Mechanism-driven screening of membrane-targeting and pore-forming antimicrobial peptides

The rise of antibiotic resistance has generated an urgent demand for the discovery of new antimicrobial peptides (AMPs), prompting the development of various screening strategies. However, the specific function mechanisms of AMPs are often overlooked during the screening and optimization processes. In this study, we introduce a mechanism-driven screening approach that employs machine learning-based computational models to identify peptide sequences that target membranes and form pores. This approach explicitly considers critical factors such as structural features, membrane ainity, and the ability of peptides to oligomerize. Our method was applied to the metaproteomes of poison frogs, African clawed frogs, and human skin, followed by experimental validation. Seven peptides were successfully screened, each demonstrating antimicrobial activity with minimal hemolysis and cytotoxicity. These peptides exhibited membrane disruption capabilities in liposome leakage assays, with three showing broad-spectrum antimicrobial activity. Furthermore, single-molecule experiments indicated that these peptides can oligomerize on membranes, while electrophysiological measurements detected pore formation, confirming the effectiveness of our screening strategy. Therefore, our screening approach can effectively identify AMP sequences that act through membrane-targeting and poreforming mechanisms, offering a promising, mechanism-driven strategy for the discovery of new antimicrobial agents to combat antibiotic resistance.