Novel active Trp- and Arg-rich antimicrobial peptides with high solubility and low red blood cell toxicity designed using machine learning tools

Abstract

Given the rising number of multidrug-resistant (MDR) bacteria, there is a need to design synthetic antimicrobial peptides (AMPs) that are highly active, non-hemolytic, and highly soluble to act as alternatives to antibiotics that are either no longer effective or used as drugs of last resort. Machine learning tools allow the straightforward in silico identification of non-hemolytic antimicrobial peptides. Here, we utilized a number of these tools to rank the best peptides from two libraries: 1) 8192 peptides with sequence bhxxbhbGAL, where b is the basic amino acid R or K, h is a hydrophobic amino acid, i.e. G, A, L, F, I, V, Y, or W and x is Q, S, A, or V; and 2) 512 peptides with sequence RWhxbhRGWL, where b and h are as for the first library and x is Q, S, A, or G. The top 100 sequences from each library, as well as 10 peptides predicted to be active but hemolytic (for a total of 220 peptides), were SPOT synthesized and their IC₅₀ values were determined against S. aureus USA 300 (MRSA). Of these, 6 AMPs with low IC₅₀'s were characterized further in terms of: MICs against MRSA, E. faecalis, K. pneumoniae, E.coli and P. aeruginosa; RBC lysis; secondary structure in mammalian and bacterial model membranes; and activity against cancer cell lines HepG2, CHO, and PC-3. Overall, the approach yielded a large family of active antimicrobial peptides with high solubility and low red blood cell toxicity. It also provides a framework for future designs and improved machine learning tools.

Keywords: Arg/Trp-rich peptides; antimicrobial peptides; host defense peptides; machine learning; peptide sequence design.