Ab initio Designed Antimicrobial Peptides Against Gram-Negative Bacteria - Bobde, Shravani S.; Alsaab, Fahad M.; Wang, Guangshuan; Van Hoek, Monique L. (Frontiers in Microbiology, 2021)
Antimicrobial peptides (AMPs) are ubiquitous amongst living organisms and are part of the innate immune system with the ability to kill pathogens directly or indirectly by modulating the immune system. AMPs have potential as a novel therapeutic against bacteria due to their quick-acting mechanism of action that prevents bacteria from developing resistance. Additionally, there is a dire need for therapeutics with activity specifically against gram-negative bacterial infections that are intrinsically difficult to treat, with or without acquired drug resistance. Development of new antibiotics has slowed in recent years and novel therapeutics (like AMPs) with a focus against gram-negative bacteria are needed. We designed eight novel AMPs, termed PHNX peptides, using ab initio computational design (database filtering technology combined with the novel positional analysis on APD3 dataset of AMPs with activity against gram-negative bacteria) and assessed their theoretical function using published machine learning algorithms, and finally, validated their activity in our laboratory. These AMPs were tested to establish their minimum inhibitory concentration (MIC) and half-maximal bactericidal concentration (EC50) under CLSI methodology against antibiotic resistant and antibiotic susceptible Escherichia coli and MRSA (Methicillin Resistant Staphylococcus aureus) bacteria. Laboratory-based experimental results were compared to computationally predicted activities for each of the peptides to ascertain the accuracy of the computational tools used. The AMPs were then evaluated for cytotoxicity using hemolysis against human red blood cells. Several PHNX AMPs demonstrated good MIC and EC50 activity and thus present novel synthetic peptides with a potential for therapeutic use. Based on our findings, we propose upfront selection of the peptide dataset for analysis, an additional step of positional analysis to add to the ab initio database filtering technology method, and we present laboratory data on the novel, synthetically designed AMPs to validate the results of the computational approach. We aim to conduct future in vivo studies which could establish these AMPs for clinical use.