Discovering Novel Antimicrobial Peptides in Generative Adversarial Network

Abstract

Due to the growing number of clinical antibiotic resistance cases in recent years, novel antimicrobial peptides (AMPs) can become ideal for next-generation antibiotics. This study trained a deep convolutional generative adversarial network (GAN) with known AMPs to generate novel AMP candidates. The quality of the GAN-designed peptides was evaluated in silico, and eight of them named GAN-pep 1[~]8 were chosen to be synthesized for further experiments. Disk diffusion testing and minimum inhibitory concentration (MIC) determination were used to determine the antibacterial effects of the synthesized GAN-designed peptides. Seven out of the eight synthesized GAN-designed peptides showed antibacterial activities. Additionally, GAN-pep 3 and GAN-pep 8 had a broad spectrum of antibacterial effects. Both of them were also effective against antibiotic-resistant bacteria strains such as methicillin-resistant Staphylococcus aureus (S. aureus) and carbapenem-resistant Pseudomonas aeruginosa (P. aeruginosa). GAN-pep 3, the most promising GAN-designed peptide candidate, had low MICs against all the tested bacteria.