Novel Antimicrobial Peptides Designed Using Recurrent Neural Network Reduce Mortality in Experimental Sepsis

The amino acid sequences of 198 novel peptides were obtained using a generative long short-term memory recurrent neural network (LTSM RNN). To assess their antimicrobial effect, I synthesized 5 out of 198 generated peptides. The PEP-38 and PEP-137 peptides were active in vitro against carbapenem-resistant isolates of Klebsiella aerogenes (n=12) and K. pneumoniae (n=18). PEP-137 was also active against Pseudomonas aeruginosa (n=17). The remaining three peptides (PEP-36, PEP-136 and PEP-174) showed no antibacterial effect. Then I investigated the effect of PEP-38 and PEP-137 (a single intraperitoneal administration of a 100 µg dose 30 min after infection) on animal survival in an experimental murine model of K. pneumoniae -induced sepsis. As a control, I used two groups of mice: one received sterile saline, and the other received inactive in vitro PEP-36 (a single 100 µg dose). The PEP-36 peptide was shown to provide the highest survival rate (66.7%). PEP-137 showed a survival rate of 50%. PEP-38 was found to be ineffective. The data obtained can be used to develop new antibacterial peptide drugs to combat antibiotic resistance.