A Generative Approach toward Precision Antimicrobial Peptide Design

Antimicrobial peptides (AMPs) are peptides with promising applications for healthcare, veterinary, and agriculture industries. Despite prior success in AMP design using physics- or knowledge-based approaches, there is still a critical need to create new methodologies to design peptides with a low false positive rate and high AMP activity and selectivity. Toward this goal, we invented a cost-effective approach which utilizes a generative model to produce AMP-like sequences and molecular simulations to select peptides based on their structures and interactions. For a proof of concept, we curated a dataset that comprises 500,000 non-AMP peptide sequences and 8,000 labeled AMP sequences to train the generative model, which generated novel and diverse AMP candidates to potentially target a wide variety of microbes. Following a screening process to select peptides that are cationic and likely helical, we assessed 12 candidates by simulating their membrane-binding tendency to a lipid bilayer model. With the umbrella sampling technique, we determined the free energy change during transfer from the solution to the membrane environments for each peptide. Accordingly, we selected the six peptides with the best membrane-binding tendency, synthesized them, and characterized through spectroscopies and biological assays. Three novel peptides were validated with activity to inhibit bacterial growth. In aggregate, the combination of AMP generator and molecular simulations afford an enhanced accuracy in AMP design. Towards future precision AMP design, our methodology and results demonstrate the viability to design novel AMP-like peptides to target selected pathogens and mechanisms.