Designing a Novel Antimicrobial Peptide Against Escherichia coli ATCC 25922

Antimicrobial resistance is a growing global health issue that threatens to make existing drugs ineffective against combating microbes. This happens when microorganisms such as bacteria evolve to withstand the drugs designed to inhibit them, making even common infections a difficult challenge to treat. This problem is driven by the overuse of certain medications in pharmaceutical and agricultural settings. Antimicrobial peptides (AMPs) are emerging as a promising solution to this issue because of how they disrupt microbial membranes rather than targeting single proteins, a method that is less likely to allow microbes to gain resistance. In this study, public peptide databases were used alongside computational tools in order to design a novel AMP candidate against Escherichia coli ATCC 25922. Over one hundred sequences were pulled from the GRAMPA (Giant Repository of Antimicrobial Peptide Activities) database, all of which were reported to have activity against E. coli. While collecting these sequences, they were screened for various traits, such as their reported potency, net charge, hydrophobicity, and CAMPR3 (Collection of Anti-Microbial Peptides) scores. The data collected was then used as a means to compare each sequence’s potential. After narrowing down the list of peptides, the baseline sequence GLKALKKVFQGIHEAIKLINNHVQ was selected for further refinement. By following a specific design process, targeted substitutions were introduced to increase positive charge and adjust hydrophobicity while maintaining structural stability and minimizing predicted toxicity. The resulting sequence, GLKARKKRAQGIKHAIGLINNHVQ, demonstrated a net charge of +6.5, a hydrophobicity of 38%, a CAMPR3 score of 0.9945, and a non-toxic reading by ToxinPred3. Modeling in AlphaFold predicted a stable α-helical conformation consistent with membrane-disruptive activity. These findings suggest a viable candidate for further in vitro laboratory testing, and highlight the potential of computational design in addressing the issue of antimicrobial resistance.