Computational evolutionary approach to generate antimicrobial peptides

As a consequence of the overuse of conventional antibiotics, there is currently an unprecedented increase in antibiotic resistance in newer generations of pathogenic bacteria. This growing problem has led scientists to discover novel medications that could potentially reduce the usage of antibiotics, such as antimicrobial peptides (AMPs). Recent study has demonstrated that pardaxin could bind deeply into the surface of a lipid model membrane and inhibit the growth of pathogenic bacteria, including Staphylococcus aureus and Escherichia coli. In this work, the antibacterial efficacy of pardaxin was extended further by performing selective in silico substitution, driven by deep learning, Evolutionary-Scale Cambrian (ESMC) combined with conventional AMP design principles. Principal component analyses of the ESMC embeddings combined with conventional design models produced a set of single-and multiple-mutant analogues of pardaxin, which were predicted and validated with experimental data to exhibit selective antimicrobial properties against either E. coli or S. aureus, respectively. Atomistic molecular dynamics simulations further supported the notion that alpha-helical stability is a critical predictor of inner membrane activity, strongly correlated with their selective antimicrobial action tested in the lab. Overall, the findings highlighted a promising application of deep evolutionary machine learning techniques for screening a range of novel AMPs for selective antimicrobial agents.