Computational Redesign of an Antimicrobial Peptide to Combat Antibiotic-Resistant Acinetobacter baumannii

This study addresses the global crisis of antibiotic resistance by using computational tools to develop a novel antimicrobial peptide (AMP) against the bacterium Acinetobacter baumannii. From a database of 141 candidates, an AMP sequence with a low minimum inhibitory concentration of 0.025 μM was selected to redesign. Despite its high potential as an AMP, the high predicted toxicity of 0.985 was a concern. Through a computational redesign approach, specific amino acid modifications were made to reduce toxicity while improving AMP efficiency. The final optimized peptide, IRKKAWKKVSKRTCKVNLKACTFLG, displayed a significantly reduced toxicity score of 0.105 while retaining high antimicrobial activity. This suggests that in silico tools provide a strong and feasible strategy for developing safe and effective AMPs to combat antibiotic-resistant pathogens.