A Generative Approach toward Precision Antimicrobial Peptide Design

As the emergence of bacterial resistance is outpacing the development of new antibiotics, we must find cost-effective and innovative approaches to discover new antibacterial therapeutics. Antimicrobial peptides (AMPs) represent one promising solution to fill this void, since they generally undergo faster development, display rapid onsets of killing, and most importantly, show lower risks of induced resistance. Despite prior success in AMP design with physics- and/or knowledge-based approaches, an efficient approach to precisely design peptides with high activity and selectivity is still lacking. Toward this goal, we have invented a novel approach which utilizes a generative model to predict AMP-like sequences, followed by molecular modeling to rank the candidates. Thus, we can identify peptides with desirable sequences, structures, and potential specific interactions with bacterial membranes. For the proof of concept, we curated a dataset that comprises 500,000 non-AMP peptide sequences and nearly 8,000 labeled AMP sequences to train the generative model. For 12 generated peptides that are cationic and likely helical, we assessed the membrane binding propensity via extensive all-atom molecular simulations. The top six peptides were promoted for synthesis, chemical characterizations, and antibacterial assays, showing various inhibition to bacterial growth. Three peptides were validated with broad-spectrum antibacterial activity. In aggregate, the combination of AMP generator and sophisticated molecular modeling affords enhanced speed and accuracy in AMP design. Our approach and results demonstrate the viability of a generative approach to develop novel AMPs and to help contain the rise of antibiotic resistant microbes.