Evolutionary and in silico guided development of novel peptide analogues for antibacterial activity against ESKAPE...

According to WHO, to combat the resistant strains, new effective anti-microbial agents are needed on an urgent basis and global researchers should focus their efforts and discovery programs on developing them against antibiotic-resistant pathogens or priority pathogens like ESKAPE. In this context, Cationic antimicrobial peptides (AMPs) are being explored extensively as promising next-generation antimicrobials due to their broad range, fast kinetics and multifunctional role. Despite recent advances, it is still a daunting challenge to identify and design a potent AMP with no cytotoxicity, but with broad specific antimicrobial activity, stability and efficacy under in vivo conditions in a cost-effective and robust manner. In this work, as a proof of concept, we designed novel potent AMPs using artificial intelligence based in silico programs. Shortlisted peptide sequences were synthesized using the fmoc chemistry approach, assessed their antimicrobial activity, cell selectivity, mode of action and in vivo efficacy using a series of experiments. The synthesized peptide analogues demonstrated their antimicrobial activity (MIC in the range of 2.5-80 μM) against bacteria. The identified potential lead molecules showed antibacterial activity in physiological conditions with no signs of cytotoxicity. We further tested the antimicrobial activity of peptide analogues for treating wounds infected with Pseudomonas aeruginosa in the mice burn wound model. In drug-development programs, the identification of lead antimicrobial agents is always challenging and involves screening a large number of molecules which is time-consuming and expensive. This work demonstrates the utility of artificial intelligence based in silico analysis programs in discovering novel antimicrobial agents in an economical, robust way.