A Localization-Based Motif Combination Approach To the Design of Antimicrobial Peptides Targeting Gram-Positive and Gram-Negative Bacteria.

In this study, we aimed to develop a novel antimicrobial peptide (AMP) design strategy via a localization-based motif combination approach to target Gram-negative and Gram-positive pathogenic bacteria. Peptide sequences with high inhibitory activity against Escherichia coli and Staphylococcus aureus were extracted from Database of Antimicrobial Activity and Structure of Peptides (DBAASP) to form two separate datasets for Gram-negative and Gram-positive bacteria, respectively. The peptide sequences in the datasets were segmented into three fragments based on their N-terminal, C-terminal, and central positions, and their statistical significance was scored. The top-scoring fragments were assembled based on their segment-specific positions to generate candidate peptides. The several properties of the candidate peptides were predicted in silico, and the peptides exhibiting the highest antibacterial activity against Gram-positive and Gram-negative bacteria were selected for subsequent synthetic production. The peptides showed membrane deformation effects against the target bacterial strains, and their minimum inhibitory concentration (MIC) values were determined to be between 4 and 16 µg/mL for Gram-negative strains and 4-128 µg/mL for Gram-positive strains. The maximum therapeutic index of the peptides varied between 86 and 129, and their docking scores were found to be higher than known antimicrobial agents, such as nisin and polymyxin B. These results suggest that the designed peptides may serve as effective agents against pathogenic bacteria, including drug-resistant strains, and represent a promising alternative to traditional antibiotics.