The Effect of Computational Optimization Modifications of Peptide on Antimicrobial activity against E.coli ATCC 25922

Antimicrobial Resistance ( AMR) is a critical global health concern , causing millions of fatalities annually. Escherichia coli (E.coli) , a Gram negative opportunistic pathogen existing in the ESCAPE group, is considered a critical threat because resistant strains reduce the effi cacy of traditional antibiotics, enabling widespread infections diffi cult to treat. Antimicrobial peptides (AMP) , a short chain of amino acids, offer a promising alternative since they are less prone to develop resistance against and can lyse the bacterial cell more effi ciently than traditional antibiotics. This study aims to test the effect of computational sequence modifi cations on optimizing AMP activity against E.coli ATCC 25922. Computational bioinformatic tools such as CAMPr3, APD3, DBAASP, ToxinPred, AlphaFold, and AXPEP were utilized to iteratively modify the lowest performing peptide. The modifi cations increased AMP probability, improved net charge and hydrophobicity balance, reduced toxicity while maintaining its essential α-helical structure. The hypothesis was supported by the results, as modifi cations optimized peptide activity without compromising its structural integrity. This demonstrated that rational peptide design can be achieved through computational methods. Future direction in vitro studies is essential to observe its effectiveness in real world applications.