI17 peptide as a structurally stable, membrane-targeting AMP with high selectivity for bacterial cells, thus overcoming common clinical limitations of such peptides, including salt tolerance and biofilm resistance. This study underscores the power of bioinformatics-guided design to accelerate the translation of AMPs into therapeutic candidates.